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Displaying items by tag: data governance
Monday, 11 December 2023 06:58
Data Governance for AI under Canada's Proposed AI and Data Act (AIDA Amendments Part IV)The federal government’s proposed Artificial Intelligence and Data Act (AIDA) (Part III of Bill C-27) - contained some data governance requirements for anonymized data used in AI in its original version. These were meant to dovetail with changes to PIPEDA reflected in the Consumer Privacy Protection Act (CPPA) (Part I of Bill C-27). The CPPA provides in s. 6(5) that “this Act does not apply in respect of personal information that has been anonymized.” Although no such provision is found in PIPEDA, this is, to all practical effects, the state of the law under PIPEDA. PIPEDA applies to “personal information”, which is defined as “information about an identifiable individual”. If someone is not identifiable, then it is not personal information, and the law does not apply. This was the conclusion reached, for example, in the 2020 Cadillac Fairview joint finding of the federal Privacy Commissioner and his counterparts from BC and Alberta. PIPEDA does apply to pseudonymized information because such information ultimately permits reidentification. The standard for identifiability under PIPEDA had been set by the courts as a “’serious possibility’ that an individual could be identified through the use of that information, alone or in combination with other available information.” (Cadillac Fairview at para 143). It is not an absolute standard (although the proposed definition for anonymized data in C-27 currently seems closer to absolute). In any event, the original version of AIDA was meant to offer comfort to those concerned with the flat-out exclusion of anonymized data from the scope of the CPPA. Section 6 of AIDA provided that: 6. A person who carries out any regulated activity and who processes or makes available anonymized data in the course of that activity must, in accordance with the regulations, establish measures with respect to (a) the manner in which data is anonymized; and (b) the use or management of anonymized data. Problematically, however, AIDA only provided for data governance with respect to this particular subset of data. It contained no governance requirements for personal, pseudonymized, or non-personal data. Artificial intelligence systems will be only as good as the data on which they are trained. Data governance is a fundamental element of proper AI regulation – and it must address more than anonymized personal data. This is an area where the amendments to AIDA proposed by the Minister of Industry demonstrate clear improvements over the original version. To begin with, the old s. 6 is removed from AIDA. Instead of specific governance obligations for anonymized data, we see some new obligations introduced regarding data more generally. For example, as part of the set of obligations relating to general-purpose AI systems, there is a requirement to ensure that “measures respecting the data used in developing the system have been established in accordance with the regulations” (s. 7(1)a)). There is also an obligation to maintain records “relating to the data and processes used in developing the general-purpose system and in assessing the system’s capabilities and limitations” (s. 7(2)(b)). There are similar obligations the case of machine learning models that are intended to be incorporated into high-impact systems (s. 9(1)(a) and 9(2)(a)). Of course, whether this is an actual improvement will depend on the content of the regulations. But at least there is a clear signal that data governance obligations are expanded under the proposed amendments to AIDA. Broader data governance requirements in AIDA are a good thing. They will apply to data generally including personal and anonymized data. Personal data used in AI will also continue to be governed under privacy legislation and privacy commissioners will still have a say about whether data have been properly anonymized. In the case of PIPEDA (or the CPPA if and when it is eventually enacted), the set of principles for the development and use of generative AI issued by federal, provincial, and territorial privacy commissioners on December 8, 2023 make it clear that the commissioners understand their enabling legislation to provide them with the authority to govern a considerable number of issues relating to the use of personal data in AI, whether in the public or private sector. This set of principles send a strong signal to federal and provincial governments alike that privacy laws and privacy regulators have a clear role to play in relation to emerging and evolving AI technologies and that the commissioners are fully engaged. It is also an encouraging example of federal, provincial and territorial co-operation among regulators to provide a coherent common position on key issues in relation to AI governance.
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Privacy
Monday, 14 August 2023 06:06
Use by the Public Sector of Private Sector Personal DataThe following is a short excerpt from a new paper which looks at the public sector use of private sector personal data (Teresa Scassa, “Public Sector Use of Private Sector Personal Data: Towards Best Practices”, forthcoming in (2024) 47:2 Dalhousie Law Journal ) The full pre-print version of the paper is available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4538632 Governments seeking to make data-driven decisions require the data to do so. Although they may already hold large stores of administrative data, their ability to collect new or different data is limited both by law and by practicality. In our networked, Internet of Things society, the private sector has become a source of abundant data about almost anything – but particularly about people and their activities. Private sector companies collect a wide variety of personal data, often in high volumes, rich in detail, and continuously over time. Location and mobility data, for example, are collected by many different actors, from cellular service providers to app developers. Financial sector organizations amass rich data about the spending and borrowing habits of consumers. Even genetic data is collected by private sector companies. The range of available data is constantly broadening as more and more is harvested, and as companies seek secondary markets for the data they collect. Public sector use of private sector data is fraught with important legal and public policy considerations. Chief among these is privacy since access to such data raises concerns about undue government intrusion into private lives and habits. Data protection issues implicate both public and private sector actors in this context, and include notice and consent, as well as data security. And, where private sector data is used to shape government policies and actions, important questions about ethics, data quality, the potential for discrimination, and broader human rights questions also arise. Alongside these issues are interwoven concerns about transparency, as well as necessity and proportionality when it comes to the conscription by the public sector of data collected by private companies. This paper explores issues raised by public sector access to and use of personal data held by the private sector. It considers how such data sharing is legally enabled and within what parameters. Given that laws governing data sharing may not always keep pace with data needs and public concerns, this paper also takes a normative approach which examines whether and in what circumstances such data sharing should take place. To provide a factual context for discussion of the issues, the analysis in this paper is framed around two recent examples from Canada that involved actual or attempted access by government agencies to private sector personal data for public purposes. The cases chosen are different in nature and scope. The first is the attempted acquisition and use by Canada’s national statistics organization, Statistics Canada (StatCan), of data held by credit monitoring companies and financial institutions to generate economic statistics. The second is the use, during the COVID-19 pandemic, of mobility data by the Public Health Agency of Canada (PHAC) to assess the effectiveness of public health policies in reducing the transmission of COVID-19 during lockdowns. The StatCan example involves the compelled sharing of personal data by private sector actors; while the PHAC example involves a government agency that contracted for the use of anonymized data and analytics supplied by private sector companies. Each of these instances generated significant public outcry. This negative publicity no doubt exceeded what either agency anticipated. Both believed that they had a legal basis to gather and/or use the data or analytics, and both believed that their actions served the public good. Yet the outcry is indicative of underlying concerns that had not properly been addressed. Using these two quite different cases as illustrations, the paper examines the issues raised by the use of private sector data by government. Recognizing that such practices are likely to multiply, it also makes recommendations for best practices. Although the examples considered are Canadian and are shaped by the Canadian legal context, most of the issues they raise are of broader relevance. Part I of this paper sets out the two case studies that are used to tease out and illustrate the issues raised by public sector use of private sector data. Part II discusses the different issues and makes recommendations. The full pre-print version of the paper is available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4538632
