accountability

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Colliding Concepts and an Immigration Case Study: Lessons on Accountability for Canadian Administrative Law from Computer Systems [Op-Ed 1 for Law 432.D Course]

I wrote this Op-Ed for my Law 432.D course titled ‘Accountable Computer Systems.’ This blog will likely be posted on the course website but as I am presenting on a few topics related, I wanted it to be available to the general public in advance. I do note that after writing this blog, my more in-depth literature review uncovered many more administrative lawyers talking about accountability. However, I still believe we need to properly define accountability and can take lessons from Joshua Kroll’s work to do so.

 

Introduction

Canadian administrative law, through judicial review, examines whether decisions made by Government decision-makers (e.g. government officials, tribunals, and regulators) are reasonable, fair, and lawful.[i]

Administrative law governs the Federal Court’s review of whether an Officer has acted in a reasonable[ii] or procedurally fair[iii] way, for example in the context of Canadian immigration and citizenship law, where an Officer has decided to deny a Jamaican mother’s permanent residence application on humanitarian and compassionate grounds[iv] or strip Canadian citizenship away from a Canadian-born to Russian foreign intelligence operatives charged with espionage in the United States.[v]

Through judicial review and subsequent appellate Court processes, the term accountability has yet to be meaningfully engaged with in Canadian administrative case law.[vi] On the contrary, in computer science accountability is quick becoming a central organizing principle and governance mechanism.[vii] Technical and computer science specialists are designing technological tools based on accountability principles that justify its use and perceived sociolegal impacts.

Accountability will need to be better interrogated within the Canadian administrative law context, especially as Government bodies increasingly render decisions utilizing computer systems (such as AI-driven decision-making systems) [viii] that are becoming subject to judicial review.[ix]

An example of this is the growing litigation around Immigration, Refugees and Citizenship Canada’s (“IRCC”) use of decision-making systems utilizing machine-learning and advanced analytics.[x]

Legal scholarship is just starting to scratch the surface of exploring administrative and judicial accountability and has done so largely as a reaction to AI systems challenging traditional human decision-making processes. In the Canadian administrative law literature I reviewed, the discussion of accountability has not involved defining the term beyond stating it is a desirable system aim.[xi]

So, how will Canadian courts perform judicial review and engage with a principle (accountability) that it hardly knows?

There are a few takeaways from Joshua Kroll’s 2020 article, “Accountability in Computer Systems” that might be good starting points for this collaboration and conversation.

 

Defining Accountability – and the Need to Broaden Judicial Review’s Considerations

Kroll defines “accountability” as a “a relationship that involves reporting information to that entity and in exchange receiving praise, disapproval, or consequences when appropriate.”[xii]

Kroll’s definition is important as it goes beyond thinking of accountability only as a check-and-balance oversight and review system,[xiii] but also one that requires mutual reporting in a variety of positive and negative situations. His definition embraces, rather than sidesteps, the role of normative standards and moral responsibility.[xiv]

This contrasts with administrative judicial review, a process that is usually only engaged when an individual or party is subject to a negative Government decision (often a refusal or denial of a benefit or service, or the finding of wrongdoing against an individual).[xv]

As a general principle that is subject to a few exceptions, judicial review limits the Court’s examination to the ‘application’ record that was before the final human officer when rendering their negative decision.[xvi] Therefore, it is a barrier to utilize judicial review to seek clarity from the Government about the underlying data, triaging systems, and biases that may form the context for the record itself.

I argue that Kroll’s definition of accountability provides room for this missing context and extends accountability to the reporting the experiences of groups or individuals who receive the positive benefits of Government decisions when others do not. The Government currently holds this information as private institutional knowledge, with fear that broader disclosure could lead to scrutiny that might expose fault-lines such as discrimination and Charter[xvii] breaches/non-compliance.[xviii]

Consequentially, I do not see accountability’s language fitting perfectly into our currently existing administrative law context, judicial review processes, and legal tests. Indeed, even the process of engaging with accountability’s definition in law and tools for implementation will challenge the starting point of judicial review’s deference and culture of reasons-based justification[xix] as being sufficient to hold Government to account.

