Teresa Scassa - Blog

The Artificial Intelligence and Data Act (AIDA) in Bill C-27 will create new obligations for those responsible for AI systems (particularly high impact systems), as well as those who process or make available anonymized data for use in AI systems. In any regulatory scheme that imposes obligations, oversight and enforcement are key issues. A long-standing critique of the Personal Information Protection and Electronic Documents Act (PIPEDA) has been that it is relatively toothless. This is addressed in the first part of Bill C-27, which reforms the data protection law to provide a suite of new enforcement powers that include order-making powers for the Privacy Commissioner and the ability to impose stiff administrative monetary penalties (AMPs). The AIDA comes with ‘teeth’ as well, although these teeth seem set within a rather fragile jaw. I will begin by identifying the oversight and enforcement powers (the teeth) and will then look at the agent of oversight and enforcement (the jaw). The table below sets out the main obligations accompanied by specific compliance measures. There is also the possibility that any breach of these obligations might be treated as either a violation or offence, although the details of these require elaboration in as-yet-to-be-drafted regulations.

 

Obligation

Oversight Power

To keep records regarding the manner in which data is anonymized and the use or management of anonymized data as well as records of assessment of whether an AI system is high risk (s. 10)

Minister may order the record-keeper to provide any of these records (s. 13(1))

 

 

Any record-keeping obligations imposed on any actor in as-yet undrafted regulations

Where there are reasonable grounds to believe that the use of a high impact system could result in harm or biased output, the Minister can order the specified person to provide these records (s. 14)

Obligation to comply with any of the requirements in ss. 6-12, or any order made under s. 13-14

Minister (on reasonable grounds to believe there has a contravention) can require the person to conduct either an internal or an external audit with respect to the possible contravention (s. 15); the audit must be provided to the Minister

 

A person who has been audited may be ordered by the Minister to implement any measure specified in the order, or to address any matter in the audit report (s. 16)

Obligation to cease using or making available a high-impact system that creates a serious risk of imminent harm

Minister may order a person responsible for a high-impact system to cease using it or making it available for use if the Minister has reasonable grounds to believe that its use gives rise to a serious risk of imminent harm (s. 17)

Transparency requirement (any person referred to in sections 6 to 12, 15 and 16)

Minister may order the person to publish on a publicly available website any information related to any of these sections of the AIDA, but there is an exception for confidential business information (s. 18)

 

Compliance with orders made by the Minister is mandatory (s. 19) and there is a procedure for them to become enforceable as orders of the Federal Court.

Although the Minister is subject to confidentiality requirements, they may disclose any information they obtain through the exercise of the above powers to certain entities if they have reasonable grounds to believe that a person carrying out a regulated activity “has contravened, or is likely to contravene, another Act of Parliament or a provincial legislature” (s. 26(1)). Those entities include the Privacy Commissioner, the Canadian Human Rights Commission, the Commissioner of Competition, the Canadian Radio-television and Telecommunications Commission, their provincial analogues, or any other person prescribed by regulation. An organization may therefore be in violation of statutes other than AIDA and may be subject to investigation and penalties under those laws.

The AIDA itself provides no mechanism for individuals to file complaints regarding any harms they may believe they have suffered, nor is there any provision for the investigation of complaints.

The AIDA sets up the Minister as the actor responsible for oversight and enforcement, but the Minister may delegate any or all of their oversight powers to the new Artificial Intelligence and Data Commissioner who is created by s. 33. The Data Commissioner is described in the AIDA as “a senior official of the department over which the Minister presides”. They are not remotely independent. Their role is “to assist the Minister” responsible for the AIDA (most likely the Minister of Industry), and they will also therefore work in the Ministry responsible for supporting the Canadian AI industry. There is essentially no real regulator under the AIDA. Instead, oversight and enforcement are provided by the same group that drafted the law and that will draft the regulations. It is not a great look, and, certainly goes against the advice of the OECD on AI governance, as Mardi Wentzel has pointed out.

The role of Data Commissioner had been first floated in the 2019 Mandate Letter to the Minister of Industry, which provided that the Minister would: “create new regulations for large digital companies to better protect people’s personal data and encourage greater competition in the digital marketplace. A newly created Data Commissioner will oversee those regulations.” The 2021 Federal Budget provided funding for the Data Commissioner, and referred to the role of this Commissioner as to “inform government and business approaches to data-driven issues to help protect people’s personal data and to encourage innovation in the digital marketplace.” In comparison with these somewhat grander ideas, the new AI and Data Commissioner role is – well – smaller than the title. It is a bit like telling your kids you’re getting them a deluxe bouncy castle for their birthday party and then on the big day tossing a couple of couch cushions on the floor instead.

To perhaps add a gloss of some ‘independent’ input into the administration of the statute, the AIDA provides for the creation of an advisory committee (s. 35) that will provide the Minister with “advice on any matters related to this Part”. However, this too is a bit of a throwaway. Neither the AIDA nor any anticipated regulations will provide for any particular composition of the advisory committee, for the appointment of a chair with a fixed term, or for any reports by the committee on its advice or activities. It is the Minister who may choose to publish advice he receives from the committee on a publicly available website (s. 35(2)).

The AIDA also provides for enforcement, which can take one of two routes. Well, one of three routes. One route is to do nothing – after all, the Minister is also responsible for supporting the AI industry in Canada– so this cannot be ruled out. A second option will be to treat a breach of any of the obligations specified in the as-yet undrafted regulations as a “violation” and impose an administrative monetary penalty (AMP). A third option is to treat a breach as an “offence” and proceed by way of prosecution (s. 30). A choice must be made between proceeding via the AMP or the offense route (s. 29(3)). Providing false information and obstruction are distinct offences (s. 30(2)). There are also separate offences in ss. 38 and 39 relating to the use of illegally obtained data and knowingly or recklessly making an AI system available for use that is likely to cause harm.

Administrative monetary penalties under Part 1 of Bill C-27 (relating to data protection) are quite steep. However, the necessary details regarding the AMPs that will be available for breach of the AIDA are to be set out in regulations that have yet to be drafted (s. 29(4)(d)). All that the AIDA really tells us about these AMPs is that their purpose is “to promote compliance with this Part and not to punish” (s. 29(2)). Note that at the bottom of the list of regulation-making powers for AMPs set out in s. 29(4). This provision allows the Minister to make regulations “respecting the persons or classes of persons who may exercise any power, or perform any duty or function, in relation to the scheme.” There is a good chance that the AMPs will (eventually) be administered by the new Personal Information and Data Tribunal, which is created in Part 2 of Bill C-27. This, at least, will provide some separation between the Minister and the imposition of financial penalties. If this is the plan, though, the draft law should say so.

It is clear that not all breaches of the obligations in the AIDA will be ones for which AMPs are available. Regulations will specify the breach of which provisions of the AIDA or its regulations will constitute a violation (s. 29(4)(a)). The regulations will also indicate whether the breach of the particular obligation is classified as minor, serious or very serious (s. 29(4)(b)). The regulations will also set out how any such proceedings will unfold. As-yet undrafted regulations will also specify the amounts or ranges of AMPS, and factors to take into account in imposing them.

This lack of important detail makes it hard not to think of the oversight and enforcement scheme in the AIDA as a rough draft sketched out on a cocktail napkin after an animated after-hours discussion of what enforcement under the AIDA should look like. Clearly, the goal is to be ‘agile’, but ‘agile’ should not be confused with slapdash. Parliament is being asked to enact a law that leaves many essential components undefined. With so much left to regulations, one wonders whether all the missing pieces can (or will) be put in place within this decade. There are instances of other federal laws left incomplete by never-drafted regulations. For example, we are still waiting for the private right of action provided for in Canada’s Anti-Spam Law, which cannot come into effect until the necessary regulations are drafted. A cynic might even say that failing to draft essential regulations is a good way to check the “enact legislation on this issue” box on the to-do list, without actually changing the status quo.