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Privacy
Tuesday, 05 November 2019 06:54
Plans to sell smart meter data nixed over privacy and consultation concerns
The Ontario Energy Board (OEB) has just released a decision that should be of interest to those concerned about data governance for data sharing. The decision relates to an application by Ontario’s Smart Metering Entity (SME) for a licence to begin sharing Ontario’s smart metering data with third parties. The SME was established in Ontario as part of the governance structure for the data collected through government-mandated smart metering for all electricity consumers in the province. Smart meters in Ontario collect fine-grained electrical consumption data. There are clear privacy interests in this consumption data as a person’s patterns of electrical consumption can reveal much about their activities, habits and preferences. In theory, fine-grained, aggregate, deidentified electrical consumption data can be useful for a broad range of purposes, including feeding the ever-hungry data economy. The SME was charged with governing this data resource in a way that would meet the needs of third parties (researchers, governments, and the private sector) to have access to the data while respecting consumer privacy. In 2019, Merlynda Vilain and I published a paper about the SME and its mandate to govern smart metering data in the public interest. In its October 24, 2019 decision, the OEB considers an application by the SME seeking approval for its plan to provide access to smart metering data. The SME’s plan is built around three categories of data. The first, labelled “public offers”, consists of “highly aggregated products” ”such as monthly, seasonal or quarterly consumption data aggregated by postal district (i.e. the first digit of the postal code).” (OEB Order, p. 8) This data would be provided free of charge, and subject to unspecified terms and conditions. The second category of data is “standard private offerings”. This consists of “pre-designed extracts based on popular data requests”. The examples provided include “Hourly or daily consumption data aggregated by 6, 5, 4 or 3 digit Postal Code at the municipal level, specifying the Distributor Rate Class and Commodity Rate Class”, as well as different types of visualizations. This category of data would be made available subject to a Data Use Agreement and at “market prices”. The third category of data is “custom private offerings”, which are data sets customized to meet the demands of specific clients. These data sets would be subject to a Data Use Agreement and sold at “market price”. Market price, is, of course, different from a fee for cost recovery. The SME in its application indicated that not only would the fees charged cover the costs of producing the data sets, any profits from the sale of smart metering data would be put towards lowering the Smart Metering Charge. In other words, the sale of data could potentially result in lower energy costs. This is an example of a plan to sell aggregate consumer data with a view to benefitting the class as a whole, although the extent of any benefits is difficult to assess without more information about market pricing and about the privacy risks and implications of the shared data. On the privacy issues, the SME maintained that shared data would be de-identified, although it acknowledged that there was some (unspecified) reidentification risk. It argued that factors mitigating against reidentification would include its work with a privacy consultant, compliance with guidance from the Office of the Information and Privacy Commissioner, the use of Data Use Agreements to limit the actions of the party acquiring the data, and the establishment of an Ethics Review Committee. Those involved in data governance for data sharing will see how the SME’s proposal features some of the key elements and challenges in the data-sharing context. There is a perceived demand for high-value data, an attempt to meet that demand, privacy issues arising because the data is generated by individual activities and consumption, and a need to think about the terms and conditions of sharing, including cost/price. In this case, the data governance entity is a public body that must act under terms set by the regulator (the OEB), and it requires OEB approval of any data sharing plan. In this case, the OEB heard from the SME as well as a number of interveners, including the Building Owners and Managers Association, the Consumers Council of Canada, the Electricity Distributors Association, Ontario Power Generation Inc., and the Vulnerable Energy Consumers Coalition. The decision of the OEB is interesting for a number of reasons. First, the approach taken is a precautionary one – the OEB sends the SME back to the drawing board over concerns about privacy and about the pricing scheme. In doing so, it appears to have paid some attention to the sometimes heated data governance discussions that have been taking place in Canada. The OEB began by noting that none of the interveners objected to the first part of the SME plan – to make its “public offerings” category of data available to the public free of charge. In fact, this was the only part of the plan that received OEB approval. The OEB noted that “As these products would comprise highly aggregated data, they do not raise the same concerns about privacy as more tailored products.” It also concluded that the costs associated with preparing and sharing this data were part of the SME’s normal operations. More problematic were the other categories of data for which sharing was planned. The OEB accepted that customers have a reasonable expectation of privacy “albeit a “significantly attenuated” one” (at p. 13) in their energy consumption data. The Board also noted that for some commercial customers, the consumption data might be confidential commercial information. The OEB observed that in spite of the fact that the plan was to de-identify the data, there remained some reidentification risk. It stated that “in light of the concerns expressed by stakeholders in this proceeding, the SME should proceed cautiously with third party access”. (at 13-14) The OEB considered that consumers needed to be well-informed about the collection and sharing of their data, and that while the SME has attempted to consult on these issues, “a more comprehensive consumer engagement process should take place.” (at 14) The OEB noted that “it is not clear form the evidence that consumers support the notion that consumption data (even if de-identified) should be offered for sale to third parties.” (at 14) This approach reflects a shift in position on the part of the OEB. Past discussions of data sharing have regarded this data primarily as a public asset that should be put to use in the public interest. In the case of third party data sharing, this public interest was largely in the stimulation of the economy and innovation. What is different in this OEB Order is a much greater recognition of the importance of individual and collective consent. In its directions to the SME, the OEB asks for more detail from the SME’s consultation with consumers, the need to propose “a protocol for receiving and dealing with consumer complaints regarding the release of the data” (at 14), a plan for informing consumers about the release of deidentified information to third parties, and a need to obtain approval “of the basic terms of any Data Use Agreement with third parties.” (at 14). In addition to these concerns about privacy and consultation, the OEB expressed reservations about the SME’s plans to share data at ‘market prices’. Some of the interveners noted that the SME held a monopoly position with respect to smart metering data, and there was therefore no market price for such data. The OEB called for the SME to develop a marketing plan that “should address pricing to ensure reasonably priced access by commercial and non-commercial users.” (at 14) This