This article was originally published on The Conversation, an independent and nonprofit source of news, analysis and commentary from academic experts. Disclosure information is available on the original site.
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Author: Benjamin Perrin, Professor of Law, University of British Columbia
Interpol Secretary General J眉rgen Stock recently warned that artificial intelligence (AI) is facilitating crime on an 鈥渋ndustrial scale鈥� using deepfakes, voice simulation and phony documents.
Police around the world are also turning to AI tools such as facial recognition, automated licence plate readers, gunshot detection systems, social media analysis and even police robots. AI use by lawyers is similarly 鈥渟kyrocketing鈥� as judges adopt new guidelines for using AI.
While AI promises to transform criminal justice by increasing operational efficiency and improving public safety, it also comes with risks related to privacy, accountability, fairness and human rights.
Concerns about AI bias and discrimination are well documented. Without safeguards, AI risks undermining the very principles of truth, fairness, and accountability that our justice system depends on.
In a recent report from the University of British Columbia鈥檚 School of Law, Artificial Intelligence & Criminal Justice: A Primer, we highlighted the myriad ways AI is already impacting people in the criminal justice system. Here are a few examples that reveal the significance of this evolving phenomenon.
The promises and perils of police using AI
In 2020, an investigation by The New York Times exposed the sweeping reach of Clearview AI, an American company that had built a facial recognition database using more than three billion images scraped from the internet, including social media, without users鈥� consent.
Policing agencies worldwide that used the program, including several in Canada, faced public backlash. Regulators in multiple countries found the company had violated privacy laws. It was asked to cease operations in Canada.
Clearview AI continues to operate, citing success stories of helping to exonerate a wrongfully convicted person by identifying a witness at a crime scene; identifying someone who exploited a child, which led to their rescue; and even detecting potential Russian soldiers seeking to infiltrate Ukrainian checkpoints.
There are longstanding and persistent concerns, however, that facial recognition is prone to false positives and other errors, particularly when it comes to identifying Black and other racialized people, exacerbating systemic racism, bias and discrimination.
Some law enforcement agencies in Canada that were caught up in the Clearview AI controversy have since responded with new measures, such as the Toronto Police Service鈥檚 policies on AI use and the RCMP鈥檚 transparency program.
Others, however, like the Vancouver Police Department, promised to develop policies but haven鈥檛, while at the same time seeking access to city traffic camera footage.
The regulation of police uses of AI is a pressing concern if we are to safely navigate the promise and perils of AI use.
Deepfake evidence in court
Another area where AI is presenting challenges in the criminal justice system is deepfake evidence, including AI-generated documents, audio, photos, and videos.
The phenomenon has already led to cases where one party alleges that the other party鈥檚 evidence is a deepfake, casting doubt on it, even if it鈥檚 legitimate. This has been dubbed the 鈥渓iar鈥檚 dividend.鈥�
A high-profile example of allegations involving deepfake evidence arose in the case of Joshua Doolin, who faced charges related to the January 6, 2021, insurrection at the U.S. Capitol for which he was ultimately convicted. Doolin鈥檚 attorney contended that prosecutors should be required to authenticate video evidence sourced from YouTube, raising concerns about the potential use of deepfakes.
Jurors could be especially prone to doubts about potential deepfakes given high-profile deepfake incidents involving celebrities or their own use of AI technologies.
Judges are also sounding the alarm about the challenges of detecting increasingly sophisticated deepfake evidence admitted in court. There are concerns that a wrongful conviction or acquittal could result.
I鈥檝e personally heard from a number of legal practitioners, including judges and lawyers, that they are struggling to address this issue. It is a frequent subject at legal seminars and judicial training events. Until we have a clear statement from appellate courts on the matter, legal uncertainty will remain.
Risk assessment algorithms
Imagine an AI algorithm that you couldn鈥檛 understand deemed you a flight risk or at high risk to re-offend, and that information was used by a judge or parole board to deny your release from custody. This dystopian reality isn鈥檛 a fiction but a reality in many parts of the world.
Automated algorithmic decision-making is already being used in various countries for decisions on access to government benefits and housing, assessing domestic violence risk, making immigration determinations and a host of criminal justice applications from bail decisions to sentencing to prison classification to parole outcomes.
People impacted by these algorithms typically fail to gain access to their underlying proprietary software. Even if they could, they are often 鈥渂lack boxes鈥� that are impossible to penetrate.
Even worse, research into some algorithms has found serious concerns about racial bias. A key reason for this problem is that AI models are trained on data from societies that are already embedded with systemic racism. 鈥淕arbage in, garbage out鈥� is a commonly used adage to explain this.
Fostering innovation while safeguarding justice
The need for legal and ethical AI in high-risk situations pertaining to criminal justice is paramount. There is undoubtedly a need for new laws, regulations and policies specifically designed to address these challenges.
The European Union鈥檚 AI Act bans AI for uses such as untargeted scraping images off the internet or CCTV, real-time remote biometric identification in public (with limited exceptions), and assessing recidivism risk based solely on profiling or personality traits.
Canada鈥檚 laws have not kept pace, and those that have been proposed have challenges. At the federal level, Bill C-27 (which includes an Artificial Intelligence and Data Act) has been stuck in committee for over a year, and it is unlikely to be adopted by this Parliament.
Ontario鈥檚 proposed AI legislation, Bill 194, would exempt police from its application and fails to include provisions on ensuring respect for human rights.
Canada should vigorously enforce existing laws and policies that already apply to AI use by public authorities. The Canadian Charter of Rights and Freedoms includes numerous fundamental freedoms, legal rights and equality protections that bear directly on these issues. Likewise, privacy legislation, human rights legislation, consumer protection legislation and tort law all set important standards for AI use.
The potential impact of AI on people in the criminal justice system is immense. Without thoughtful and rigorous oversight, it risks undermining public confidence in the justice system and perpetuating existing problems with real human consequences.
Fortunately, Canada has not yet gone as far down the road of widespread AI adoption in criminal justice as other countries. We still have time to get ahead of it. Policymakers, courts and civil society must act swiftly to ensure that AI serves justice rather than undermines it.
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Benjamin Perrin does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
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This article is republished from The Conversation under a Creative Commons license. Disclosure information is available on the original site. Read the original article: https://theconversation.com/ai-and-criminal-justice-how-ai-can-support-not-undermine-justice-242389
Benjamin Perrin, Professor of Law, University of British Columbia, The Conversation