Published in Privacy

This is the third in my series of posts on the Artificial Intelligence and Data Act (AIDA) found in Bill C-27, which is part of a longer series on Bill C-27 generally. Earlier posts on the AIDA have considered its purpose and application, and regulated activities. This post looks at the harms that the AIDA is designed to address.

The proposed Artificial Intelligence and Data Act (AIDA), which is the third part of Bill C-27, sets out to regulate ‘high-impact’ AI systems. The concept of ‘harm’ is clearly important to this framework. Section 4(b) of the AIDA states that a purpose of the legislation is “to prohibit certain conduct in relation to artificial intelligence systems that may result in serious harm to individuals or harm to their interests”.

Under the AIDA, persons responsible for high-impact AI systems have an obligation to identify, assess, and mitigate risks of harm or biased output (s. 8). Those persons must also notify the Minister “as soon as feasible” if a system for which they are responsible “results or is likely to result in material harm”. There are also a number of oversight and enforcement functions that are triggered by harm or a risk of harm. For example, if the Minister has reasonable grounds to believe that a system may result in harm or biased output, he can demand the production of certain records (s. 14). If there is a serious risk of imminent harm, the Minister may order a person responsible to cease using a high impact system (s. 17). The Minister is also empowered to make public certain information about a system where he believes that there is a serious risk of imminent harm and the publication of the information is essential to preventing it (s. 28). Elevated levels of harm are also a trigger for the offence in s. 39, which involves “knowing or being reckless as to whether the use of an artificial intelligence system is likely to cause serious physical or psychological harm to an individual or substantial damage to an individual’s property”.

‘Harm’ is defined in s. 5(1) to mean:

(a) physical or psychological harm to an individual;

(b) damage to an individual’s property; or

(c) economic loss to an individual.

I have emphasized the term “individual” in this definition because it places an important limit on the scope of the AIDA. First, it is unlikely that the term ‘individual’ includes a corporation. Typically, the word ‘person’ is considered to include corporations, and the word ‘person’ is used in this sense in the AIDA. This suggests that “individual” is meant to have a different meaning. The federal Interpretation Act is silent on the issue. It is a fair interpretation of the definition of ‘harm’ that “individual” is not the same as “person”, and means an individual (human) person. The French version uses the term “individu”, and not “personne”. The harms contemplated by this legislation are therefore to individuals and not to corporations.

Defining harm in terms of individuals has other ramifications. The AIDA defines high-risk AI systems in terms of their impacts on individuals. Importantly, this excludes groups and communities. It also very significantly focuses on what are typically considered quantifiable harms, and uses language that suggests quantifiability (economic loss, damage to property, physical or psychological harm). Some important harms may be difficult to establish or to quantify. For example, class action lawsuits relating to significant data breaches have begun to wash up on the beach of lost causes due to the impossibility of proving material loss either because, although thousands may have been impacted, the individual losses are impossible to quantify, or because it is impossible to prove a causal link between very real identity theft and that particular data breach. Consider an AI system that manipulates public opinion through an algorithm that drives content to individuals based on its shock value rather than its truth. Say this happens during a pandemic and it convinces people that they should not get vaccinated or take other recommended public health measures. Say some people die because they were misled in this way. Say other people die because they were exposed to infected people who were misled in this way. How does one prove the causal link between the physical harm of injury or death of an individual and the algorithm? What if there is an algorithm that manipulates voter sentiment in a way that changes the outcome of an election? What is the quantifiable economic loss or psychological harm to any individual? How could causation be demonstrated? The harm, once again, is collective.

The EU AI Act has also been criticized for focusing on individual harm, but the wording of that law is still broader than that in the AIDA. The EU AI Act refers to high-risk systems in terms of “harm to the health and safety or a risk of adverse impact on fundamental rights of persons”. This at least introduces a more collective dimension, and it avoids the emphasis on quantifiability.

The federal government’s own Directive on Automated Decision-Making (DADM) which is meant to guide the development of AI used in public sector automated decision systems (ADS) also takes a broader approach to impact. In assessing the potential impact of an ADS, the DADM takes into account: “the rights of individuals or communities”, “the health or well-being of individuals or communities”, “the economic interests of individuals, entities, or communities”, and “the ongoing sustainability of an ecosystem”.

With its excessive focus on individuals, the AIDA is simply tone deaf to the growing global understanding of collective harm caused by the use of human-derived data in AI systems.

One response of the government might be to point out that the AIDA is also meant to apply to “biased output”. Biased output is defined in the AIDA as:

content that is generated, or a decision, recommendation or prediction that is made, by an artificial intelligence system and that adversely differentiates, directly or indirectly and without justification, in relation to an individual on one or more of the prohibited grounds of discrimination set out in section 3 of the Canadian Human Rights Act, or on a combination of such prohibited grounds. It does not include content, or a decision, recommendation or prediction, the purpose and effect of which are to prevent disadvantages that are likely to be suffered by, or to eliminate or reduce disadvantages that are suffered by, any group of individuals when those disadvantages would be based on or related to the prohibited grounds. (s. 5(1)) [my emphasis]

The argument here will be that the AIDA will also capture discriminatory biases in AI. However, I have underlined the part of this definition that once again returns the focus to individuals, rather than groups. It can be very hard for an individual to demonstrate that a particular decision discriminated against them (especially if the algorithm is obscure). In any event, biased AI will tend to replicate systemic discrimination. Although it will affect individuals, it is the collective impact that is most significant – and this should be recognized in the law. The somewhat obsessive focus on individual harm in the AIDA may unwittingly help perpetuate denials of systemic discrimination.

It is also important to note that the definition of “harm” does not include “biased output”, and while the terms are used in conjunction in some cases (for example, in s. 8’s requirement to “identify, assess and mitigate the risks of harm or biased output”), other obligations relate only to “harm”. Since the two are used conjunctively in some parts of the statute, but not others, a judge interpreting the statute might presume that when only one of the terms is used, then it is only that term that is intended. Section 17 of the AIDA allows the Minister to order a person responsible for a high-impact system to cease using it or making it available if there is a “serious risk of imminent harm”. Section 28 permits the Minister to order the publication of information related to an AI system where there are reasonable grounds to believe that the use of the system gives rise to “a serious risk of imminent harm”. In both cases, the defined term ‘harm’ is used, but not ‘biased output’.

The goals of the AIDA to protect against harmful AI are both necessary and important, but in articulating the harm that it is meant to address, the Bill underperforms.

Published in Privacy

This is the second in a series of posts on Bill C-27’s proposed Artificial Intelligence and Data Act (AIDA). The first post looked at the scope of application of the AIDA. This post considers what activities and what data will be subject to governance.

Bill C-27’s proposed Artificial Intelligence and Data Act (AIDA) governs two categories of “regulated activity” so long as they are carried out “in the course of international or interprovincial trade and commerce”. These are set out in s. 5(1):

(a) processing or making available for use any data relating to human activities for the purpose of designing, developing or using an artificial intelligence system;

(b) designing, developing or making available for use an artificial intelligence system or managing its operations.

These activities are cast in broad terms, capturing activities related both to the general curating of the data that fuel AI, and the design, development, distribution and management of AI systems. The obligations in the statute do not apply universally to all engaged in the AI industry. Instead, different obligations apply to those performing different roles. The chart below identifies the actor in the left-hand column, and the obligation the column on the right.