 

Rethinking Transparency in Canadian Administrative Law

Transparency is a cornerstone concept in Canadian administrative law. Like accountability, this term is also not well-defined in operation, beyond the often-repeated phrase of a reasonable decision needing to be “justified, intelligent, and transparent.”[xx] Kroll challenges the equivalency of transparency with accountability. He defines transparency as “the concept that systems and processes should be accessible to those affected either through an understanding of their function, through input into their structure, or both.”[xxi] Kroll argues that transparency is a possible vehicle or instrument for achieving accountability but also one that can be both insufficient and undesirable,[xxii] especially where it can still lead to illegitimate participants or lead actors to alter their behaviour to violate an operative norm.[xxiii]

The shortcomings of transparency as a reviewing criterion in Canadian administrative law are becoming apparent in IRCC’s use of automated decision-making (“ADM”) systems. Judicial reviews to the Federal Court are asking judges to consider the reasonableness, and by extension transparency of decisions made by systems that are non-transparent – such as security screening automation[xxiv] and advanced analytics-based immigration application triaging tools.[xxv]

Consequently, IRCC and the Federal Court have instead defended and deconstructed pro forma template decisions generated by computer systems[xxvi] while ignoring the role of concepts such as bias, itself a concept under-explored and under-theorized in administrative law.[xxvii] Meanwhile, IRCC has denied applicants and Courts access to mechanisms of accountability such as audit trails and the results of the technical and equity experts who are required to review these systems for gender and equity-based bias considerations.[xxviii]

One therefore must ask – even if full technical system transparency were available, would it be desirable for Government decision-makers to be transparent about their ADM systems,[xxix] particularly with outstanding fears of individuals gaming the system,[xxx] or worse yet – perceived external threats to infrastructure or national security in certain applications.[xxxi] Where Baker viscerally exposed an Officer’s discrimination and racism in transparent written text, ADM systems threaten to erase the words from the page and provide only a non-transparent result.

 

Accountability as Destabilizing Canadian Administrative Law

Adding the language of accountability will be destabilizing for administrative judicial review.

Courts often recant in Federal Court cases that it is “not the role of the Court to make its own determinations of fact, to substitute its view of the evidence or the appropriate outcome, or to reweigh the evidence.”[xxxii] The seeking of accountability may ask Courts to go behind and beyond an administrative decision, to function in ways and to ask questions they may not feel comfortable asking, possibly out of fear of overstepping the legislation’s intent.

A liberal conception of the law seeks and gravitates towards taxonomies, neat boxes, clean definitions, and coherent rules for consistency.[xxxiii] On the contrary, accountability acknowledges the existence of essentially contested concepts[xxxiv] and the layers of interpretation needed to parse out various accountability types,[xxxv] and consensus-building. Adding accountability to administrative law will inevitably make law-making become more complex. It may also suggest that judicial review may not be as effective as an ex-ante tool,[xxxvi] and that a more robust, frontline, regulatory regime may be needed for ADMs.

 

Conclusion: The Need for Administrative Law to Develop Accountability Airbags

The use of computer systems to render administrative decisions, more specifically the use of AI which Kroll highlights as engaging many types of accountability,[xxxvii] puts accountability and Canadian administrative law on an inevitable collision course. Much like the design of airbags for a vehicle, there needs to be both technical/legal expertise and public education/awareness needed of both what accountability is, and how it works in practice.

It is also becoming clearer that those impacted and engaging legal systems want the same answerability that Kroll speaks to for computer systems, such as ADMs used in Canadian immigration.[xxxviii] As such, multi-disciplinary experts will need to examine computer science concepts and accountable AI terminology such as explainability[xxxix] or interpretability[xl] alongside their administrative law conceptual counterparts, such as intelligibility[xli] and justification.[xlii]

As this op-ed suggests, there are already points of contention, (but also likely underexplored synergies), around the definition of accountability, the role of transparency, and whether the normative or multi-faceted considerations of computer systems are even desirable in Canadian administrative law.

 

References

[i] Government of Canada, “Definitions” in Canada’s System of Justice. Last Modified: 01 September 2021. Accessible online <https://www.justice.gc.ca/eng/csj-sjc/ccs-ajc/06.html> See also: Legal Aid Ontario, “Judicial Review” (undated). Accessible online: <https://www.legalaid.on.ca/faq/judicial-review/>

[ii] The Supreme Court of Canada in Canada (Minister of Citizenship and Immigration) v. Vavilov, 2019 SCC 65 (CanLII), [2019] 4 SCR 653, <https://canlii.ca/t/j46kb> [“Vavilov”] set out the following about reasonableness review:

[15] In conducting a reasonableness review, a court must consider the outcome of the administrative decision in light of its underlying rationale in order to ensure that the decision as a whole is transparent, intelligible and justified. What distinguishes reasonableness review from correctness review is that the court conducting a reasonableness review must focus on the decision the administrative decision maker actually made, including the justification offered for it, and not on the conclusion the court itself would have reached in the administrative decision maker’s place.

