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Teresa Scassa

Teresa Scassa

Law and the “Sharing Economy”: Regulating Online Market Platforms is a new, peer-reviewed collection of papers co-edited by Derek McKee, Finn Makela and myself. The book is the product of a workshop held in January 2017. It is published in late November 2018 by the University of Ottawa press in both print and open access PDF formats.

The title of the book uses scare quotes around ‘Sharing Economy’ because of the deep ambivalence felt about the term amongst contributors to the volume, and the inability to find a suitable alternative. The term ‘sharing economy’ is used by some to suggest an alternative to the market; others have used it to describe activities taking place over large, commercial platforms. And, while some of the platforms use the rhetoric of helping ordinary individuals make ends meet by providing them with the ability to commercialize (‘share’) underutilized resources, the reality is that many of the large platform companies have resulted in other resources finding their way into the ‘sharing economy’. These resources may include, for example, living spaces once rented out on a long term basis that now turn greater profits as short term accommodation. Platform companies have had broad disruptive impacts. Our authors consider their impacts on licensing regimes, alternative dispute resolution, legal normativity, local governance, specific industries, and labour rights. The also consider platform companies’ digital data, their relationship to international trade agreements, and the competition law and policy issues they raise.

The collection of papers in this book offer “a set of diverse lenses through which we can examine both the sharing economy and its broader social impacts, and from which certain key themes emerge” (introduction, p. 5). The book is organized into 5 broad themes: Technologies of Regulation; Regulating Technology; The Space of Regulation – Local to Global; Regulating Markets; and Regulating Labour. The papers reflect a diversity of perspectives. Some explore issues in the context of specific platforms such as Airbnb or Uber; others consider the issues raised by the ‘sharing economy’ more broadly. A Table of Contents for the book is found below.

 

Law and the “Sharing Economy”: Regulating Online Market Platforms

Derek McKee, Finn Makela, Teresa Scassa, eds.

 

Table of contents

Introduction

Derek McKee, Finn Makela and Teresa Scassa

Technologies of regulation

Peer Platform Markets and Licensing Regimes

Derek McKee

The False Promise of the Sharing Economy

Harry Arthurs

The Fast to the Furious

Nofar Sheffi

Regulating technology

The Normative Ecology of Disruptive Technology

Vincent Gautrais

Information Law in the Platform Economy: Ownership, Control and Reuse of Platform Data

Teresa Scassa

The space of regulation: local to global

Urban Cowboy E-Capitalism Meets Dysfunctional Municipal Policy-Making: What the Uber Story Tells Us About Canadian Local Governance

Mariana Valverde

The Sharing Economy and Trade Agreements: The Challenge to Domestic Regulation

Michael Geist

Regulating markets

Should Licence Plate Owners be Compensated when Uber Comes to Town?

Eran Kaplinsky

Competition Law and Policy Issues in the Sharing Economy

Francesco Ducci

Regulating labour

The Legal Framework for Digital Platform Work: The French Experience

Marie-Cécile Escande-Varniol

Uber and the Unmaking and Remaking of Taxi Capitalisms: Technology, Law and Resistance in Historical Perspective

Eric Tucker

Making Sense of the Public Discourse on Airbnb and Labour: What About Labour Rights?

Sabrina Tremblay-Huet

 

 

 

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?

The following is a copy of remarks I made in an appearance before the Standing Senate Committee on Banking, Trade and Commerce, on November 21, 2018. The remarks are about proposed amendments to the Trade-marks Act found in the omnibus Bill C-86. I realize that not everyone is as interested in official marks as I am. To get a sense of what the fuss is all about, you can have a look at my posts about the overprotection of Olympic Marks, the dispute over the Spirit Bear mark, the struggle to register a trademark in the face of an wrongly granted and since abandoned official mark, Canada Post’s official marks for POSTAL CODE and CODE POSTAL and a previous private member’s bill to reform official marks.

Canada’s official marks regime has long been criticized by lawyers, academics and the Federal Court. In fact, it is the Federal Court that has, over the years, created some much needed boundaries for these “super marks”. The problems with official marks are well known, but they have largely been ignored by Parliament. It is therefore refreshing to see the proposed changes in ss. 215 and 216 of Bill C-86.

Sections 215 and 216 address only one of the problems with the official marks regime. Although it is an important one, it is worth noting that there is more that could be done. The goal of my remarks will be to identify what I see as two shortfalls of ss 215 and 216.

Official marks are a subcategory of prohibited marks, which may not be adopted, used or registered unless consent is provided. They are available to “public authorities”. A public authority need only ask the Registrar of Trademarks to give public notice of its adoption and use of an official mark for that mark to be protected. There are no limits to what can be adopted. There are no registration formalities, no examination or opposition proceedings. Until the very recent decision of the Federal Court in Quality Program Services Inc. v. Canada, it seemed nothing prevented a public authority from obtaining an official mark that was identical to or confusing with an already registered trademark. While Quality Program Services at least provides consequences for adopting a confusing official mark, it is currently under appeal and it is not certain that the decision will be upheld. This is another instance of the Federal Court trying to set boundaries for official marks that simply have not been set in the legislation.