 

Actor

Obligation

A person who carries out any regulated activity and who processes or makes available for use anonymized data in the course of that activity

(see definition of “regulated activity” in s. 5(1)

s. 6 (data anonymization, use and management)

s. 10 (record keeping regarding measures taken under s. 6)

A person who is responsible for an artificial intelligence system (see definition of ‘person responsible’ in s. 5(2)

s. 7 (assess whether a system is high impact)

s. 10 (record keeping regarding reasons supporting their assessment of whether the system is high-impact under s. 7)

A person who is responsible for a high-impact system (see definition of ‘person responsible’ in s. 5(2; definition of “high-impact” system, s. 5(1))

s. 8 (measures to identify, assess and mitigate risk of harm or biased output)

s. 9 (measures to monitor compliance with the mitigation measures established under s. 8 and the effectiveness of the measures

s. 10 (record keeping regarding measures taken under ss. 8 and 9)

s. 12 (obligation to notify the Minister as soon as feasible if the use of the system results or is likely to result in material harm)

A person who makes available for use a high-impact system

s. 11(1) (publish a plain language description of the system and other required information)

A person who manages the operation of a high-impact system

s. 11(2) (publish a plain language description of how the system is used and other required information)

 

For most of these provisions, the details of what is actually required by the identified actor will depend upon regulations that have yet to be drafted.

A “person responsible” for an AI system is defined in s. 5(2) of the AIDA in these terms:

5(2) For the purposes of this Part, a person is responsible for an artificial intelligence system, including a high-impact system, if, in the course of international or interprovincial trade and commerce, they design, develop or make available for use the artificial intelligence system or manage its operation.

Thus, the obligations in ss. 7, 8, 9, 10 and 11, apply only to those engaged in the activities described in s. 5(1)(b) (designing, developing or making available an AI system or managing its operation). Further, it is important to note that with the exception of sections 6 and 7, the obligations in the AIDA also apply only to ‘high impact’ systems. The definition of a high-impact system has been left to regulations and is as yet unknown.

Section 6 stands out somewhat as a distinct obligation relating to the governance of data used in AI systems. It applies to a person who carries out a regulated activity and who “processes or makes available for use anonymized data in the course of that activity”. Of course, the first part of the definition of a regulated activity includes someone who processes or makes available for use “any data relating to human activities for the purpose of designing, developing or using” an AI system. So, this obligation will apply to anyone “who processes or makes available for use anonymized data” (s. 6) in the course of “processing or making available for use any data relating to human activities for the purpose of designing, developing or using an artificial intelligence system” (s. 5(1)). Basically, then for s. 6 to apply, the anonymized data must be processed for the purposes of development of an AI system. All of this must also be in the course if international or interprovincial trade and commerce.

Note that the first of these two purposes involves data “related to human activities” that are used in AI. This is interesting. The new Consumer Privacy Protection Act (CPPA) that forms the first part of Bill C-27 will regulate the collection, use and disclosure of personal data in the course of commercial activity. However, it provides, in s. 6(5), that: “For greater certainty, this Act does not apply in respect of personal information that has been anonymized.” By using the phrase “data relating to human activities” instead of “personal data”, s. 5(1) of the AIDA clearly addresses human-derived data that fall outside the definition of personal information in the CPPA because of anonymization.

Superficially, at least, s. 6 of the AIDA appears to pick up the governance slack that arises where anonymized data are excluded from the scope of the CPPA. [See my post on this here]. However, for this to happen, the data have to be used in relation to an “AI system”, as defined in the legislation. Not all anonymized data will be used in this way, and much will depend on how the definition of an AI system is interpreted. Beyond that, the AIDA only applies to a ‘regulated activity’ which is one carried out in the course of international and inter-provincial trade and commerce. It does not apply outside the trade and commerce context, nor does it apply to any excluded actors [as discussed in my previous post here]. As a result, there remain clear gaps in the governance of anonymized data. Some of those gaps might (eventually) be filled by provincial governments, and by the federal government with respect to public-sector data usage. Other gaps – e.g., with respect to anonymized data used for purposes other than AI in the private sector context – will remain. Further, governance and oversight under the proposed CPPA will be by the Privacy Commissioner of Canada, an independent agent of Parliament. Governance under the AIDA (as will be discussed in a forthcoming post) is by the Minister of Industry and his staff, who are also responsible for supporting the AI industry in Canada. Basically, the treatment of anonymized data between the CPPA and the AIDA creates a significant governance gap in terms of scope, substance and process.

On the issue of definitions, it is worth making a small side-trip into ‘personal information’. The definition of ‘personal information’ in the AIDA provides that the term “has the meaning assigned by subsections 2(1) and (3) of the Consumer Privacy Protection Act.” Section 2(1) is pretty straightforward – it defines “personal information” as “information about an identifiable individual”. However, s. 2(3) is more complicated. It provides:

2(3) For the purposes of this Act, other than sections 20 and 21, subsections 22(1) and 39(1), sections 55 and 56, subsection 63(1) and sections 71, 72, 74, 75 and 116, personal information that has been de-identified is considered to be personal information.

The default rule for ‘de-identified’ personal information is that it is still personal information. However, the CPPA distinguishes between ‘de-identified’ (pseudonymized) data and anonymized data. Nevertheless, for certain purposes under the CPPA – set out in s. 2(3) – de-identified personal information is not personal information. This excruciatingly-worded limit on the meaning of ‘personal information’ is ported into the AIDA, even though the statutory provisions referenced in s. 2(3) are neither part of AIDA nor particularly relevant to it. Since the legislator is presumed not to be daft, then this must mean that some of these circumstances are relevant to the AIDA. It is just not clear how. The term “personal information” is used most significantly in the AIDA in the s. 38 offense of possessing or making use of illegally obtained personal information. It is hard to see why it would be relevant to add the CPPA s. 2(3) limit on the meaning of ‘personal information’ to this offence. If de-identified (not anonymized) personal data (from which individuals can be re-identified) are illegally obtained and then used in AI, it is hard to see why that should not also be captured by the offence.

 

Published in Privacy

This is the first of a series of posts on the part of Bill C-27 that would enact a new Artificial Intelligence and Data Act (AIDA) in Canada. Previous posts have considered the part of the bill that would reform Canada’s private sector data protection law. This series on the AIDA begins with an overview of its purpose and application.

Bill C-27 contains the text of three proposed laws. The first is a revamped private sector data protection law. The second would establish a new Data Tribunal that is assigned a role under the data protection law. The third is a new Artificial Intelligence and Data Act (AIDA) While the two other components were present in the bill’s failed predecessor Bill C-11, the AIDA is new – and for many came as a bit of a surprise. The common thread, of course, is the government’s Digital Charter, which set out a series of commitments for building trust in the digital and data economy.

The preamble to Bill C-27, as a whole, addresses both AI and data protection concerns. Where it addresses AI regulation directly, it identifies the need to harmonize with national and international standards for the development and deployment of AI, and the importance of ensuring that AI systems uphold Canadian values in line with the principles of international human rights law. The preamble also signals a need for a more agile regulatory framework – something that might go towards justifying why so much of the substance of AI governance in the AIDA has been left to the development of regulations. Finally, the preamble speaks of a need “to foster an environment in which Canadians can seize the benefits of the digital and data-driven economy and to establish a regulatory framework that supports and protects Canadian norms and values, including the right to privacy.” This, then, frames how AI regulation (and data protection) will work in Canada – an attempt to walk a tightrope between enabling fast-paced innovation and protecting norms, values and privacy rights.

Regulating the digital economy has posed some constitutional (division of powers) challenges for the federal government, and these challenges are evident in the AIDA, particularly with respect to the scope of application of the law. Section 4 sets out the dual purposes of the legislation:

(a) to regulate international and interprovincial trade and commerce in artificial intelligence systems by establishing common requirements, applicable across Canada, for the design, development and use of those systems; and

(b) to prohibit certain conduct in relation to artificial intelligence systems that may result in serious harm to individuals or harm to their interests.

By focusing on international and interprovincial trade and commerce, the government asserts its general trade and commerce jurisdiction, without treading on the toes of the provinces, who remain responsible for intra-provincial activities. Yet, this means that there will be important gaps in AI regulation. Until the provinces act, these will be with respect to purely provincial AI solutions, whether in the public or private sectors, and, to a large extent, AI in the not-for-profit sector. However, this could get complicated since the AIDA sets out obligations for a range of actors, some of which could include international or interprovincial providers of AI systems to provincial governments.

