Fairness, accountability and transparency: notes on algorithmic decision-making in criminal justice

Abstract

Over the last few years, legal scholars, policy-makers, activists and others have generated a vast and rapidly expanding literature concerning the ethical ramifications of using artificial intelligence, machine learning, big data and predictive software in criminal justice contexts. These concerns can be clustered under the headings of fairness, accountability and transparency. First, can we trust technology to be fair, especially given that the data on which the technology is based are biased in various ways? Second, whom can we blame if the technology goes wrong, as it inevitably will on occasion? Finally, does it matter if we do not know how an algorithm works or, relatedly, cannot understand how it reached its decision? I argue that, while these are serious concerns, they are not irresolvable. More importantly, the very same concerns of fairness, accountability and transparency apply, with even greater urgency, to existing modes of decisionmaking in criminal justice. The question, hence, is comparative: can algorithmic modes of decision-making improve upon the status quo in criminal justice? There is unlikely to be a categorical answer to this question, although there are some reasons for cautious optimism.Downloaded from https://www.cambridge.org/core. IP address: 44.224.250.200, on 05 Jul 2020 at 23:09:34, subject to the Cambridge Core terms of use, available at 1 Silvestri and Crowther-Dowey (2008) note that '[t]he overriding consensus within criminology remains that while women do commit a broad range of offences, they commit less crime than men and are less dangerous and violent than their male counterparts' (p. 25).

Properties of artificial intelligence systems in the context of their use in legal activities

“…We agree with V. Chiao that it can be considered unfair for the predictor indicators in group A in the second situation, while in the first situation, this indicator can be regarded as objective [3].…”

“…Unlike humans, disputes with an algorithm can be as meaningful as disputes with a household appliance such as a refrigerator or toaster. Finally, how important is it for us to understand the inner workings of an algorithm, and what consequences can arise from not understanding the logic used by AI in making decisions [3]?…”

Catching up with AI: Pushing toward a cohesive governance framework

“…Holding AI and its predictive algorithm accountable is not the same as holding humans accountable for their decisions and actions (Chiao, 2019). In the traditional court system, lawyers and judges do not have the technical credentials to evaluate if an AI algorithm is at fault for acting unethically and without transparency.…”

“…In the traditional court system, lawyers and judges do not have the technical credentials to evaluate if an AI algorithm is at fault for acting unethically and without transparency. However, it is reasonable to hold individuals, such as software developers and public administrators, who use AI to deliver public e‐government services accountable for occasions where AI violates the law or acts unethically (Chiao, 2019). In this regard, U.S. policy makers could learn from the EU and its AI governance framework.…”

Beyond 100 Ethical Concerns in the Development of Robot-to-Robot Cooperation

“…The field of AI ethics has relatively long-established origins in areas such as information ethics [18], machine ethics [1] and is related to the domain of Fairness, Accountability and Transparency (FAT) [9]. Societies are witnessing a sharp turn from mere research interest in AI ethics towards a growing urgency for applied ethical considerations in relation to issues already manifesting in the systems.…”