Official marks are theoretically perpetual in duration. They remain on the register until they are either voluntarily withdrawn by the ‘owner’ (and owners rarely think to do this), or until a successful (and costly) action for judicial review results in one being struck from the Register. Until the Ontario Association of Architects decision in 2002 tightened up the meaning of ‘public authority’, official marks were handed out like Halloween candy, and many entities that were not ‘public authorities’ were able to obtain official marks. Many of these erroneously-issued official marks continue to exist today; in fact, the Register of Trademarks has become cluttered with official marks that are either invalid or no longer in use.

Sections 215 and 216 address at least part of this last problem. They provide an administrative process through which either the Registrar or any person prepared to pay the prescribed fee can have an official mark invalidated if the entity that obtained the mark “is not a public authority or no longer exists.” This is a good thing. I would, however, suggest one amendment to the proposed new s. 9(4). Where it is the case (as per the new s. 9(3)) that the entity that obtained the official mark was not a public authority or has ceased to exist, s. 9(4) allows the Registrar to give public notice that subparagraph (1)(n)(iii) “does not apply with respect to the badge, crest, emblem or mark”. As it is currently worded, this is permissive– the Registrar “may” give public notice of non-application. In my view, it should read:

(4) In the circumstances set out in subsection (3), the Registrar may, on his or her own initiative or shall, at the request of a person who pays a prescribed fee, give public notice that subparagraph (1)‍(n)‍(iii) does not apply with respect to the badge, crest, emblem or mark.

There is no reason why a person who has paid a fee to have established the invalidity of an official mark should not have the Registrar give public notice of this.

I would also suggest that the process for invalidating official marks should extend to those that have not been used within the preceding three years – in other words, something parallel to s. 45 of the Trade-marks Act which provides an administrative procedure to remove unused registered trademarks from the Register. There are hundreds of ‘public authorities’ at federal and provincial levels across Canada, and they adopt official marks for all sorts of programs and initiatives, many of which are relatively transient. There should be a means by which official marks can simply be cleared from the Register when they are no longer used. Thus, I would recommend adding new subsections 9(5) and (6) to the effect that:

(5) The Registrar may at any time – and, at the written request of a person who pays a prescribed fee, made after three years from the date that public notice was given of an official mark, shall, unless the Registrar sees good reason to the contrary – give notice to the public authority requiring it to furnish, within three months, an affidavit or a statutory declaration showing that the official mark was in use at any time during the three year period immediately preceding the date of the notice and, if not, the date when it was last so in use and the reason for the absence of such use since that date.

(6) Where, by reason of the evidence furnished to the Registrar or the failure to furnish any evidence, it appears to the Registrar that an official mark was not used at any time during the three year period immediately preceding the date of the notice and that the absence of use has not been due to special circumstances that excuse it, the Registrar shall give public notice that subparagraph (1)‍(n)‍(iii) does not apply with respect to the badge, crest, emblem or mark.

These are my comments on changes to the official marks regime that most closely relate to the amendments in Bill C-86. The regime has other deficiencies which I would be happy to discuss.

A Global News story about Statistics Canada’s collection of detailed financial data of a half million Canadians has understandably raised concerns about privacy and data security. It also raises interesting questions about how governments can or should meet their obligations to produce quality national statistics in an age of big data.

According to Andrew Russell’s follow-up story, Stats Canada plans to collect detailed customer information from Canada’s nine largest banks. The information sought includes financial information including account balances, transaction data, credit card and bill payments. It is unclear whether the collection has started.

As a national statistical agency, Statistics Canada is charged with the task of collecting and producing data that “ensures Canadians have the key information on Canada's economy, society and environment that they require to function effectively as citizens and decision makers.” Canadians are perhaps most familiar with providing census data to Statistics Canada, including more detailed data through the long form census. However, the agency’s data collection is not limited to the census.

Statistics Canada’s role is important, and the agency has considerable expertise in carrying out its mission and in protecting privacy in the data it collects. This is not to say, however, that Statistics Canada never makes mistakes and never experiences privacy breaches. One of the concerns, therefore, with this large-scale collection of frankly sensitive data is the increased risk of privacy breaches.

The controversial collection of detailed financial data finds its legislative basis in this provision of the Statistics Act:

13 A person having the custody or charge of any documents or records that are maintained in any department or in any municipal office, corporation, business or organization, from which information sought in respect of the objects of this Act can be obtained or that would aid in the completion or correction of that information, shall grant access thereto for those purposes to a person authorized by the Chief Statistician to obtain that information or aid in the completion or correction of that information. [My emphasis]

Essentially, it conveys enormous power on Stats Canada to request “documents or records” from third parties. Non-compliance with a request is an offence under s. 32 of the Act, which carries a penalty on conviction of a fine of up to $1000. A 2017 amendment to the legislation removed the possibility of imprisonment for this offence.