decision is important and interesting for a number of reasons. First, it reflects a cautious, go-slow, precautionary approach to data sharing that might not have existed before Ontarians lost their data innocence in the debates over plans for Sidewalk Toronto. The OEB’s concerns include reidentification risk, proper consultation, accountability, and the terms and conditions for data sharing. The need to adequately and appropriately consult those individuals whose data is to be shared is an important theme in this decision. Although the SME claims to have made efforts to include consumer perspectives, the OEB is not satisfied that these efforts went far enough. The decision also lands in the middle of the Ontario government’s data strategy consultation (which I have written about here, here and here). The consultation process – which lacks detail and is moving far too quickly – is clearly geared towards increasing data sharing and leveraging data for economic development and innovation, all while maintaining public ‘trust and confidence’. The Ontario government clearly wants to make some quick changes. Yet what this OEB decision reflects is a need to adopt a precautionary approach and to ensure adequate consultation and public awareness. As frameworks, models and templates are developed, things can being to move more quickly – but there is real value in getting things right from the outset.
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Monday, 28 October 2019 07:03
Why Ontarians should be concerned about Ontario’s data strategy
Ontario is currently engaged in a data strategy consultation process. The stated goals are to create economic opportunities and to improve government services by facilitating greater data sharing and by using more analytics and artificial intelligence. The plan is to do this while maintaining the ‘trust and confidence’ of Ontarians. The consultation process has had an extraordinarily low profile considering what is at stake. That said, it is happening so quickly that it is easy to miss. Even for those paying attention, the consultation is long on boosterism and short on detail. This post outlines some reasons why Ontarians should be concerned. 1. Major transformation without proper debate/consultation Developing a data strategy is a good idea. Data-driven innovations are dramatically changing our economy and society. There are many ways for government to become more effective and efficient by embracing new technologies. It could also become more transparent and find new ways to engage citizens. To do these things some changes to the law and policy infrastructure will be necessary. Businesses seeking to innovate and grow in the digital and data economy will need better access to quality data and, among other things, new models for data governance and data sharing. Data-driven technologies also bring with them risks of harm and these too may need new legislation or normative frameworks. There is a lot to consider and some of the changes will be transformative, and will rely upon citizen data. These are all good reasons to consult deeply and broadly, both to seek input and to lay the foundations for a transparent public engagement. The data strategy consultation was announced in February 2019, with a report to be published before the end of the year. The consultation centres around three discussion papers, the first of which was only made available in mid-August 2019 and the last of which has just been released, with comments due by the end of November. The public meetings held as part of these consultations are taking place on very short notice. The process is hurried, obscure and fails to properly engage the full range of stakeholders. 2, Superficiality Quite apart from the rushed nature of the process, the discussion papers are woefully inadequate. They are full of assertions of the benefits of what is planned, with the occasional nod to the importance of privacy and trust. There is little detail about the nature, scope or timelines for what the government plans to do. The discussion papers give only brief glimpses of things that merit a much more detailed treatment. For example, on the issue of broad-scale data sharing between different government departments and agencies, we are told that “while ‘connecting the dots’ between datasets can help government provide better services, there are privacy and cybersecurity risks to be managed.” ‘Connecting the dots’ can mean all sorts of things. Perhaps data matching will be used to find ways to improve service delivery. Data analytics might also be used to discover or anticipate certain citizen behaviors. This could include identifying patterns that suggest certain individuals are fraudulently obtaining benefits or cheating on taxes, or identifying children potentially at risk. The data-matching possibilities are endless. And while the goals might be important, there are significant risks of harm. Beyond privacy concerns, there are issues of discrimination and undue surveillance. What processes will be in place to ensure transparency and accountability as these programs are developed and implemented? The consultation documents are so general and superficial that they fail to identify, let alone invite engagement on some of the real challenges posed by the government’s (undisclosed) plans. An alarming glimpse of what lies beneath the superficial gloss of these documents is found in the second discussion paper which focuses on “Creating Economic Benefits”. The document talks about the value that can be derived by the private sector from data shared by governments. It then casually states “Given that Ontario has a wealth of data in digital health assets, clinical and administrative health data can also be considered as a high-value dataset that may present various opportunities for Ontario.” This suggests that the government is planning to make the personal health data of Ontarians available to the private sector. As the Privacy Commissioner, in his comment on this aspect of the discussion paper, aptly notes, “It is important to distinguish between the high value of health-related data in terms of utilizing it to foster innovation and research, and its high monetary value (that is, health-related data as a commodity to be sold as a source of revenue for the government). The specific scope of what the government may be contemplating is not clear from the discussion paper.” 3. The Gaps The government is designing a data strategy but its focus is relatively narrow. Ontario’s Privacy Commissioner has pointed out that there are many other data-related reforms that could enhance government transparency including open contracting and open procurement, as well as other reforms to improve access to government information. While the Simpler, Faster, Better Services Act introduced reforms around open data, open data is not necessarily the best route to transparency, especially with a government that has indicated it wants to be more strategic about its release of open data and that sees it primarily as a driver of the economy. 4. Social impacts This consultation process relies far too much on the increasingly tired trope of “trust and confidence”. The first consultation document, a truly abstract exercise in asking people what they think about plans that have neither been discussed or disclosed, is titled “Promoting Public Trust and Confidence”. Trust and confidence must be earned, not promoted. Although the first discussion paper identifies a broad range of issues, including bias and discrimination, surveillance, data privacy and security, these are raised largely in the abstract. In the subsequent discussion papers on creating economic benefits and smarter government, the issues are boiled down to individual data privacy and security. There needs to be a detailed, robust and informed discussion on the impacts of proposed technological changes on individuals and communities, as well as on limits, oversight and safeguards. 5. ‘Stakeholders’ and the Rest of Us Another issue that should concern Ontarians in this consultation is whose voices really matter. The lightning fast consultation hints at some major changes, many of which are driven by industry demands (such as the massive sharing of personal health data with the private sector). Industry clearly has the ear of government and does not need the consultation process in order to be heard. The data strategy consultation has been poorly publicized. The discussion papers have been published late in the process, contain little detail, and have narrow windows for providing feedback. The paper on trust and confidence was released in mid-August with comments due just after Labour Day. The timing could hardly be worse for ensuring public engagement. The public meetings around the province are scheduled with very short notice. This consultation favours larger organizations with the resources to throw together a quick response or to find someone who can attend a meeting at short notice. It does not favour the general public, nor does it favour civil society groups and academia with limited resources and personnel. At the same time that this speedy data consultation is taking place, there are closed-door consultations underway with “stakeholders” about reforms to the Personal Health Information Protection Act. While there is no doubt that much could be done to modernize this legislation, the fact that it taking place behind the scenes of the superficial data strategy consultation is deeply troubling. There is also, reportedly, work being done on an ethical AI strategy for the government. Not only is this not part of any public consultation process, it is only hinted at in the third discussion paper. It is also profoundly disturbing that institutions that serve the public interest such as the Office of the Information and Privacy Commissioner so clearly do not have the ear of government. The Privacy Commissioner’s input has been reduced to letters written in response to the discussion papers. These letters politely invite the government to seek out his expertise on issues that are squarely within his mandate.
