Artificial Intelligence and Policing: It’s a Matter of Trust
Related to these concerns is the problem of transparency and explainability. Some AI systems lack transparency because their algorithms are closed-source proprietary software. But it can be difficult to render even open-source algorithms explainable—particularly those used in machine learning—due to their complexity. After all, a key benefit of AI lies in its ability to analyse large datasets and detect relationships that are too subtle for the human mind to identify. Making models more comprehensible by simplifying them may require trade-offs in sensitivity, and therefore also in accuracy. Together these concerns are often referred to as the ‘AI black box’ (inputs and outputs are known, but not what goes on in the middle).
In short, a lack of transparency and explainability makes the detection of bias and discriminatory outputs more difficult. This is both an ethical concern and a legal one when justice systems require that charging decisions be understood by all parties to avoid discriminatory practices. Indeed, research suggests that when individuals trust the process of decision-making, they are more likely to trust the outcomes in justice settings, even if those outcomes are unfavourable. Explainability and transparency can therefore be important considerations when seeking to enhance public accountability and trust in these systems.
As Westendorf points out, steps can be taken to mitigate bias, such as pre-emptively coding against foreseeable biases and involving human analysts in the processes of building and leveraging AI systems. With these sorts of safeguards in place (as well as deployment reviews and evaluations), use of AI may have the upshot of establishing built-in objectivity for policing decisions by reducing reliance on heuristics and other subjective decision-making practices. Over time, AI use may assist in debiasing policing outcomes.
While there’s no silver bullet for enhancing explainability, there are plenty of suggestions, particularly when it comes to developing AI solutions to enhance AI explainability. Transparency challenges generated by proprietary systems can also be alleviated when AI systems are owned by police and designed in house.
Yet the need for explainability is only one consideration for enhancing accountability and public trust in the use of AI systems by police, particularly when it comes to predictive policing. Recent research has found that people’s level of trust in the police (which is relatively high in Australia) correlates with their level of acceptance of changes in the tools and technology used by police. In another study, participants exposed to purportedly successful policing applications of AI technology were more likely to support wider police use of such technologies than those exposed to unsuccessful uses, or not exposed to examples of AI application at all. In fact, participants exposed to purportedly successful applications even judged the decision-making process involved to be trustworthy.
This suggests that focusing on broader public trust in policing will be vital in sustaining public trust and confidence in the use of AI in policing, regardless of the degree of algorithmic transparency and explainability. The goal of transparent and explainable AI shouldn’t neglect this broader context.
Nick Evans is a lecturer and researcher at the Tasmanian Institute of Law Enforcement Studies at the University of Tasmania.This article is published courtesy of the Australian Strategic Policy Institute (ASPI).
