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

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

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

 

 

Principles for Ethical Use [Alpha]

Principles for Ethical Use [Beta]

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

 

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

 

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

 

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

 

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

 

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

1. Transparent and Explainable

 

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

 

 

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

 

Why it Matters

 

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

 

1. Transparent and explainable

 

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

 

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

 

Why it matters

 

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

 

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

 

For more on this, please consult the Transparency Guidelines.

 

2. Good and Fair

 

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

 

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

 

Why it matters

 

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

 

2. Good and fair

 

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

 

Why it matters

 

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

 

3. Safe

 

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

 

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

 


Why it matters

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

3. Safe

 

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

 

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

 

Why it matters

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

 

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

 

4. Accountable and Responsible

 

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

 

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

 

Why it matters

 

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

 

4. Accountable and responsible

 

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

 

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

 

Why it matters

 

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

 

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

 

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

 

5. Human Centric

 

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

 

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

 

Why it matters

 

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

 

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

 

5. Human centric

 

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

 

Why it matters

 

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

 

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

 

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

 

6. Sensible and Appropriate

 

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

 

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

 

Why it matters

 

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

 

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

6. Sensible and appropriate

 

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

 

Why it matters

 

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

 

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

 

Published in Privacy
Thursday, 07 February 2019 08:09

Ontario Launches Data Strategy Consultation

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

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

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

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

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

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

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

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

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

creating economic benefits

increasing public trust and confidence

better, smarter government

other

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

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

Yes

No

I’m not sure

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

Yes

No

I’m not sure

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

Yes

No

I’m not sure

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

Published in Privacy

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

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

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

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

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

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

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

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

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

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

 

Published in Copyright Law

Canadian Trademark Law

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Canadian Trademark Law 2d Edition

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Electronic Commerce and Internet Law in Canada, 2nd Edition

Published in 2012 by CCH Canadian Ltd.

Electronic Commerce and Internet Law in Canada

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Intellectual Property for the 21st Century

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