Proposed massive technological change, ‘trust us’ assurances about privacy that fall short of the mark, and a disregard for early and inclusive consultations are a recipe for disaster. People are not data cows to be milked by government and industry, and acknowledged with only a pat on the rump and a vague assurance that they will be well looked after. The data strategy must serve all Ontarians and must be built on a foundation of credible and meaningful public engagement. As the Sidewalk Toronto process has demonstrated, people do care, the private sector doesn’t have all the answers, and transformative change needs social legitimacy.
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Monday, 23 September 2019 06:39
Second Ontario Data Strategy Paper Hints at Plans to Share More Citizen DataThe second discussion paper in Ontario’s lightning-quick consultation on a new data strategy for the province was released on September 20, 2019. Comments are due by October 9, 2019. If you blink, you will miss the consultation. But if you read the discussion paper, it will make you blink – in puzzlement. Although it is clear from its title that Ontario wants to “create economic benefits” through data, the discussion paper is coy, relying mainly on broad generalities with occasional hints at which might actually be in the works. Governments around the world are clearly struggling to position their countries/regions to compete in a burgeoning data economy. Canada is (until the election period cooled things off) in the middle of developing its own digital and data strategy. Ontario launched its data strategy consultation in February 2019. The AI industry (in which Canada and Ontario both aspire to compete) is thirsty for data, and governments are contemplating the use of AI to improve governance and to automate decision-making. It is not surprising, therefore, that this document tackles the important issue of how to support the data economy in Ontario. The document identifies a number of challenges faced by Ontario. These include skill and knowledge deficits in existing industries and businesses; the high cost of importing new technologies, limited digital infrastructure outside urban core areas, and international competition for highly qualified talent for the data economy. The consultation paper makes clear that the data strategy will need to address technology transfer, training/education, recruitment, and support for small businesses. Beyond this, a key theme of the document is enhancing access to data for businesses. It is with respect to data that the consultation paper becomes troublingly murky. It begins its consideration of data issues with a discussion of open government data. Ontario has had an open data portal for a number of years and has been steadily developing it. A new law, pushed through in the omnibus budget bill that followed the Ford government’s election is the first in Canada to entrench open government data in law. The consultation document seems to suggest that the government will put more resources into open data. This is good. However, the extent of the open data ambitions gives pause. The consultation document notes, “it is important for governments to ensure that the right level of detailed data is released while protecting government security and personal privacy.” Keep in mind that up until now, the approach to open data has been to simply not release as open data datasets that contain personal information. This includes data sets that could lead to the reidentification of individuals when combined with other available data. The consultation paper states “Ontario’s government holds vast amounts of data that can help businesses develop new products and services that make Ontarian’s lives easier, while ensuring that their privacy is protected.” These references to open data and privacy protection are indications that the government is contemplating that it will make personal data in some form or another available for sharing. Alarmingly, businesses may be invited to drive decision-making around what data should be shared. The document states, “New collaboration with businesses can help us determine which data assets have the greatest potential to drive growth.” An out-of-the-blue example provided in the consultation paper is even more disturbing. At a point where the document discusses classic categories of important open data such as geospatial reference and weather data, it suddenly states “Given that Ontario has a wealth of data in digital health assets, clinical and administrative health data can also be considered a high-value dataset that may present various opportunities for Ontario.” If personal data is on the table (and the extent to which this is the case should be a matter of serious public consultation and not lightning-round Q & A), then governance becomes all the more important. The consultation paper acknowledges the importance of governance – of a sort. It suggests new guidelines (the choice of words here is interesting – as guidelines are not laws and are usually non-binding) to help govern how data is shared. The language of standards, guidance and best practices is used. Words such as law, regulation and enforcement are not. While “soft law” instruments can have a role to play in a rapidly changing technological environment, Canadians should be justifiably wary of a self-regulating private sector – particularly where there is so much financially at stake for participating companies. It should also be wary of norms and standards developed by ‘stakeholder’ groups that only marginally represent civil society, consumer and privacy interests. If there is one thing that governments in Canada should have learned from the Sidewalk Toronto adventure, it is that governments and the private sector require social licence to collect and share a populations’ personal data. What this consultation does instead is say to the public, “the data we collect about you will be very valuable to businesses and it is in the broader public interest that we share it with them. Don’t worry, we’re thinking about how to do it right.” That is an illustration of paternalism, not consultation or engagement. It is certainly not how you gain social licence. The Ontario government’s first Consultation Paper, which I discuss here was about “promoting trust and confidence”, and it ostensibly dealt with privacy, security and related issues. However, the type of data sharing that is strongly hinted at in the second discussion paper is not discussed in that first paper and the consultation questions in that document do not address it either. There is a great deal of non-personal government data that can be valuable for businesses and that might be used to drive innovation. There is already knowledge and experience around open data in Ontario, and building upon this is a fine objective. Sharing of personal and human behavioural data may also be acceptable in some circumstances and under some conditions. There are experiments in Canada and in other countries with frameworks for doing this that are worth studying. But this consultation document seems to reflect a desire to put all government data up for grabs, without social licence, with only the vaguest plans for protection, and with a clear inclination towards norms and standards developed outside the usual democratic processes. Yes, there is a need to move quickly – and to be “agile” in response to technological change. But speed is not the only value. There is a difference between a graceful dive and a resounding belly flop – both are fast, only one is agile.