The second purpose set out in s. 4 suggests that at least when it comes to AI systems that may result in serious harm, the federal jurisdiction over criminal law may be invoked. The AIDA creates a series of offences that could be supported by this power – yet, ultimately the offences relate to failures to meet the obligations that arise based on being engaged in a ‘regulated activity’, which takes one back to activities carried out in the course of international or interprovincial trade and commerce. The federal trade and commerce power thus remains the backbone of this bill.

Although there would be no constitutional difficulties with the federal government exerting jurisdiction over its own activities, the AIDA specifically excludes its application to federal government institutions, as defined in the Privacy Act. Significantly, it also does not apply to products, services or activities that are under the control of the Minister of National Defence, the Canadian Security Intelligence Service, the Communications Security Establishment or any other person who is responsible for a federal or provincial department or agency that is prescribed by regulation. This means that the AIDA would not apply even to those AI systems developed by the private sector for any of the listed actors. The exclusions are significant, particularly since the AIDA seems to be focussed on the prevention of harm to individuals (more on this in a forthcoming post) and the parties excluded are ones that might well develop or commission the development of AI that could (seriously) adversely impact individuals. It is possible that the government intends to introduce or rely upon other governance mechanisms to ensure that AI and personal data are not abused in these contexts. Or not. In contrast, the EU’s AI Regulation addresses the perceived need for latitude when it comes to national defence via an exception for “AI systems developed or used exclusively for military purposes” [my emphasis]. This exception is nowhere near as broad as that in the AIDA, which excludes all “products, services or activities under the control of the Minister of National defence”. Note that the Department of National Defence (DND) made headlines in 2020 when it contracted for an AI application to assist in hiring; it also made headlines in 2021 over an aborted psyops campaign in Canada. There is no reason why non-military DND uses of AI should not be subject to governance.

The government might justify excluding the federal public sector from governance under the AIDA on the basis that it is already governed by the Directive on Automated Decision-Making. This Directive applies to automated decision-making systems developed and used by the federal government, although there are numerous gaps in its application. For example, it does not apply to systems adopted before it took effect, it applies only to automated decision systems and not to other AI systems, and it currently does not apply to systems used internally (e.g., to govern public sector employees). It also does not have the enforcement measures that the AIDA has, and, since government systems could well be high-impact, this seems like a gap in governance. Consider in this respect the much-criticized ArriveCan App, designed for COVID-19 border screening and now contemplated for much broader use at border entries into Canada. The app has been criticized for its lack of transparency, and for the ‘glitch’ that sent inexplicable quarantine orders to potentially thousands of users. The ArriveCan app went through the DADM process, but clearly this is not enough to address governance issues.

Another important limit on the application of the AIDA is that most of its obligations apply only to “high impact systems”. This term is defined in the legislation as “an artificial intelligence system that meets the criteria for a high-impact system that are established in regulations.” This essentially says that this crucial term in the Bill will mean what cabinet decides it will mean at some future date. It is difficult to fully assess the significance or impact of this statute without any sense of how this term will be defined. The only obligations that appear to apply more generally are the obligation in s. 6 regarding the anonymization of data used or intended for use in AI systems, and the obligation in s. 10 to keep records regarding the anonymization measures taken.

By contrast, the EU’s AI Regulation applies to all AI systems. These fall into one of four categories: unacceptable risk, high-risk, limited risk, and low/minimal risk. Those systems that fall into the first category are banned. Those in the high-risk category are subject to the regulation’s most stringent requirements. Limited-risk AI systems need only meet certain transparency requirements and low-risk AI is essentially unregulated. Note that Canada’s approach to ‘agile’ regulation is to address only one category of AI systems – those that fall into the as-yet undefined category of high ‘impact’. It is unclear whether this is agile or supine. It is also not clear what importance should be given to the choice of the word ‘impact’ rather than ‘risk’. However, it should be noted that risk refers not just to actual but to potential harm, whereas ‘impact’ seems to suggest actual harm. Although one should not necessarily read too much into this choice of language, the fact that this important element is left to regulations means that Parliament will be asked to enact a law without understanding its full scope of application. This seems like a problem.

 

Published in Privacy

As part of my series on Bill C-27, I will be writing about both the proposed amendments to Canada’s private sector data protection law and the part of the Bill that will create a new Artificial Intelligence and Data Act (AIDA). So far, I have been writing about privacy, and my posts on consent, de-identification, data-for-good, and the right of erasure are already available. Posts on AIDA, will follow, although I still have a bit more territory on privacy to cover first. However, in the meantime, as a teaser, perhaps you might be interested in playing a bit of statutory MadLibs…...

Have you ever played MadLibs? It’s a paper-and-pencil game where someone asks the people in the room to supply a verb, noun, adverb, adjective, or body part, and the provided words are used to fill in the blanks in a story. The results are often absurd and sometimes hilarious.

The federal government’s proposal in Bill C-27 for an Artificial Intelligence and Data Act, really lends itself to a game of statutory MadLibs. This is because some of the most important parts of the bill are effectively left blank – either the Minister or the Governor-in-Council is tasked in the Bill with filling out the details in regulations. Do you want to play? Grab a pencil, and here goes:

Company X is developing an AI system that will (insert definition of ‘high impact system). It knows that this system is high impact because (insert how a company should assess impact). Company X has established measures to mitigate potential harms by (insert measures the company took to comply with the regulations) and has also recorded (insert records it kept), and published (insert information to be published).

Company X also had its system audited by an auditor who is (insert qualifications). Company X is being careful, because if it doesn’t comply with (insert a section of the Act for which non-compliance will count as a violation), it could be found to have committed a (insert degree of severity) violation. This could lead to (insert type of proceeding).

Company X, though, will be able to rely on (insert possible defence). However, if (insert possible defence) is unsuccessful, Company X may be liable to pay an Administrative Monetary Penalty if they are a (insert category of ‘person’) and if they have (insert factors to take into account). Ultimately, if they are unhappy with the outcome, they can launch a (insert a type of appeal proceeding).

Because of this regulatory scheme, Canadians can feel (insert emotion) at how their rights and interests are protected.

Published in Privacy

 

Note: The following is my response to the call for submissions on the recommendations following the third review of Canada’s Directive on Automated Decision-Making. Comments are due by June 30, 2022. If you are interested in commenting, please consult the Review Report and the Summary of Key Issues and Proposed Amendments. Comments can be sent to This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

 

The federal Directive on Automated Decision-Making (DADM) and its accompanying Algorithmic Impact Assessment tool (AIA) are designed to provide governance for the adoption and deployment of automated decision systems (ADS) by Canada’s federal government. Governments are increasingly looking to ADS in order to speed up routine decision-making processes and to achieve greater consistency in decision-making. At the same time, there are reasons to be cautious. Automated decision systems carry risks of incorporating and replicating discriminatory bias. They may also lack the transparency required of government decision-making, particularly where important rights or interests are at stake. The DADM, which has been in effect since April 2019 (with compliance mandatory no later than April 2020), sets out a series of obligations related to the design and deployment of automated decision-making systems. The extent of the obligations depends upon a risk assessment, and the AIA is the tool by which the level of risk of the system is assessed.

Given that this is a rapidly evolving area, the DADM provides that it will be reviewed every six months. It is now in its third review. The first two reviews led to the clarification of certain obligations in the DADM and to the development of guidelines to aid in its interpretation. This third review proposes a number of more substantive changes. This note comments on some of these changes and proposes an issue for future consideration.