In case you were wondering whether Canada’s private sector data protection legislation offers any protection when it comes to companies sharing customer data with Statistics Canada, rest assured that it does not. Paragraph 7(3)(c.1) of the Personal Information Protection and Electronic Documents Act provides that an organization may disclose personal information without the knowledge or consent of an individual where the disclosure is:

(c.1) made to a government institution or part of a government institution that has made a request for the information, identified its lawful authority to obtain the information and indicated that

[. . .]

(iii) the disclosure is requested for the purpose of administering any law of Canada or a province

According to the Global News story, Statistics Canada notified the Office of the Privacy Commissioner about its data collection plan and obtained the Commissioner’s advice. In his recent Annual Report to Parliament the Commissioner reported on Statistic’s Canada’s growing practice of seeking private sector data:

We have consulted with Statistics Canada (StatCan) on a number of occasions over the past several years to discuss the privacy implications of its collection of administrative data – such as individuals’ mobile phone records, credit bureau reports, electricity bills, and so on. We spoke with the agency about this again in the past year, after a number of companies contacted us with concerns about StatCan requests for customer data.

The Commissioner suggested that Stats Canada might consider the collection of only data that has been deidentified at source rather than detailed personal information. He also recommended an ongoing assessment of the necessity and effectiveness of such programs.

The Commissioner also indicated that one of the problems with the controversial data collection by Statistics Canada is its lack of openness. He stated: “many Canadians might be surprised to learn the government is collecting their information in this way and for this purpose.” While part of this lack of transparency lies in the decision not to be more upfront about the data collection, part of it lies in the fact that the legislation itself – while capable of being read to permit this type of collection – clearly does not expressly contemplate it. Section 13 was drafted in a pre-digital, pre-big data era. It speaks of “documents or records”, and not “data”. While it is possible to interpret it so as to include massive quantities of data, the original drafters no doubt contemplated a collection activity on a much more modest scale. If Section 13 really does include the power to ask any organization to share its data with Stats Canada, then it has become potentially limitless in scope. At the time it was drafted, the limits were inherent in the analogue environment. There was only so much paper Stats Canada could ask for, and only so much paper it had the staff to process. In addition, there was only so much data that entities and organizations collected because they experienced the same limitations. The digital era means not only that there is a vast and increasing amount of detailed data collected by private sector organizations, but that this data can be transferred in large volumes with relative ease, and can be processed and analyzed with equal facility.

Statistics Canada is not the only national statistics organization to be using big data to supplement and enhance its data collection and generation. In some countries where statistical agencies struggle with a lack of human resources and funding, big data from the private sector offer opportunities to meet the data needs of their governments and economies. Statistical agencies everywhere recognize the potential of big data to produce more detailed, fine-grained and reliable data about many aspects of the economy. For example, the United Nations maintains a big data project inventory that catalogues experiments by national statistical agencies around the world with big data analytics. Remember the cancellation of the long form census by the Harper government? This was not a measure to protect Canadians’ privacy by collecting less information; it was motivated by a belief that better and more detailed data could be sought using other means – including reliance on private sector data.

It may well be that Statistics Canada needs the power to collect digital data to assist in data collection programs that serve national interests. However, the legislation that authorizes such collection must be up-to-date with our digital realities. Transparency requires an amendment to the legislation that would specifically enable the collection and use of digital and big data from the private sector tor statistical purposes. Debate over the scope and wording of such a provision would give both the public and the potential third party data sources an opportunity to identify their concerns. It would also permit the shaping of limits and conditions that are specific to the nature and risks of this form of data collection.

Late in the afternoon of Monday, October 15, 2018, Sidewalk Labs released a densely-packed slide-deck which outlined its new and emerging data governance plan for the Sidewalk Toronto smart city development. The plan was discussed by Waterfront Toronto’s Digital Strategy Advisory Panel at their meeting on Thursday, October 18. I am a member of that panel, and this post elaborates upon the comments I made at that meeting.

Sidewalk Labs’ new data governance proposal builds upon the Responsible Data Use Policy Framework (RDUPF) document which had been released by Sidewalk Labs in May 2018. It is, however, far more than an evolution of that document – it is a different approach reflecting a different smart city concept. It is so different that Ann Cavoukian, advisor to Sidewalk Labs on privacy issues, resigned on October 19. The RDUPF had made privacy by design its core focus and promised the anonymization of all sensor data. Cavoukian cited the fact that the new data governance framework contemplated that not all personal information would be deidentified as a reason for her resignation.

Neither privacy by design nor data anonymization are privacy panaceas, and the RDUPF document had a number of flaws. One of them was that by championing deidentification of personal information as the key to responsible data use, it very clearly only addressed privacy concerns relating to a subset of the data that would inevitably be collected in the proposed smart city. In addition, by focusing on privacy by design, it did little to address the many other data governance issues the project faced.

The new proposal embraces a broader concept of data governance. It is cognizant of privacy issues but also considers issues of data control, access, reuse, and localization. In approaching data governance, Sidewalk is also proposing using a ‘civic data trust’ as a governance model. Sidewalk has made it clear that this is a work in progress and that it is open to feedback and comment. It received some at the DSAP meeting on Thursday, and more is sure to come.