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Tuesday, 13 August 2019 11:50
Comments on the Discussion Paper 'Promoting Trust and Confidence in Ontario's Data Economy'
On July 31, 2019 the Ontario Government released a discussion paper titled Promoting Trust and Confidence in Ontario’s Data Economy. This is the first in a planned series of discussion papers related to the province’s ongoing Data Strategy consultation. This particular document focuses on the first pillar of the strategy: Promoting Trust and Confidence. The other pillars are: Creating Economic Benefit; and Enabling Better, Smarter Government. The entire consultation process is moving at lightning speed. The government plans to have a final data strategy in place by the end of this calendar year. My first comment on the document is about timing. A release on July 31, with comments due by September 6, means that it hits both peak vacation season and mad back to school rush. This is not ideal for gathering feedback on such an important set of issues. A further timing issue is the release of this document and the call for comments before the other discussion papers are available. The result is a discussion paper that considers trust and confidence in a policy vacuum, even though it makes general reference to some pretty big planned changes to how the public sector will handle Ontarians’ personal information as well as planned new measures to enable businesses to derive economic benefit from data. It would have been very useful to have detailed information about what the government is thinking about doing on these two fronts before being asked what would ensure ongoing trust and confidence in the collection, use and disclosure of Ontarians’ data. Of course, this assumes that the other two discussion documents will contain these details – they might not. My second comment is about the generality of this document. This is not a consultation paper that proposes a particular course of action and seeks input or comment. It describes the current data context in broad terms and asks questions that are very general and open-ended. Here are a couple of examples: “How can the province help businesses – particularly small and medium-sized businesses – better protect their consumers’ data and use data-driven practices responsibly?” “How can the province build capacity and promote culture change concerning privacy and data protection throughout the public sector (e.g., through training, myth-busting, new guidance and resources for public agencies)?” It’s not that the questions are bad ones – most of them are important, challenging and worth thinking about. But they are each potentially huge in scope. Keep in mind that the Data Strategy that these questions are meant to inform is to be released before the end of 2019. It is hard to believe that anything much could be done with responses to such broad questions other than to distil general statements in support of a strategy that must already be close to draft stage. That doesn’t mean that there are not a few interesting nuggets to mine from within the document. Currently, private sector data protection in Ontario is governed by the federal Personal Information Protection and Electronic Documents Act. This is because, unlike Alberta, B.C. and Quebec, Ontario has not enacted a substantially similar private sector data protection law. Is it planning to? It is not clear from this document, but there are hints that it might be. The paper states that it is important to “[c]larify and strengthen Ontario’s jurisdiction and the application of provincial and federal laws over data collected from Ontarians.” (at p. 13) One of the discussion questions is “How can Ontario promote privacy protective practices throughout the private sector, building on the principles underlying the federal government’s private sector privacy legislation (the Personal Information Protection and Electronic Documents Act)?” Keep in mind that a private member’s bill was introduced by a Liberal backbencher just before the last election that set out a private sector data protection law for Ontario. There’s a draft text already out there. Given that this is a data strategy document for a government that is already planning to make major changes to how public sector data is handled, there are a surprising number of references to the private sector. For example, in the section on threats and risks of data-driven practices, there are three examples of data breaches, theft and misuse – none of which are from Ontario’s public sector. This might support the theory that private sector data protection legislation is in the offing. On the other hand, Ontario has jurisdiction over consumer protection; individuals are repeatedly referred to as “consumers” in the document. It may be that changes are being contemplated to consumer protection legislation, particularly in areas such as behavioural manipulation, and algorithmic bias and discrimination. Another question hints at possible action around online consumer contracts. These would all be interesting developments. There is a strange tension between public and private sectors in the document. Most examples of problems, breaches, and technological challenges are from the private sector, while the document remains very cagey about the public sector. It is this cageyness about the public sector that is most disappointing. The government has already taken some pretty serious steps on the road to its digital strategy. For example, it is in the process of unrolling much broader sharing of personal information across the public sector through amendments to the Freedom of Information and Protection of Privacy Act passed shortly after the election. These will take effect once data standards are in place (my earlier post on these amendments is here). The same bill enacted the Simpler, Faster, Better, Services Act. This too awaits regulations setting standards before it takes effect (my earlier post on this statute is here). These laws were passed under the public radar because they were rushed through in an omnibus budget bill and with little debate. It would be good to have a clear, straightforward document from the government that outlines what it plans to do under both of these new initiatives and what it will mean for Ontarians and their personal data. Details of this kind would be very helpful in allowing Ontarians to make informed comments on trust and confidence. For example, the question “What digital and data-related threats to human rights and civil liberties pose the greatest risk for Ontarians” (p. 14) might receive different answers if readers were prompted to think more specifically about the plans for greater sharing of personal data across government, and a more permissive approach to disclosures for investigatory purposes (see my post on this issue here). The discussion questions are organized by category. Interestingly, there is a separate category for ‘Privacy, Data Protection and Data Governance’. That’s fine – but consider that there is a later category titled Human Rights and Civil Liberties. Those of us who think privacy is a human right might find this odd. It is also odd that the human rights/civil liberties discussion is separated from data governance since they are surely related. It is perhaps wrong to read too much into this, since the document was no doubt drafted quickly. But thinking about privacy as a human right is important. The document’s focus on trust and confidence seems to relegate privacy to a lower status. It states: “A loss of trust reduces people’s willingness to share data or give social license for its use. Likewise, diminishing confidence impedes the creative risk-taking at the heart of experimentation, innovation and investment.” (at p. 8) In this plan, protection of privacy is about ensuring trust which will in turn foster a thriving data economy. The fundamental question at the heart of this document is thus not: ‘what measures should be taken to ensure that fundamental values are protected and respected in a digital economy and society”. Rather, it is: ‘What will it take to make you feel ok about sharing large quantities of personal information with business and government to drive the economy and administrative efficiencies?’ This may seem like nitpicking, but keep in mind that the description of the ‘Promoting Trust and Confidence’ pillar promises “world-leading, best-in-class protections that benefits the public and ensures public trust and confidence in the data economy” (page 4). Right now, Europe’s GDPR offers the world-leading, best-in-class protections. It does so because it treats privacy as a human right and puts the protection of this and other human rights and civil liberties at the fore. A process that puts feeling ok about sharing lots of data at the forefront won’t keep pace.