Clarify and Broaden the Scope

A key recommendation in this third round of review relates to the scope of the DADM. Currently, the DADM applies only to ‘external’ services of government – in other words services offered to individuals or organizations by government. It does not apply internally. This is a significant gap when one considers the expanding use of ADS in the employment context. AI-enabled decision systems have been used in hiring processes, and they can be used to conduct performance reviews, and to make or assist in decision-making about promotions and internal workforce mobility. The use of AI tools in the employment context can have significant impacts on the lives and careers of employees. It seems a glaring oversight to not include such systems in the governance regime for ADM. The review team has recommended expanding the scope of the DADM to include internal as well as external services. They note that this move would also extend the DADM to any ADS used for “grants and contributions, awards and recognition, and security screening” (Report at 11). This is an important recommendation and one which should be implemented.

The review team also recommends a clarification of the language regarding the application of the DADM. Currently it puts within its scope “any system, tool, or statistical models used to recommend or make an administrative decision about a client”. Noting that “recommend” could be construed as including only those systems that recommend a specific outcome, as opposed to systems that process information on behalf of a decision-maker, the team proposes replacing “recommend” with “support”. This too is an important recommendation which should be implemented.

Periodic Reviews

Currently the DADM provides for its review every six months. This was always an ambitious review schedule. No doubt it was motivated by the fact that the DADM was a novel tool designed to address a rapidly emerging and evolving technology with potentially significant implications. The idea was to ensure that it was working properly and to promptly address any issues or problems. In this third review, however, the team recommends changing the review period from six months to two years. The rationale is that the six-month timetable makes it challenging for the team overseeing the DADM (which is constantly in a review cycle), and makes it difficult to properly engage stakeholders. They also cite the need for the DADM to “display a degree of stability and reliability, enabling federal institutions and the clients they serve to plan and act with a reasonable degree of confidence.” (Report at 12).

This too is a reasonable recommendation. While more frequent reviews were important in the early days of the DADM and the AIA, reviews every six months seem unduly burdensome once initial hiccups are resolved. A six-month review cycle engages the team responsible for the DADM in a constant cycle of review, which may not be the best use of resources. The proposed two-year review cycle would allow for a more experience to be garnered with the DADM and AIA, enabling a more substantive assessment of issues arising. Further, a two-year window is much more realistic if stakeholders are to be engaged in a meaningful way. Being asked to comment on reports and proposed changes every six months seems burdensome for anyone – including an already stretched civil society sector. The review document suggests that Canada’s Chief Information Officer could request completion of an off-cycle review if the need arose, leaving room for the possibility that a more urgent issue could be addressed outside of the two-year review cycle.

Data Model and Governance

The third review also proposes amendments to provide for what it describes as a more ‘holistic’ approach to data governance. Currently, the DADM focuses on data inputs – in other words on assessing the quality, relevance and timeliness of the data used in the model. The review report recommends the addition of an obligation to establish “measures to ensure that data used and generated by the Automated Decision System are traceable, protected, and appropriately retained and disposed of in accordance with the Directive on Service and Digital, Directive on Privacy Practices, and Directive on Security Management”. It will also recommend amendments to extend testing and assessment beyond data to underlying models, in order to assess both data and algorithms for bias or other problems. These are positive amendments which should be implemented.

Explanation

The review report notes that while the DADM requires “meaningful explanations” of how automated decisions were reached, and while guidelines provide some detail as to what is meant by explainability, there is still uncertainty about what explainability entails. The Report recommends adding language in Appendix C, in relation to impact assessment, that will set out the information necessary for ‘explainability’. This includes:

  • The role of the system in the decision-making process;
  • The training and client data, their source and method of collection, if applicable;
  • The criteria used to evaluate client data and the operations applied to process it; and
  • The output produced by the system and any relevant information needed to interpret it in the context of the administrative decision.

Again, this recommendation should be implemented.

Reasons for Automation

The review would also require those developing ADM systems for government to specifically identify why it was considered necessary or appropriate to automate the existing decision-making process. The Report refers to a “clear and demonstrable need”. This is an important additional criterion as it requires transparency as to the reasons for automation – and that these reasons go beyond the fact that vendor-demonstrated technologies look really cool. As the authors of the review note, requiring justification also helps to assess the parameters of the system adopted – particularly if the necessity and proportionality approach favoured by the Office of the Privacy Commissioner of Canada is adopted.

Transparency

The report addresses several issues that are relevant to the transparency dimensions of the DADM and the accompanying AIA. Transparency is an important element of the DADM, and it is key both to the legitimacy of the adoption of ADS by government, but also to its ongoing use. Without transparency in government decision-making that impacts individuals, organizations and communities, there can be no legitimacy. There are a number of transparency elements that are built into the DADM. For example, there are requirements to provide notice of automated decision systems, a right to an explanation of decisions that is tailored to the impact of the decision, and a requirement not just to conduct an AIA, but to publish the results. The review report includes a number of recommendations to improve transparency. These include a recommendation to clarify when an AIA must be completed and released, greater transparency around peer review results, more explicit criteria for explainability, and adding additional questions to the AIA. These are all welcome recommendations.

At least one of these recommendations may go some way to allaying my concerns with the system as it currently stands. The documents accompanying the report (slide 3 of summary document) indicate that there are over 300 AI projects across 80% of federal institutions. However, at the time of writing, only four AIAs were published on the open government portal. There is clearly a substantial lag between development of these systems and release of the AIAs. The recommendation that an AIA be not just completed but also released prior to the production of the system is therefore of great importance to ensuring transparency.

It may be that some of the discrepancy in the numbers is attributable to the fact that the DADM came into effect in 2020, and it was not grandfathered in for projects already underway. For transparency’s sake, I would also recommend that a public register of ADS be created that contains basic information about all government ADS. This could include their existence and function, as well as some transparency regarding explainability, the reasons for adoption, and measures taken to review, assess and ensure the reliability of these systems. Although it is too late, in the case of these systems, to perform a proactive AIA, there should be some form of reporting tool that can be used to provide important information, for transparency purposes, to the public.

Consideration for the Future

The next review of the DADM and the AIA should also involve a qualitative assessment of the AIAs that have been published to date. If the AIA is to be a primary tool not just for assessing ADS but for providing transparency about them, then they need to be good. Currently there is a requirement to conduct an AIA for a system within the scope of the DADM – but there is no explicit requirement for it to be of a certain quality. A quick review of the four AIAs currently available online shows some discrepancy between them in terms of the quality of the assessment. For example, the project description for one such system is an unhelpful 9-word sentence that does not make clear how AI is actually part of the project. This is in contrast to another that describes the project in a 14-line paragraph. These are clearly highly divergent in terms of the level of clarity and detail provided.

The first of these two AIAs also seems to contain contradictory answers to the AIA questionnaire. For example, the answer to the question “Will the system only be used to assist a decision-maker” is ‘yes’. Yet the answer to the question “Will the system be replacing a decision that would otherwise be made by a human” is also ‘yes’. Either one of these answers is incorrect, or the answers do not capture how the respondent interpreted these questions. These are just a few examples. It is easy to see how use of the AIA tool can range from engaged to pro forma.

The obligations imposed on departments with respect to ADS vary depending upon the risk assessment score. This score is evaluated through the questionnaire, and one of the questions asks “Are clients in this line of business particularly vulnerable?” In the AIA for an access to information (ATIP) tool, the answer given to this question is “no”. Of course, the description of the tool is so brief that it is hard to get a sense of how it functions. However, I would think that the clientele for an ATIP portal would be quite diverse. Some users will be relatively sophisticated (e.g., journalists or corporate users). Others will be inexperienced. For some of these, information sought may be highly important to them as they may be seeking access to government information to right a perceived wrong, to find out more about a situation that adversely impacts them, and so on. In my view, this assessment of the vulnerability of the clients is not necessarily accurate. Yet the answer provided contributes to a lower overall score and thus a lower level of accountability. My recommendation for the next round of reviews is to assess the overall effectiveness of the AIA tool in terms of the information and answers provided and in terms of their overall accuracy.