My comments at the DSAP focused on two broad issues. The first was data and the second was governance. I prefaced my discussion of these by warning that in my view it is a mistake to talk about data governance using either of the Sidewalk Labs documents as a departure point. This is because these documents embed assumptions that need to be examined rather than simply accepted. They propose a different starting point for the data governance conversation than I think is appropriate, and as a result they unduly shape and frame that discussion.

Data

Both the RDUPF and the current data governance proposal discuss how the data collected by the Sidewalk Toronto development will be governed. However, neither document actually presents a clear picture of what those data are. Instead, both documents discuss a subset of data. The RDUPF discussed only depersonalized data collected by sensors. The second discussed only what it defines as “urban data”:

Urban Data is data collected in a physical space in the city, which includes:

● Public spaces, such as streets, squares, plazas, parks, and open spaces

● Private spaces accessible to the public, such as building lobbies, courtyards, ground-floor markets, and retail stores

● Private spaces not controlled by those who occupy them (e.g. apartment tenants)

This is very clearly only a subset of smart cities data. (It is also a subset that raises a host of questions – but those will have to wait for another blog post.)

In my view, any discussion of data governance in the Sidewalk Toronto development should start with a mapping out of the different types of data that will be collected, by whom, for what purposes, and in what form. It is understood that this data landscape may change over time, but at least a mapping exercise may reveal the different categories of data, the issues they raise, and the different governance mechanisms that may be appropriate depending on the category. By focusing on deidentified sensor data, for example, the RDUPF did not address personal information collected in relation to the consumption of many services that will require identification – e.g., for billing or metering purposes. In the proposed development, what types of services will require individuals to identify themselves? Who will control such data? How will it be secured? What will policies be with respect to disclosure to law enforcement without a warrant? What transparency measures will be in place? Will service consumption data also be deidentified and made available for research? In what circumstances? I offer this as an example of a different category of data that still requires governance, and that still needs to be discussed in the context of a smart cities development. This type of data would also fall outside the category of “urban data” in the second governance plan, making that plan only a piece of the overall data governance required, as there are many other categories of data that are not captured by “urban data”. The first step in a data governance must be for all involved to understand what data is being collected, how, why, and by whom.

The importance of this is also made evident by the fact that between the RDUPF and the new governance plan, the very concept of the Sidewalk Toronto smart city seems to have changed. The RDUPF envisioned a city in which sensors were installed by Sidewalk and Sidewalk was committing to the anonymization of any collected personal information. In the new version, the model seems to be of the smart city as a technology platform on which any number of developers will be invited to build. As a result, the data governance model proposes an oversight body to provide approval for new data collection in public spaces, and to play some role in the sharing of the collected data if appropriate. This is partly behind the resignation of Ann Cavoukian. She objected to the fact that this model accepts that some new applications might require the collection of personal information and so deidentification could not be an upfront promise for all data collected.

The technology-platform model seems responsive to concerns that the smart city would effectively be subsumed by a single corporation. It allows other developers to build on the platform – and by extension to collect and process data. Yet from a governance perspective this is much messier. A single corporation can make bold commitments with respect to its own practices; it may be difficult or inappropriate to impose these on others. It also makes it much more difficult to predict what data will be collected and for what purposes. This does not mean that the data mapping exercise is not worthwhile – many kinds and categories of data are already foreseeable and mapping data can help to understand different governance needs. In fact, it is likely that a project this complex will require multiple data governance models.

Governance

The second point I tried to make in my 5 minutes at the Thursday meeting was about data governance. The new data governance plan raises more questions than it answers. One glaring issue seems to be the place for our already existing data governance frameworks. These include municipal and provincial Freedom of Information and Protection of Privacy Acts and PIPEDA. They may also include the City of Toronto’s open data policies and platforms. There are very real questions to be answered about which smart city data will be private sector data and which will be considered to be under the custody or control of a provincial or municipal government. Government has existing legal obligations about the management of data that are under its custody or control, and these obligations include the protection of privacy as well as transparency. A government that decides to implement a new data collection program (traffic cameras, GPS trackers on municipal vehicles, etc.) would be the custodian of this data, and it would be subject to relevant provincial laws. The role of Sidewalk Labs in this development challenges, at a very fundamental level, the understanding of who is ultimately responsible for the collection and governance of data about cities, their services and infrastructure. Open government data programs invite the private sector to innovate using public data. But what is being envisaged in this proposal seems to be a privatization of the collection of urban data – with some sort of ‘trust’ model put in place to soften the reality of that privatization.