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Monday, 15 July 2019 07:56
Data Governance for Data Sharing: Lessons for Toronto's Quayside?
Smart city data governance has become a hot topic in Toronto in light of Sidewalk Labs’ proposed smart city development for Toronto’s waterfront. In its Master Innovation Development Plan (MIDP), Sidewalk Labs has outlined a data governance regime for “urban data” that will be collected in the lands set aside for the proposed Sidewalk Toronto smart city development. The data governance scheme sets out to do a number of different things. First, it provides a framework for sharing ‘urban data’ with all those who have an interest in using this data. This could include governments, the private sector, researchers or civil society. Because the data may have privacy implications, the governance scheme must also protect privacy. Sidewalk Labs is also proposing that the governance body be charged with determining who can collect data within the project space, and with setting any necessary terms and conditions for such collection and for any subsequent use or sharing of the data. The governance body, named the Urban Data Trust (UDT), will have a mandate to act in the public interest, and it is meant to ensure that privacy is respected and that any data collection, use or disclosure – even if the data is non-personal or deidentified – is ethical and serves the public interest. They propose a 5-person governance body, with representation from different stakeholder communities, including “a data governance, privacy, or intellectual property expert; a community representative; a public-sector representative; an academic representative; and a Canadian business industry representative” (MIDP, Chapter 5, p. 421). The merits and/or shortcomings of this proposed governance scheme will no doubt be hotly debated as the public is consulted and as Waterfront Toronto develops its response to the MIDP. One thing is certain – the plan is sure to generate a great deal of discussion. Data governance for data sharing is becoming an increasingly important topic (it is also relevant in the Artificial Intelligence (AI) context) – one where there are many possibilities and proposals and much unexplored territory. Relatively recent publications on data governance for data sharing include reports by Element AI, MaRS, and the Open Data Institute). These reflect both the interest in and the uncertainties around the subject. Yet in spite of the apparent novelty of the subject and the flurry of interest in data trusts, there are already many different existing models of data governance for data sharing. These models may offer lessons that are important in developing data governance for data sharing for both AI and for smart city developments like Sidewalk Toronto. My co-author Merlynda Vilain and I have just published a paper that explores one such model. In the early 2000’s the Ontario government decided to roll out mandatory smart metering for electrical consumption in the province. Over a period of time, all homes and businesses would be equipped with smart meters, and these meters would collect detailed data in real time about electrical consumption. The proposal raised privacy concerns, particularly because detailed electrical consumption data could reveal intimate details about the activities of people within their own homes. The response to these concerns was to create a data governance framework that would protect customer privacy while still reaping the benefits of the detailed consumption data. Not surprisingly, as the data economy surged alongside the implementation of smart metering, the interest in access to deidentified electrical consumption data grew across different levels of government and within the private sector. The data governance regime had therefore to adapt to a growing demand for access to the data from a broadening range of actors. Protecting privacy became a major concern, and this involved not just applying deidentification techniques, but also setting terms and conditions for reuse of the data. The Smart Metering Entity (SME), the data governance body established for smart metering data, provides an interesting use case for data governance for data sharing. We carried out our study with this in mind; we were particularly interested in seeing what lessons could be learned from the SME for data governance in other context. We found that the SME made a particularly interesting case study because it involved public sector data, public and private sector stakeholders, and a considerable body of relatively sensitive personal information. It also provides a good example of a model that had to adapt to changes over a relatively short period of time – something that may be essential in a rapidly evolving data economy. There were changes in the value of the data collected, and new demands for access to the data by both public and private sector actors. Because of the new demand and new users, the SME was also pushed to collect additional data attributes to enrich the value of its data for potential users. The SME model may be particularly useful to think about in the smart cities context. Smart cities also involve both public and private sector actors, they may involve the collection of large volumes of human behavioural data, and this gives rise to a strong public interest in appropriate data governance. Another commonality is that in both the smart metering and smart cities contexts individuals have little choice but to have their data collected. The underlying assumption is that the reuse and repurposing of this data across different contexts serves the public interest in a number of different ways. However, ‘public interest’ is a slippery fish and is capable of multiple interpretations. With a greatly diminished role for consent, individuals and communities require frameworks that can assist not just in achieving the identified public interests – but in helping them to identify and set them. At the same time protecting individual and community privacy, and ensuring that data is not used in ways that are harmful or exploitative. Overall, our study gave us much to think about, and its conclusion develops a series of ‘lessons’ for data governance for data sharing. A few things are worthy of particular note in relation to Sidewalk Labs’ proposed Urban Data Trust. First, designing appropriate governance for smart metering data was a significant undertaking that took a considerable amount of time, particularly as demands for data evolved. This was the case even though the SME was dealing only with one type of data (smart metering data), and that it was not responsible for overseeing new requests to collect new types of data. This is a sobering reminder that designing good data governance – particularly in complex contexts – may take considerable time and resources. The proposed UDT is very complex. It will deal with many different types of data, data collectors, and data users. It is also meant to approve and set terms and conditions for new collection and uses. The feasibility of creating robust governance for such a complex context is therefore an issue – especially within relatively short timelines for the project. Defining the public interest – which both the SME and the UDT are meant to serve – is also a challenge. In the case of the SME, the democratically elected provincial government determines the public interest at a policy level, and it is implemented through the SME. Even so, there are legitimate concerns about representation and about how the public interest is defined. With the UDT, it is not clear who determines the public interest or how. There will be questions about who oversees appointments to the UDT, and how different stakeholders and their interests are weighted in its composition and in its decision-making.