I note that the review report recommends adding questions to the AIA in order to improve the tool. Quite a number of these are free text answers, which require responses to be drafted by the party completing the AIA. Proposed questions include ones relating to the user needs to be addressed, how the system will meet those needs, and the effectiveness of the system in meeting those needs, along with reasons for this assessment. Proposed questions will also ask whether non-AI-enabled solutions were also considered, and if so, why AI was chosen as the preferred method. A further question asks what the consequences would be of not deploying the system. This additional information is important both to assessing the tool and to providing transparency. However, as noted above, the answers will need to be clear and sufficiently detailed in order to be of any use.

The AIA is crucial to assessing the level of obligation and to ensuring transparency. If AIAs are pro forma or excessively laconic, then the DADM can be as finely tuned as can be, but it will still not achieve desired results. The review committee’s recommendation that plain language summaries of peer review assessments also be published will provide a means of assessing the quality of the AIAs, and thus it is an important recommendation to strengthen both transparency and compliance.

A final issue that I would like to address is that, to achieve transparency, people will need to be able to easily find and access the information about the systems. Currently, AIAs are published on the Open Government website. There, they are listed alphabetically by title. This is not a huge problem right now, since there are only four of them. As more are published, it would be helpful to have a means of organizing them by department or agency, or by other criteria (including risk/impact score) to improve their findability and usability. Further, it will be important that any peer review summaries are linked to the appropriate AIAs. In addition to publication on the open government portal, links to these documents should be made available from department, agency or program websites. It would also be important to have an index or registry of AI in the federal sector – including not just those projects covered by the DADM, but also those in production prior to the DADM’s coming into force.

[Note: I have written about the DADM and the AIA from an administrative law perspective. My paper, which looks at the extent to which the DADM addresses administrative law concerns regarding procedural fairness, can be found here.]

Published in Privacy

 

Ontario has just released its Beta principles for the ethical use of AI and data enhanced technologies in Ontario. These replace the earlier Alpha principles, and are revised based upon commentary and feedback on the Alpha version. Note that these principles are designed for use in relation to AI technologies adopted for the Ontario public sector.

Below you will find a comparison table I created to provide a quick glance at what has been changed since the previous version. I have flagged significant additions with italics in the column for the Beta version. I have also flagged some words or concepts that have disappeared in the Beta version by using strikethrough in the column with the Alpha version. I have focused on the principles, and have not flagged changes to the “Why it Matters” section of each principle.

One important change to note is that the Beta version now refers not just to technologies used to make decisions, but also technologies used to assist in decision-making.

 

 

Principles for Ethical Use [Alpha]

Principles for Ethical Use [Beta]

The alpha Principles for Ethical Use set out six points to align the use of data-driven technologies within government processes, programs and services with ethical considerations and values. Our team has undertaken extensive jurisdictional scans of ethical principles across the world, in particular the US the European Union and major research consortiums. The Ontario “alpha” principles complement the Canadian federal principles by addressing a gap concerning specificity. Ontario’s principles support our diverse economic ecosystem by not clashing with existing best practices, principles and frameworks. This approach references and harmonizes with known standards, principles and tools to create clarity rather than barriers for innovation that is safe, responsible and beneficial.

 

These Principles for Ethical Use set out six points to align the use of data enhanced technologies within government processes, programs and services with ethical considerations and values.

 

The Trustworthy AI team within Ontario’s Digital Service has undertaken extensive jurisdictional scans of ethical principles across the world, in particular New Zealand, the United States, the European Union and major research consortiums.

 

The Ontario “beta” principles complement the Canadian federal principles by addressing a gap concerning specificity. Ontario’s principles support our diverse economic ecosystem by not clashing with existing best practices, principles and frameworks. This approach references and harmonizes with known standards, principles and tools to create clarity rather than barriers for innovation that is safe, responsible and beneficial.

 

We’re in the early days of bringing these principles to life. We encourage you to adopt as much of the principles as possible, and to share your feedback with us. You can email This e-mail address is being protected from spambots. You need JavaScript enabled to view it for more details.

 

You can also check out the Transparency Guidelines (GitHub).

1. Transparent and Explainable

 

There must be transparent and responsible disclosure around data-driven technology like Artificial Intelligence (AI), automated decisions and machine learning (ML) systems to ensure that people understand outcomes and can discuss, challenge and improve them.

 

 

Where automated decision making has been used to make individualized and automated decisions about humans, meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject should be available.

 

Why it Matters

 

There is no way to hold data-driven technologies accountable, particularly as they impact various historically disadvantaged groups if the public is unaware of the algorithms and automated decisions the government is making. Transparency of use must be accompanied with plain language explanations for the public to have access to and not just the technical or research community. For more on this, please consult the Transparency Guidelines.

 

1. Transparent and explainable

 

There must be transparent use and responsible disclosure around data enhanced technology like AI, automated decisions and machine learning systems to ensure that people understand outcomes and can discuss, challenge and improve them. This includes being open about how and why these technologies are being used.

 

When automation has been used to make or assist with decisions, a meaningful explanation should be made available. The explanation should be meaningful to the person requesting it. It should include relevant information about what the decision was, how the decision was made, and the consequences.

 

Why it matters

 

Transparent use is the key principle that helps enable other principles while building trust and confidence in government use of data enhanced technologies. It also encourages a dialogue between those using the technology and those who are affected by it.

 

Meaningful explanations are important because they help people understand and potentially challenge outcomes. This helps ensure decisions are rendered fairly. It also helps identify and reverse adverse impacts on historically disadvantaged groups.

 

For more on this, please consult the Transparency Guidelines.

 

2. Good and Fair

 

Data-driven technologies should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards to ensure a fair and just society.

 

Designers, policy makers and developers should respect the rule of law, human rights and democratic values, throughout the AI system lifecycle. These include freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality, diversity, fairness, social justice, and internationally recognized labor rights.

 

Why it matters

 

Algorithmic and machine learning systems evolve through their lifecycle and as such it is important for the systems in place and technologies to be good and fair at the onset, in their data inputs and throughout the lifecycle of use. The definitions of good and fair are intentionally vague to allow designers and developers to consider all of the users both directly and indirectly impacted by the deployment of an automated decision making system.

 

2. Good and fair

 

Data enhanced technologies should be designed and operated in a way throughout their life cycle that respects the rule of law, human rights, civil liberties, and democratic values. These include dignity, autonomy, privacy, data protection, non-discrimination, equality, and fairness.

 

Why it matters

 

Algorithmic and machine learning systems evolve through their lifecycle and as such it is important for the systems in place and technologies to be good and fair at the onset, in their data inputs and throughout the life cycle of use. The definitions of good and fair are intentionally broad to allow designers and developers to consider all of the users both directly and indirectly impacted by the deployment of an automated decision making system.

 

3. Safe

 

Data-driven technologies like AI and ML systems must function in a robust, secure and safe way throughout their life cycles and potential risks should be continually assessed and managed.

 

Designers and developers should implement mechanisms and safeguards, such as capacity for human determination and complete halt of the system operations, that are appropriate to the context and predetermined at initial deployment.

 


Why it matters

Creating safe data-driven technologies means embedding safeguards throughout the life cycle of the deployment of the algorithmic system. Automated algorithmic decisions can reflect and amplify undesirable patterns in the data they are trained on. Despite our best efforts there will be unexpected outcomes and impacts. Systems will require ongoing monitoring and mitigation planning to ensure that if the algorithmic system is making decisions that are no longer agreeable that a human can adapt, correct or improve the system.

3. Safe

 

Data enhanced technologies like AI and ML systems must function in a safe and secure way throughout their life cycles and potential risks should be continually assessed and managed.

 

Designers, policy makers and developers should embed appropriate safeguards throughout the life cycle of the system to ensure it is working as intended. This would include mechanisms related to system testing, piloting, scaling and human intervention as well as alternative processes in case a complete halt of system operations is required. The mechanisms must be appropriate to the context and determined before deployment but should be iterated upon throughout the system’s life cycle.