[iii]The question for the Court to determine is whether “the procedure was fair having regard to all of the circumstances” and “whether the applicant knew the case to meet and had a full and fair chance to respond”.  See: Ahmed v. Canada (Citizenship and Immigration), 2023 FC 72 at […]

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What is an AI Hype Cycle and How Is it Relevant to Canadian Immigration Law?

Recently I have been reading and learning more about AI Hype Cycles.

I first learned this term from Professor Kristen Thomasen when she did a guest lecture for our Legal Methodologies graduate class and discussed it with respect to her own research on drone technology and writing/researching during hype cycles. Since then, in almost AI-related seminar I have attended the term has come up with respect to the current buzz and attention being paid to AI. For example, Timnit Gebru in her talk for the GC Data Conference which I recently attended noted that a lot of what is being repackaged as new AI today was the same work in ‘big data’ that she studied many years back. For my own research, it is important to understand hype cycles to ground my research into more principled and foundational approaches so that I can write and explore the changes in technology while doing slow scholarship notwithstanding changing public discourse and the respective legislative/regulatory changes that might follow.

A good starting point for understanding hype cycles, especially in the AI market, is the Gartner Hype Cycle. Who those who have not heard the term yet, I would recommend checking out the following video:

Gartner reviews technological hype cycles through five phases: (1) innovation trigger; (2) peak of inflated expectations; (3) trough of disillusionment; (4) slope of enlightenment, and plateau of productivity.

It is interesting to see how Gartner has labelled the current cycles:

One of the most surprising things to me on first view is how automatic systems and deicsion intelligence is still on the innovation trigger – early phase on the hype cycle. The other is how many different types of AI technology are on the hype cycle and how many the general public actually know/engage with. I would suggest at most 50% of this list is in the vocabulary and use of even the most educated folks. I also find that from a laypersons perspective (which I consider myself on AI), challenges in classifying whether certain AI concepts fit one category or another or are a hybrid. This means AI societal knowledge is low and even for some of the items that are purportedly on the Slope of Enlightment or Plateau of Productivity.

It is important to note before I move on that that the AI Hype Cycle also has been used in terms outside of the Gartner definition, more in a more criticial sense of technologies that are in a ‘hype’ phase that will eventually ebb and flow. A great article on this and how it affects AI definitions is the piece by Eric Siegel in the Harvard Business Review how the hype around Supervised Machine Learning has been rebranded into a hype around AI and has been spun into this push for Artificial General Intelligence that may or may not be achievable.

 

Relevance to the Immigration Law Space

The hype cycle is relevant to Canadian immigration law in a variety of ways.

First, on the face, Gartner is a contracting partner of IRCC which means they are probably bringing in the hype cycle into their work and their advice to them.

Second, it brings into question again how much AI-based automated decision-making systems (ADM) is still in the beginning of the hype cycle. It make sense utilizing this framework to understand why these systems are being so heralded by Government in their policy guides and presentation, but also that there could be a peak of inflated expectations on the horizon that may lead to more hybrid decision-making or perhaps a step back from use.

The other question is about whether we are (and I am a primary perpetrator of this) overly-focused on automated-decision making systems without considering the larger AI supply chain that will likely interact. Jennifer Cobbe et al talk about this in their paper “Understanding accountability in algorithmic supply chains” which was assigned for reading in my Accountable Computer Systems course. Not only are there different AI components, providers, downstream/upstream uses, and actors that may be involved in the AI development and application process.

Using immigration as an example, there may be one third-party SAAS that checks photos, another software using black-box AI may engage in facial recognition, and ultimately, internal software that does machine-learning triaging or automation of refusal notes generation. The question of how we hold these systems and their outputs accountable will be important, especially if various components of the system are on different stages of the hype cycle or not disclosed in the final decision to the end user (or immigration applicant).

Third, I think that the idea of hype cycles is very relevant to my many brave colleagues who are investing their time and energy into building their own AI tools or implementing sofware solutions for private sector applicants. The hype cycle may give some guidance as to the innovation they are trying to bring and the timeframe they have to make a splash into the market. Furthermore, immigration (as a dynamic and rapidly changing area of law) and immigrants (as perhaps needing different considerations with respect to technological use, access, or norms) may have their own considerations that may alter Gartner’s timelines.

It will be very interesting to continue to monitor how AI hype cycles drive both private and public innovation in this emerging space of technologies that will significantly impact migrant lives.

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Will Tao is an Award-Winning Canadian Immigration and Refugee Lawyer, Writer, and Policy Advisor based in Vancouver. Vancouver Immigration Blog is a public legal resource and social commentary.

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