Our full paper can be found in open access format on the website of the Centre for International Governance Innovation (CIGI): here.
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Monday, 17 December 2018 06:43
Whose Data Is It? A Key Question for the Quayside Development
Digital and data governance is challenging at the best of times. It has been particularly challenging in the context of Sidewalk Labs’ proposed Quayside development for a number of reasons. One of these is (at least from my point of view) an ongoing lack of clarity about who will ‘own’ or have custody or control over all of the data collected in the so-called smart city. The answer to this question is a fundamentally important piece of the data governance puzzle. In Canada, personal data protection is a bit of a legislative patchwork. In Ontario, the collection, use or disclosure of personal information by the private sector, and in the course of commercial activity, is governed by the federal Personal Information Protection and Electronic Documents Act (PIPEDA). However, the collection, use and disclosure of personal data by municipalities and their agencies is governed by the Municipal Freedom of Information and Protection of Privacy Act (MFIPPA), while the collection, use and disclosure of personal data by the province is subject to the Freedom of Information and Protection of Privacy Act (FIPPA). The latter two statutes – MFIPPA and FIPPA – contain other data governance requirements for public sector data. These relate to transparency, and include rules around access to information. The City of Toronto also has information management policies and protocols, including its Open Data Policy. The documentation prepared for the December 13, 2018 Digital Strategy Advisory Panel (DSAP) meeting includes a slide that sets out implementation requirements for the Quayside development plan in relation to data and digital governance. A key requirement is: “Compliance with or exceedance of all applicable laws, regulations, policy documents and contractual obligations” (page 95). This is fine in principle, but it is not enough on its own to say that the Quayside project must “comply with all applicable laws”. At some point, it is necessary to identify what those applicable laws are. This has yet to be done. And the answer to the question of which laws apply in the context of privacy, transparency and data governance, depends upon who ultimately is considered to ‘own’ or have ‘custody or control’ of the data. So – whose data is it? It is troubling that this remains unclear even at this stage in the discussions. The fact that Sidewalk Labs has been asked to propose a data governance scheme suggests that Sidewalk and Waterfront may be operating under the assumption that the data collected in the smart city development will be private sector data. There are indications buried in presentations and documentation that also suggest that Sidewalk Labs considers that it will ‘own’ the data. There is a great deal of talk in meetings and in documents about PIPEDA, which also indicates that there is an assumption between the parties that the data is private sector data. But what is the basis for this assumption? Governments can contract with a private sector company for data collection, data processing or data stewardship – but the private sector company can still be considered to act as an agent of the government, with the data being legally under the custody or control of the government and subject to public sector privacy and freedom of information laws. The presence of a private sector actor does not necessarily make the data private sector data. If the data is private sector data, then PIPEDA will apply, and there will be no applicable access to information regime. PIPEDA also has different rules regarding consent to collection than are found in MFIPPA. If the data is considered ultimately to be municipal data, then it will be subject to MFIPPA’s rules regarding access and privacy, and it will be governed by the City of Toronto’s information management policies. These are very different regimes, and so the question of which one applies is quite fundamental. It is time for there to be a clear and forthright answer to this question.
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Sunday, 25 November 2018 12:47
Some thoughts on Smart Cities and Data Governance
On November 23, 2018, Waterfront Toronto hosted a Civic Labs workshop in Toronto. The theme of the workshop was Smart City Data Governance. I was asked to give a 10 minute presentation on the topic. What follows is a transcript of my remarks. Smart city governance relates to how smart cities govern themselves and their processes; how they engage citizens and how they are transparent and accountable to them. Too often the term “smart city” is reduced to an emphasis on technology and on technological solutionism – in other words “smart cities” are presented as a way in which to use technology to solve urban problems. In its report on Open Smart Cities, Open North observes that “even when driven in Canada by good intentions and best practices in terms of digital strategies, . . . [the smart city] remains a form of innovation and efficient driven technological solutionism that is not necessarily integrated with urban plans, with little or no public engagement and little to no relation to contemporary open data, open source, open science or open government practices”. Smart cities governance puts the emphasis on the “city” rather than the “smart” component, focusing attention on how decisions are made and how the public is engaged. Open North’s definition of the Open Smart City is in fact a normative statement about digital urban governance: An Open Smart City is where residents, civil society, academics, and the private sector collaborate with public officials to mobilize data and technologies when warranted in an ethical, accountable and transparent way to govern the city as a fair, viable and liveable commons and balance economic development, social progress and environmental responsibility. This definition identifies the city government as playing a central role, with engagement from a range of different actors, and with particular economic, social and environmental goals in mind. This definition of a smart city involves governance in a very basic and central way – stakeholders are broadly defined and they are engaged not just in setting limits on smart cities technology, but in deciding what technologies to adopt and deploy and for what purposes. There are abundant interesting international models of smart city governance – many of them arise in the context of specific projects often of a relatively modest scale. Many involve attempts to find ways to include city residents in both identifying and solving problems, and the use of technology is relevant both to this engagement and to finding solutions. The Sidewalk Toronto project is somewhat different since this is not a City of Toronto smart city initiative. Rather, it is the tri-governmental entity Waterfront Toronto that has been given the lead governance role. This has proved challenging since while Waterfront Toronto has a public-oriented mandate, it is not a democratically elected body, and its core mission is to oversee the transformation of specific brownfield lands into viable communities. This is important to keep in mind in thinking about governance issues. Waterfront Toronto has had to build public engagement into its governance framework in ways that are different from a municipal government. The participation of federal and provincial privacy commissioners, and representatives from federal and provincial governments feed into governance as does the DSAP and there has been public outreach. There will also be review of and consultation of the Master Innovation Development Plan (MIDP) once it is publicly released. But