 

Why it matters

Automated algorithmic decisions can reflect and amplify undesirable patterns in the data they are trained on. As well, issues with the system can arise that only become apparent after the system is deployed.

 

Therefore, despite our best efforts unexpected outcomes and impacts need to be considered. Accordingly, systems will require ongoing monitoring and mitigation planning to ensure that if the algorithmic system is making decisions that are not intended, a human can adapt, correct or improve the system.

 

4. Accountable and Responsible

 

Organizations and individuals developing, deploying or operating AI systems should be held accountable for their ongoing proper functioning in line with the above principles. Algorithmic systems should be periodically peer-reviewed or audited to ensure that unwanted biases have not inadvertently crept in over time.

 

Where AI is used to make decisions about individuals there needs to be a process for redress to better understand how a given decision was made.

 

Why it matters

 

In order for there to be accountability for decisions that are made by an AI or ML system a person, group of people or organization needs to be identified prior to deployment. This ensures that if redress is needed there is a preidentified entity that is responsible and can be held accountable for the outcomes of the algorithmic systems.

 

4. Accountable and responsible

 

Organizations and individuals developing, deploying or operating AI systems should be held accountable for their ongoing proper functioning in line with the other principles. Human accountability and decision making over AI systems within an organization needs to be clearly identified, appropriately distributed and actively maintained throughout the system’s life cycle. An organizational culture around shared ethical responsibilities over the system must also be promoted.

 

Where AI is used to make or assist with decisions, a public and accessible process for redress should be designed, developed, and implemented with input from a multidisciplinary team and affected stakeholders. Algorithmic systems should also be regularly peer-reviewed or audited to ensure that unwanted biases have not inadvertently crept in over time.

 

Why it matters

 

Identifying and appropriately distributing accountability within an organization helps ensure continuous human oversight over the system is properly maintained. In addition to clear roles related to accountability, it is also important to promote an organizational culture around shared ethical responsibilities. This helps prevent gaps and avoids the situation where ethical considerations are always viewed as someone else’s responsibility.

 

While our existing legal framework includes numerous traditional processes of redress related to governmental decision making, AI systems can present unique challenges to those traditional processes with their complexity. Input from a multidisciplinary team and affected stakeholders will help identify those issues in advance and design appropriate mechanisms to mitigate them.

 

Regular peer review of AI systems is also important. Issues around bias may not be evident when AI systems are initially designed or developed, so it's important to consider this requirement throughout the lifecycle of the system.

 

5. Human Centric

 

The processes and outcomes behind an algorithm should always be developed with human users as the main consideration. Human centered AI should reflect the information, goals, and constraints that a human decision-maker weighs when arriving at a decision.

 

Keeping human users at the center entails evaluating any outcomes (both direct and indirect) that might affect them due to the use of the algorithm. Contingencies for unintended outcomes need to be in place as well, including removing the algorithms entirely or ending their application.

 

Why it matters

 

Placing the focus on human user ensures that the outcomes do not cause adverse effects to users in the process of creating additional efficiencies.

 

In addition, Human-centered design is needed to ensure that you are able to keep a human in the loop when ensuring the safe operation of an algorithmic system. Developing algorithmic systems with the user in mind ensures better societal and economic outcomes from the data-driven technologies.

 

5. Human centric

 

AI systems should be designed with a clearly articulated public benefit that considers those who interact with the system and those who are affected by it. These groups should be meaningfully engaged throughout the system’s life cycle, to inform development and enhance operations. An approach to problem solving that embraces human centered design is strongly encouraged.

 

Why it matters

 

Clearly articulating a public benefit is an important step that enables meaningful dialogue early with affected groups and allows for measurement of success later.

 

Placing the focus on those who interact with the system and those who are affected by it ensures that the outcomes do not cause adverse effects in the process of creating additional efficiencies.

 

Developing algorithmic systems that incorporate human centred design will ensure better societal and economic outcomes from the data enhanced technologies.

 

6. Sensible and Appropriate

 

Data-driven technologies like AI or ML shall be developed with consideration of how it may apply to specific sectors or to individual cases and should align with the Canadian Charter of Human Rights and Freedoms and with Federal and Provincial AI Ethical Use.

 

Other biproducts of deploying data-driven technologies such as environmental, sustainability, societal impacts should be considered as they apply to specific sectors and use cases and applicable frameworks, best practices or laws.

 

Why it matters

 

Algorithmic systems and machine learning applications will differ by sector and user. As a result, while the above principles are a good starting point for developing ethical data-driven technologies it is important that additional considerations be given to the specific sectors and environments to which the algorithm is applied.

 

Experts in both technology and ethics should be consulted in development of data-driven technologies such as AI to guard against any adverse effects (including societal, environmental and other long-term effects).

6. Sensible and appropriate

 

Every data enhanced system exists not only within its use case, but also within a particular sector of society and a broader context that can feel its impact. Data enhanced technologies should be designed with consideration of how they may apply to a particular sector along with awareness of the broader context. This context could include relevant social or discriminatory impacts.

 

Why it matters

 

Algorithmic systems and machine learning applications will differ by sector. As a result, while the above principles are a good starting point for developing ethical data enhanced technologies it is important that additional considerations be given to the specific sectors to which the algorithm is applied.

 

Encouraging sector specific guidance also helps promote a culture of shared ethical responsibilities and a dialogue around the important issues raised by data enhanced systems.

 

Published in Privacy
Thursday, 07 February 2019 08:09

Ontario Launches Data Strategy Consultation

On February 5, 2019 the Ontario Government launched a Data Strategy Consultation. This comes after a year of public debate and discussion about data governance issues raised by the proposed Quayside smart cities development in Toronto. It also comes at a time when the data-thirsty artificial intelligence industry in Canada is booming – and hoping very much to be able to continue to compete at the international level. Add to the mix the view that greater data sharing between government departments and agencies could make government ‘smarter’, more efficient, and more user-friendly. The context might be summed up in these terms: the public is increasingly concerned about the massive and widespread collection of data by governments and the private sector; at the same time, both governments and the private sector want easier access to more and better data.

Consultation is a good thing – particularly with as much at stake as there is here. This consultation began with a press release that links to a short text about the data strategy, and then a link to a survey which allows the public to provide feedback in the form of answers to specific questions. The survey is open until March 7, 2019. It seems that the government will then create a “Minister’s Task Force on Data” and that this body will be charged with developing a draft data strategy that will be opened for further consultation. The overall timeline seems remarkably short, with the process targeted to wrap up by Fall 2019.

The press release telegraphs the government’s views on what the outcome of this process must address. It notes that 55% of Canada’s Big data vendors are located in Ontario, and that government plans “to make life easier for Ontarians by delivering simpler, faster and better digital services.” The goal is clearly to develop a data strategy that harnesses the power of data for use in both the private and public sectors.

If the Quayside project has taught anyone anything, it is that people do care about their data in the hands of both public and private sector actors. The press release acknowledges this by referencing the need for “ensuring that data privacy and protection is paramount, and that data will be kept safe and secure.” Yet perhaps the Ontario government has not been listening to all of the discussions around Quayside. While the press release and the introduction to the survey talk about privacy and security, neither document addresses the broader concerns that have been raised in the context of Quayside, nor those that are raised in relation to artificial intelligence more generally. There are concerns about bias and discrimination, transparency in algorithmic decision-making, profiling, targeting, and behavioural modification. Seamless sharing of data within government also raises concerns about mass surveillance. There is also a need to consider innovative solutions to data governance and the role the government might play in fostering or supporting these.

There is no doubt that the issues underlying this consultation are important ones. It is clear that the government intends to take steps to facilitate intra-governmental sharing of data as well as greater sharing of data between government and the private sector. It is also clear that much of that data will ultimately be about Ontarians. How this will happen, and what rights and values must be protected, are fundamental questions.