The ‘civic data trust’ proposed by Sidewalk Labs is meant to be an innovation in data governance, and I am certainly not opposed to the development of innovative data governance solutions. However, the use of the word “trust” in this context feels wrong, since the model proposed is not a data trust in any real sense of the word. This view seems to be shared by civic data trust advocate Sean MacDonald in an article written in response to the proposal. It is also made clear in this post by the Open Data Institute which attempts to define the concept of a civic data trust. In fact, it is hard to imagine such an entity being created and structured without significant government involvement. This perhaps is at the core of the problem with the proposal – and at the root of some of the pushback the Sidewalk Toronto project has been experiencing. Sidewalk Labs is a corporation – an American one at that – and it is trying to develop a framework to govern vast amounts of data collected about every aspect of city life in a proposed development. But smart cities are still cities, and cities are public institutions created and structured by provincial legislation and with democratically elected councils. If data is to be collected about the city and its residents, it is important to ask why government is not, in fact, much more deeply implicated in any development of both the framework for deciding who gets to use city infrastructure and spaces for data collection, and what data governance model is appropriate for smart cities data.

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.

 

Wednesday, 12 September 2018 13:44

Smart cities data - governance challenges

This post gives a brief overview of a talk I am giving September 12, 2018, on a panel hosted by the Centre for Law Technology and Society at uOttawa. The panel title is ‘Smart and the City’

 

This post (and my presentation) explores the concept of the ‘smart’ city and lays the groundwork for a discussion of governance by exploring the different types of data collected in so-called smart cities.

Although the term ‘smart city’ is often bandied about, there is no common understanding of what it means. Anthony Townsend has defined smart cities as “places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems.” (A. Townsend, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. (New York: W.W. Norton & Co., 2013), at p. 15). This definition emphasizes the embedding of information technologies within cities with the goal of solving a broad range of urban problems. Still, there is uncertainty as to which cities are ‘smart’ or at what point a city passes the invisible ‘smart’ threshold.

Embedded technologies are multiple and ever-evolving, and many are already in place in the cities in which we live. Technologies that have become relatively commonplace include smart transit cards, GPS systems on public vehicles (e.g.: buses, snowplows, emergency vehicles, etc.), smart metering for utilities, and surveillance and traffic cameras. Many of the technologies just identified collect data; smart technologies also process data using complex algorithms to generate analytics that can be used in problem identification and problem solving. Predictive policing is an example of a technology that generates information based on input data and complex algorithms.

While it is possible for a smart city to be built from the ground up, this is not the most common type of smart city. Instead, most cities become ‘smarter’ by increments, as governments adopt one technology after another to address particular needs and issues. While both from-the-ground-up and incremental smart cities raise important governance issues, it is the from-the-ground-up projects (such as Sidewalk Toronto) that get the most public attention. With incremental smart cities, the piecemeal adoption of technologies often occurs quietly, without notice, and thus potentially without proper attention being paid to important overarching governance issues such as data ownership and control, privacy, transparency, and security.

Canada has seen two major smart cities initiatives launched in the last year. These are the federal government’s Smart Cities Challenge – a contest between municipalities to fund the development of smart cities projects – and the Sidewalk Toronto initiative to create a from-the-ground-up smart development in Toronto’s Quayside area. Although Canadian cities have been becoming ‘smart’ by increments for some time now, these two high-profile initiatives have sparked discussion of the public policy issues, bringing important governance issues to the forefront.

These initiatives, like many others, have largely been conceived of and presented to the public as technology, infrastructure, and economic development projects. Rather than acknowledging up-front the need for governance innovation to accompany the emerging technologies, governance tends to get lost in the hype. Yet it is crucial. Smart cities feed off data, and residents are primary sources. Much of the data collected in smart cities is personal information, raising obvious privacy issues. Issues of ownership and control over smart cities data (whether personal or non-personal) are also important. They are relevant to who gets to access and use the data, for what purposes, and for whose profit. The public outcry over the Sidewalk Toronto project (examples here, here and here) clearly demonstrates that cities are not just tech laboratories; they are the places where we try to live decent and meaningful lives.

The governance issues facing so-called smart cities are complex. They may be difficult to disentangle from the prevailing ‘innovate or perish’ discourse. They are also rooted in technologies that are rapidly evolving. Existing laws and legal and policy frameworks may not be fully adequate to address smart cities challenges. This means that the governance issues raised by smart cities may require a rethinking of the existing law and policy infrastructure almost at pace with the emerging and evolving technologies.

The complexity of the governance challenges may be better understood when one considers the kind of data collected in smart cities. The narrower the categories of data, the more manageable data governance in the smart city will seem. However, the nature of information technologies, including the types and locations of sensors, and the fact that many smart cities are built incrementally, require a broad view of the types of data at play in smart cities. Here are some kinds of data collected and used in smart cities:

 

· traditional municipal government data (e.g. data about registrants or applicants for public housing or permits; data about water consumption, infrastructure, waste disposal, etc.)

· data collected by public authorities on behalf of governments (eg: electrical consumption data; transit data, etc.)

· sensor data (e.g.: data from embedded sensors such as traffic cameras, GPS devices, environmental sensors, smart meters)

· data sourced from private sector companies (e.g.: data about routes driven or cycled from companies such as Waze or Strava; social media data, etc.)