it is a different model from city government and this may set it apart in important ways from other smart cities initiatives in Canada and around the world. Setting aside for a moment the smart cities governance issue, let’s discuss data governance. The two are related – especially with respect to the issue of what data is collected in the smart city and for what purposes. Broadly speaking, data governance goes to the question of how data will be stewarded (and by whom) and for what purposes. Data governance is about managing data. As such, it is not a new concept. Data governance is a practice that is current in both private and public sector contexts. Most commonly it takes place within a single organization which develops practices and protocols to manage its existing and future data. Governance issues include considering who is responsible for the data, who is entitled to set the rules for access to and reuse of it, how those rules will be set, and who will profit/benefit from the data and on what terms. It also includes addressing issues such as data security, standards, interoperability, and localization. Where the data include personal information, compliance with privacy laws is an aspect of data governance. But governance is not limited to compliance – for example, an organization may adopt higher standards than those required by privacy law, or may develop novel approaches to managing and protecting personal information. There are many different data governance models. Some (particularly in the public sector) are shaped by legislation, regulations and government policies. Others may be structured by internal policies, standards, industry practice, and private law instruments such as contracts or trusts. As the term is commonly used, data governance does not necessarily implicate citizen involvement or participation in the same way as “smart city governance” does – it is the “city” part of “smart city governance” that brings in to focus democratic principles of transparency, accountability, engagement and so on. However, where there is a public sector dimension to the collection or control of data, then public sector laws, including those relating to transparency and accountability, may apply. With the rise of the data economy, data sharing is becoming an important activity for both public and private sector actors. As a result, new models of data governance are needed to facilitate data sharing. There are many different benefits that flow from data sharing. It may be carried out for financial gain, or it may be done to foster innovation, enable new insights, stimulate the economy, increase transparency, solve thorny problems, and so on. There are also different possible beneficiaries. Data may be shared amongst a group of entities each of which will find advantages in the mutual pooling of their data resources. Or it may be shared broadly in the hope of generating new data-based solutions to existing problems. In some cases, data sharing has a profit motive. The diversity of actors, beneficiaries, and motivations, makes it necessary to find multiple, diverse and flexible frameworks and principles to guide data sharing arrangements. Open government data regimes are an important example of a data governance model for data sharing. Many governments have decided that opening government data is a significant public policy goal, and have done tremendous amount of work to create the infrastructure not just for sharing data, but for doing it in a useful, accessible and appropriate manner. This means the development of standards for data and metadata, and the development of portals and search functions. It has meant paying attention to issues of interoperability. It has also required governments to consider how best to protect privacy and confidential information, or information that might impact on security issues. Once open, the sharing frameworks are relatively straightforward -- open data portals typically offer data to anyone, with no registration requirement, under a simple open licence. Governments are not the only ones developing open data portals – research institutions are increasingly searching for ways in which to publicly share research outputs including publications and data. Some research data infrastructures support sharing, but not necessarily on fully open terms – this requires another level of consideration as to the policy reasons for limiting access, how to limit access effectively, and how to set and ensure respect for appropriate limits on reuse. The concept of a data trust has also received considerable attention as a means of data sharing. The term data trust is now so widely and freely used that it does not have a precise meaning. In its publication “What is a Data Trust”, the ODI identifies at least 5 different concepts of a data trust, and they provide examples of each: · A data trust as a repeatable framework of terms and mechanisms. · A data trust as a mutual organisation. · A data trust as a legal structure. · A data trust as a store of data. · A data trust as public oversight of data access.The diversity of “data trusts” means that there are a growing number of models to study and consider. However, it also makes it a little dangerous to talk about “data trust” as if it has a precise meaning. With data trusts, the devil is very much in the details. If Sidewalk Labs is to propose a ‘data trust’ for the management of data gathered in the Sidewalk Toronto development, then it will be important to probe into exactly what the term means in this context. What Sidewalk Labs is proposing is a particular vision of a data trust as a data governance model for data sharing in a smart cities development. It is admittedly a work in progress. It has some fairly particular characteristics. For example, not only is it a framework to set the parameters for sharing the subset “urban data” (defined by Sidewalk Labs) collected through the project, it also contemplates providing governance for any proposals by third parties who might want to engage in the collection of new kinds, categories or volumes of data. In thinking about the proposed ‘trust’, some questions I would suggest considering are: 1) What is the relationship between the proposed trust and the vision for smart city governance? In other words, to what extent is the public and/or are public sector decision-makers engaged in determining what data will be governed by the trust, on what terms, for whose benefit, and on what terms will sharing take place? 2) A data governance model does not make up for a robust smart city governance up front (in identifying the problems to be solved, the data to be collected to solve them, etc.). If this piece is missing, then discussion of the trust may involve discussing the governance of data where there is no group consensus or input as to its collection. How should this be done (if at all)? 3) A data governance model can be created for the data of a single entity (e.g. an open government portal, or a data governance framework for a corporation); but it can also be developed to facilitate data sharing between entities, or even between a group of entities and a broader public. So an important question in the ST context is what model is this? Is this Sidewalk Labs data that is being shared? Or is it Waterfront’s? Or the City’s? Who has custody/control or ownership of the data that will be governed by the ‘trust’? 4) Data governance is crucial with respect to all data held by an entity. Not all data collected through the Sidewalk Toronto project will fall within Sidewalk’s definition of “urban data” (for which the ‘trust’ is proposed). If the data governance model under consideration only deals with a subset of data, then there must be some form of data governance for the larger set. What is it? And who determines its parameters?
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