As is the case at the provincial and federal level across the country, the laws which govern data in Ontario were written for a different era. Not only are access to information and protection of privacy laws out of date, data-driven practices increasingly impact areas such as consumer protection, competition, credit reporting, and human rights. An effective data strategy might need to reach out across these different areas of law and policy.

Privacy and security – the issues singled out in the government’s documents – are important, but privacy must mean more than the narrow view of protecting identifiable individuals from identity theft. We need robust safeguards against undue surveillance, assurances that our data will not be used to profile or target us or our communities in ways that create or reinforce exclusion or disadvantage; we need to know how privacy and autonomy will be weighed in the balance against the stimulation of the economy and the encouragement of innovation. We also need to consider whether there are uses to which our data should simply not be put. Should some data be required to be stored in Canada, and if so in what circumstances? These and a host of other questions need to be part of the data strategy consultation. Perhaps a broader question might be why we are talking only about a data strategy and not a digital strategy. The approach of the government seems to focus on the narrow question of data as both an input and output – but not on the host of other questions around the digital technologies fueled by data. Such questions might include how governments should go about procuring digital technologies, the place of open source in government, the role and implication of technology standards – to name just a few.

With all of these important issues at stake, it is hard not to be disappointed by the form and substance of at least this initial phase of the government's consultation. It is difficult to say what value will be derived from the survey which is the vehicle for initial input. Some of the questions are frankly vapid. Consider question 2:

2. I’m interested in exploring the role of data in:

creating economic benefits

increasing public trust and confidence

better, smarter government

other

There is no box in which to write in what the “other” might be. And questions 9 to 11 provide sterling examples of leading questions:

9. Currently, the provincial government is unable to share information among ministries requiring individuals and businesses to submit the same information each time they interact with different parts of government. Do you agree that the government should be able to securely share data among ministries?

Yes

No

I’m not sure

10. Do you believe that allowing government to securely share data among ministries will streamline and improve interactions between citizens and government?

Yes

No

I’m not sure

11. If government made more of its own data available to businesses, this data could help those firms launch new services, products, and jobs for the people of Ontario. For example, government transport data could be used by startups and larger companies to help people find quicker routes home from work. Would you be in favour of the government responsibly sharing more of its own data with businesses, to help them create new jobs, products and services for Ontarians?

Yes

No

I’m not sure

In fairness, there are a few places in the survey where respondents can enter their own answers, including questions about what issues should be put to the task force and what skills and experience members should have. Those interested in data strategy should be sure to provide their input – both now and in the later phases to come.

Published in Privacy

A law suit filed in Montreal this summer raises novel copyright arguments regarding AI-generated works. The plaintiffs are artist Amel Chamandy and Galerie NuEdge Fine Arts (which sells and exhibits her art). They are suing artist Adam Basanta for copyright and trademark infringement. (The trademark infringement arguments are not discussed in this post). Mr Basanta is a world renowned new media artist who experiments with AI in his work. (See the Globe and Mail story by Chris Hannay on this law suit here).

According to a letter dated July 4, filed with the court, Mr. Basanta’s current project is “to explore connections between mass technologies, using those technologies themselves.” He explains his process in a video which can be found here. Essentially, he has created what he describes as an “art-factory” that randomly generates images without human input. The images created are then “analyzed by a series of deep-learning algorithms trained on a database of contemporary artworks in economic and institutional circulation” (see artist’s website). The images used in the database of artworks are found online. Where the analysis finds a match of more than 83% between one of the randomly generated images and an image in the database, the randomly generated image is presented online with the percentage match, the title of the painting it matches, and the artist’s name. This information is also tweeted out. The image of the painting that matches the AI image is not reproduced or displayed on the website or on Twitter.

One of Mr Basanta’s images was an 85.81% match with a painting by Ms Chamandy titled “Your World Without Paper”. This information was reported on Mr Basanta’s website and Twitter accounts along with the machine-generated image which resulted in the match.

The copyright infringement allegation is essentially that “the process used by the Defendant to compare his computer generated images to Amel Chamandy’s work necessarily required an unauthorized copy of such a work to be made.” (Statement of Claim, para 30). Ms Chamandy claims statutory damages of up to $20,000 for the commercial use of her work. Mr Basanta, for his part, argues that there is no display of Ms Chamandy’s work, and therefore no infringement.

AI has been generating much attention in the copyright world. AI algorithms need to be ‘trained’ and this training requires that they be fed a constant supply of text, data or images, depending upon the algorithm. Rights holders argue that the use of their works in this way without consent is infringement. The argument is that the process requires unauthorized copies to be fed into the system for algorithmic analysis. Debates have raged in the EU over a text-and-data mining exception to copyright infringement which would make this type of use of copyright protected works acceptable so long as it is for research purposes. Other uses would require clearance for a fee. There has already been considerable debate in Europe over whether research is a broad enough basis for the exception and what activities it would include. If a similar exception is to be adopted in Canada in the next round of copyright reform, we will face similar challenges in defining its boundaries.

Of course, the Chamandy case is not the conventional text and data mining situation. The copied image is not used to train algorithms. Rather, it is used in an analysis to assess similarities with another image. But such uses are not unknown in the AI world. Facial recognition technologies match live captured images with stored face prints. In this case, the third party artwork images are like the stored face prints. It is AI, just not the usual text and data mining paradigm. This should also raise questions about how to draft exceptions or to interpret existing exceptions to address AI-related creativity and innovation.

In the US, some argue that the ‘fair use’ exception to infringement is broad enough to support text and data mining uses of copyright protected works since the resulting AI output is transformative. Canada’s fair dealing provisions are less generous than U.S. fair use, but it is still possible to argue that text and data mining uses might be ‘fair’. Canadian law recognizes fair dealing for the purposes of research or private study, so if an activity qualifies as ‘research’ it might be fair dealing. The fairness of any dealing requires a contextual analysis. In this case the dealing might be considered fair since the end result only reports on similarities but does not reproduce any of the protected images for public view.

The problem, of course, with fair dealing defences is that each case turns on its own facts. The fact-dependent inquiry necessary for a fair dealing defense could be a major brake on innovation and creativity – either by dissuading uses out of fear of costly infringement claims or by driving up the cost of innovation by requiring rights clearance in order to avoid being sued.

The claim of statutory damages here is also interesting. Statutory damages were introduced in s. 38.1 of the Copyright Act to give plaintiffs an alternative to proving actual damage. For commercial infringements, statutory damages can range from $500 to $20,000 per work infringed; for non-commercial infringement the range is $100 to $5,000 for all infringements and all works involved. A judge’s actual award of damages within these ranges is guided by factors that include the need for deterrence, and the conduct of the parties. Ms Chamandy asserts that Mr Basanda’s infringement is commercial, even though the commercial dimension is difficult to see. It would be interesting to consider whether the enhancement of his reputation or profile as an artist or any increase in his ability to obtain grants would be considered “commercial”. Beyond the challenge of identifying what is commercial activity in this context, it opens a window into the potential impact of statutory damages in text and data mining activities. If such activities are considered to infringe copyright and are not clearly within an exception, then in Canada, a commercial text and data miner who consumes – say 500,000 different images to train an algorithm – might find themselves, even on the low end of the spectrum, liable for $250 million dollars in statutory damages. Admittedly, the Act contains a clause that gives a judge the discretion to reduce an award of statutory damages if it is “grossly out of proportion to the infringement”. However, not knowing what a court might do or by how much the damages might be reduced creates uncertainty that can place a chill on innovation.

Although in this case, there may well be a good fair dealing defence, the realities of AI would seem to require either a clear set of exceptions to clarify infringement issues, or some other scheme to compensate creators which expressly excludes resort to statutory damages. The vast number of works that might be consumed to train an algorithm for commercial purposes makes statutory damages, even at the low end of the scale, potentially devastating and creates a chill.

 

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