· data from individuals as sensors (e.g. data collected about the movements of individuals based on signals from their cell phones; data collected by citizen scientists; crowd-sourced data, etc.)

· data that is the product of analytics (e.g. predictive data, profiles, etc.)

 

Public sector access to information and protection of privacy legislation provides some sort of framework for transparency and privacy when it comes to public sector data, but clearly such legislation is not well adapted to the diversity of smart cities data. While some data will be clearly owned and controlled by the municipality, other data will not be. Further the increasingly complex relationship between public and private sectors around input data and data analytics means that there will be a growing number of conflicts between rights of access and transparency on the one hand, and the protection of confidential commercial information on the other.

Given that few ‘smart’ cities will be built from the ground up (with the potential for integrated data governance mechanisms), the complexity and diversity of smart cities data and technologies creates a stark challenge for developing appropriate data governance.

 

(Sorry to leave a cliff hanger – I have some forthcoming work on smart cities data governance which I hope will be published by the end of this year. Stay tuned!)

 

 

A recent Federal Court decision highlights the risks to privacy that could flow from unrestrained access by government to data in the hands of private sector companies. It also demonstrates the importance of judicial oversight in ensuring transparency and the protection of privacy.

The Income Tax Act (ITA) gives the Minister of National Revenue (MNR) the power to seek information held by third parties where it is relevant to the administration of the income tax regime. However, where the information sought is about unnamed persons, the law requires judicial oversight. A judge of the Federal Court must review and approve the information “requirement”. Just such a matter arose in Canada (Minister of National Revenue) v. Hydro-Québec. The MNR sought information from Hydro-Québec, the province’s electrical utility, about a large number of its business customers. Only a few classes of customers, such as heavy industries that consumed very large amounts of electricity were excluded. Hydro itself did not object to the request and was prepared to fulfil it if ordered to do so by the Federal Court. The request was considered by Justice Roy who noted that because the information was about unnamed and therefore unrepresented persons, it was “up to the Court to consider their interests.” (at para 5)

Under s. 231.2(3) of the ITA, before ordering the disclosure of information about unnamed persons, a must be satisfied that:

(a) the person or group is ascertainable; and

(b) the requirement is made to verify compliance by the person or persons with any duty or obligation under this Act.

The information sought from Hydro in digital format included customer names, business numbers, full billing addresses, addresses of each place where electricity is consumed, telephone numbers associated with the account, billing start dates, and, if applicable, end dates, and any late payment notices sent to the customer.

Justice Roy noted that no information had been provided to the court to indicate whether the MNR had any suspicions about the tax compliance of business customers of Hydro-Quebec. Nor was there much detail about what the MNR planned to do with the information. The documents provided by the MNR, as summarized by the Court, stated that the MNR was “looking to identify those who seem to be carrying on a business but failed to file all the required income tax returns.” (at para 14) However, Justice Roy noted that there were clearly also plans to share the information with other groups at the Canada Revenue Agency (CRA). These groups would use the information to determine “whether the individuals and companies complied with their obligations under the ITA and the ETA”. (at para 14)

Justice Roy was sympathetic to the need of government to have powerful means of enforcing tax laws that depend upon self-reporting of income. However, he found that what the MNR was attempting to do under s. 231.2 went too far. He ruled that the words used in that provision had to be interpreted in light of “the right of everyone to be left alone by the state”. (at para 28) He observed that it is clear from the wording of the Act that “Parliament wanted to limit the scope of the Minister’s powers, extensive as they are.” (at para 68)

Justice Roy carefully reviewed past jurisprudence interpreting s. 231.2(3). He noted that the section has always received a strict interpretation by judges. In past cases where orders had been issued, the groups of unnamed persons about whom information was sought were clearly ascertainable, and the information sought was ‘directly related to these taxpayers’ tax status because it is financial in nature.” (at para 63) In the present case, he found that the group was not ascertainable, and the information sought “has nothing to do with tax-status.” (at para 63)

In his view, the aim of the request was to determine the identity of business customers of Hydro-Québec. The information was not sought in relation to a good faith audit, and with a proper factual basis. Because it was a fishing expedition meant to determine who might suitably be audited, the group of individuals identified by Hydro-Québec could not be considered “ascertainable”, as was required by the law. Justice Roy noted that no information was provided to demonstrate what “business customer” meant. He observed that “the Minister would render the concept of “ascertainable group” meaningless if, in the context of the ITA, she may claim that any group is an ascertainable group.” (at para 78) He opined that giving such broad meaning to “ascertainable” could be an abuse that would lead to violations of privacy by the state.

