CRII: CHS: Estimating the Financial, Social and Ethical Impacts of Algorithmic Crime Analysis

Marquette University, Milwaukee WI

Investigators

Abstract

This research seeks to understand the financial, social and ethical impacts of crime analysis algorithms. Misapplication of algorithms has the capacity for great harm and discrimination, notably when applied in sentencing decisions about criminal offenders. Inspecting and investigating algorithmic biases for their real-world impacts is an extremely novel but increasingly important area. The current literature has mostly focused on philosophical and sociological critiques of algorithms. What is sorely needed is a mathematical and computational investigation that builds on these socio-technical critiques in order to provide contextual and quantitative evidence for actual impact. We need to know how people's biases, assumptions, norms and values play into algorithmic perceptions and applications. More specifically, governments often seek to apply algorithms to crime analysis with the usually stated justification of financial efficiencies, reducing human error and smoothening bureaucracies. Therefore, this research seeks to understand the practical, financial, social and ethical impacts of deploying algorithms in the criminal justice system in order to develop evidence-based policies. This project has four main objectives. First, using crime mapping as a lens of inquiry, the project will investigate the perceptions of financial, social and ethical dimensions of algorithmic crime analysis, to uncover both qualitative and quantitative insights about how laypeople and experts perceive its various implications. Second, the research will deconstruct popular algorithms used in crime analysis in order to understand specific points at which bias may occur and statistically better variants that may mitigate them. Third, it will compare and contrast each biased variant with its less biased counterpart to compute metrics about financial, social and ethical impacts, providing quantitative implications for biased criminal justice policy. Fourth, as a result of the first three objectives, this proposal will help expand initial efforts to build a community of like-minded scholars, though organizing a summer workshop involving practitioners, researchers and students to educate and co-create a community of practice around ethical, fair, accountable and transparent algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.