Teresa Scassa - Blog

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

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

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

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

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

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

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

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

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

creating economic benefits

increasing public trust and confidence

better, smarter government

other

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

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

Yes

No

I’m not sure

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

Yes

No

I’m not sure

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

Yes

No

I’m not sure

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

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.