Justice Roy also found that the second condition of s. 231.2(3) was not met. Section 231.2(3)(b) required that the information be sought in order “to verify compliance by the person or persons in the group with any duty or obligation under this Act.” He observed that the MNR was seeking an interpretation of this provision that would amount to: “Any information the Minister may consider directly or indirectly useful”. (at para 80) Justice Roy favoured a much more restrictive interpretation, limiting it to information that could “shed light on compliance with the Act.” (at para 80) He found that “the knowledge of who has a business account with Hydro-Québec does not meet the requirement of a more direct connection between the information and documents and compliance with the Act.” (at para 80)

The MNR had argued that if the two conditions of s. 231.2(3) were met, then a judge was required to issue the authorization. Because Justice Roy found the two conditions were not met, the argument was moot. Nevertheless, he noted that even if he had found the conditions to be met, he would still have had the discretion to deny the authorization if to grant it would harm the public interest. In this case, there would be a considerable invasion of privacy “given the number of people indiscriminately included in the requirement for which authorization of the Court is being sought. (at para 88) He also found that the fact that digital data was sought increased the general risk of harm. He observed that “the applicant chose not to restrict the use she could make of the large quantity of information she received” (at para 91) and that it was clearly planned that the information would be shared within the CRA. Justice Roy concluded that even if he erred in his interpretation of the criteria in s. 231.2(3), and these criteria had to be given a broad meaning, he would still not have granted the authorization on the basis that “judicial intervention is required to prevent such an invasion of the privacy of many people in Quebec.” (at para 96) Such intervention would particularly be required where “the fishing expedition is of unprecedented magnitude and the information being sought is far from serving to verify compliance with the Act.” (at para 96)

This is a strong decision which clearly protects the public interest. It serves to highlight the privacy risks in an era where both private and public sectors amass vast quantities of personal information in digital form. Although the ITA provides a framework to ensure judicial oversight in order to limit potential abuses, there are still far too many other contexts where information flows freely and where there may be insufficient oversight, transparency or accountability.

 

The report of an investigator for Ontario’s Office of the Information and Privacy Commissioner (OIPC) into personal information contained within a published tribunal decision adds to the debate around how to balance individual privacy with the open courts principle. In this case (Privacy Complaint No. PC17-9), the respondent is the Ontario Human Rights Tribunal (OHRT), established under the Ontario Human Rights Code. The OHRT often hears matters involving highly sensitive personal information. Where an adjudicator considers it relevant to their decision, they may include this information in their written reasons. Although a party may request that the decision be anonymized to protect their personal information, OHRT adjudicators have been sparing in granting requests for anonymization, citing the importance of the open courts principle.

The OIPC investigated after receiving a complaint about the reporting of sensitive personal information in an OHRT decision. The interesting twist in this case was that the personal information at issue was not that of the person who had complained to the OHRT (the ‘OHRT complainant’), and whose complaint had led to the tribunal hearing. Rather, it was the personal information of the OHRT complainant’s sister and mother. The complaint to the OIPC was made by the sister (the ‘OIPC complainant’) on behalf of herself and her mother. Although the sister’s and mother’s names were not used in the OHRT decision, they argued that they were easily identifiable since they lived in a small town and shared a distinctive surname with the OHRA complainant. The OIPC investigator agreed. She noted that the information at the heart of the complaint consisted of “the applicant’s name, the applicant’s mother’s age, the mother’s primary language, the number of medications the applicant’s mother was taking, the reason for the medication, the state of the mother’s memory and the city the complainant resides in.” (at para 19). The investigator found that although the names of the OIPC complainant and her mother were not mentioned, their relationship to the OHRT complainant was. She observed: “Given that the applicant’s name is available, the uniqueness of the names and the size of the community, it is reasonable to assume that someone reading the decision would be able to identify her mother and sister and connect the information in the decision to them.” (at para 26)

Since the OHRT is a public body, and the information at issue was personal information, the OIPC complainant argued that the OHRT had breached the province’s Freedom of Information and Protection of Privacy Act (FIPPA) by publishing this information in its decision. For its part, the OHRT argued that the information was exempted from the application of FIPPA under s. 37 of that Act because it was “personal information that is maintained for the purpose of creating a record that is available to the general public”. It argued that it has an adjudicative mandate under the Human Rights Code and that the Statutory Powers Procedures Act (SPPA) permits it to determine its own practices and procedures. Although neither the OHRC nor the SPPA address the publication of decisions, the OHRT had decided that as a matter of practice, its decisions would be published, including on the public legal information website CanLII. The OHRT also argued that its proceedings were subject to the open courts principle. This argument was supported by the recent Ontario Superior Court decision (discussed here) which confirmed that the open courts principle applied to the decisions of statutory tribunals. The investigator agreed with the OHRT. She observed that “[o]penness at tribunals tends to improve the quality of testimony and for that reason is conducive to the pursuit of truth in adjudicative proceedings.” (at para 56). She noted as well that the other elements of the open courts principle, including “oversight of decision-makers, the integrity of the administration of justice, and the educational and democracy-enhancing features of open courts” (at para 57) were all linked to the Charter value of freedom of expression. She accepted that the publication of reasons for decision was part of the openness principle, and concluded that: “The publication of decisions is an aspect of the Tribunal’s control over its own process and the information that is included in the Tribunal’s decisions is within the adjudicator’s discretion in providing reasons for those decisions.” (at para 65) She noted that many public values were served by the publication of the Tribunal’s decisions: “The publication of its decisions supports public confidence in the justice system, serves an educational purpose, promotes accountability by the Tribunal for its decision-making, and ensures that the public has the information necessary to exercise the Charter right to freedom of expression.” (at para 66) As a result, she concluded that s. 37 of FIPPA excluded the published decisions from the application of the privacy provisions of the Act.

This seems like an appropriate conclusion given the legislative framework. However, it does raise two general points of importance with respect to how the OHRT deals with personal information in its decisions. First, human rights legislation exists in an attempt to provide recourse and redress for those who experience discrimination in contexts which closely affect their lives, such as employment, accommodation, and the receipt of services. The prohibited grounds of discrimination are ones which touch on highly personal and intimate aspects of peoples’ lives, relating to sexual identity, national origin, religion, and mental or physical disability, to provide but a few examples. Personal information of this kind is generally considered highly sensitive. The spectre that it will be published – online – alongside an individual’s name, might be daunting enough to prevent some from seeking redress under the legislation at all. For example, fear that the online publication of one’s mental health information might make it difficult to find future employment could prevent a person from filing of a complaint of discrimination. This would seem to subvert the purpose of human rights legislation. And yet, human rights tribunals have been reticent in granting requests for anonymization, citing the open courts principle.

Secondly, this case raises the further issue of how the sensitive personal information of third parties – who were neither witness before the tribunal or complainants to the OHRC – ended up in a decision published online, and for which the Tribunal had refused an anonymization request. The OIPC investigator concluded her report by recommending that the OHRT “continue to apply data minimization principles in the drafting of its decisions and include only personal information necessary to achieve the purpose of those decisions.” (at para 72) In the absence of clear directives for dealing with the online publication of personal information in court or tribunal decisions, and appropriate training for adjudicators, this gentle reminder seems to be the best that complainants can hope for. It is not good enough. One need only recall the complaints to the Office of the Privacy Commissioner of Canada about the offshore website that had scraped decisions from CanLII and court websites in order to make them available in fully indexable form over the internet, to realize that we have important unresolved issues about how personal information is published and disseminated in court and tribunal decisions in Canada.

Some years ago, a reporter from the Toronto Star filed an access to information request to obtain the names of the top 100 physician billers to Ontario’s Health Insurance Program (OHIP). She also sought the amounts billed, and the physicians’ fields of specialization. The information was in the hands of the Ministry of Health and Long-Term Care, and the request was made under Ontario’s Freedom of Information and Protection of Privacy Act (FIPPA). The Ministry refused to disclose the records on the basis that they constituted the physicians’ personal information. An adjudicator with the Ontario Information and Privacy Commissioner’s Office disagreed, and ordered disclosure. An appeal by the Ontario Medical Association (OMA) to the Ontario Divisional Court was unsuccessful (discussed here). On August 3, 2018, the Ontario Court of Appeal dismissed the OMA’s further appeal of that decision.

The relatively brief and unanimous Court of Appeal decision made short work of the OMA’s arguments. The Court found that the adjudicator’s determination that the information was not personal information was reasonable. FIPPA specifically excludes from the definition of personal information “the name, title, contact information or designation of an individual that identifies the individual in a business, professional or official capacity”. The OMA had argued that the disclosure of the names in conjunction with the billing information meant that the disclosure would include personal information that “describes an individual’s finances, income, assets, liabilities…”. FIPPA provides in s. 21(3) that the disclosure of personal information is presumptively an invasion of privacy when it falls within this category. However, the Court found that the billing information constituted “the affected physicians’ gross revenue before allowable business expenses such as office, personnel, lab equipment, facility and hospital expenses.” (at para 25) The Court agreed with the adjudicator that the gross billing information did not reveal the actual income of the physicians. It stated: “where, as here, an individual’s gross professional or business income is not a reliable indicator of the individual’s actual personal finances or income, it is reasonable to conclude not only that the billing information is not personal information as per s. 2(1), but also that it does not describe “an individual’s finances [or] income”, for the purpose of s. 21(3)(f).” (at para 26)

The OMA had resisted disclosure because the billing information might give the public, who might not understand the costs associated with running a medical practice, a distorted idea of the physicians’ personal finances. Ironically, the Court found that the differences between billing information and actual income were so different that it did not amount to personal information. The OMA had objected to what it considered to be the OIPC’s changed position on the nature of this type of information; in the past, the OIPC had accepted that this information was personal information and had not ordered disclosure. The Ontario Court of Appeal observed that the adjudicator was not bound to follow precedent; it also observed that there were differences of opinion in past OIPC decisions on this issue, and no clear precedent existed in any event.

The decision is an important one for access to information. A publicly funded health care system consumes substantial resources, and there is a public interest in understanding, analyzing, critiquing and discussing how those resources are spent. The OMA was understandably concerned that public discussions not centre on particular individuals. However, governments have been moving towards greater transparency when it comes to monies paid to specific individuals and businesses, whether they are contractors or even public servants. As the Court of Appeal noted, FIPPA balances access to information with the protection of personal privacy. The public interest clearly prevailed in this instance.

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