Hope, Hype, and Fear: The Promise and Potential Pitfalls of the Big Data Era in Criminal Justice

Isaac, William

doi:10.2139/ssrn.3145308

Cited by 15 publications

(16 citation statements)

References 5 publications

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“…Algorithmic decision systems (Isaac, 2017) are increasingly common within the US criminal justice system despite significant evidence of shortcomings, such as the linking of criminal datasets to patterns of discriminatory policing (Angwin et al, 2016;Lum and Isaac, 2016;Richardson et al, 2019). Beyond the domain of criminal justice, there are numerous instances of predictive algorithms perpetuating social harms in everyday interactions, including examples of facial recognition systems failing to detect Black faces and perpetuating gender stereotypes (Buolamwini and Gebru, 2018;Keyes, 2018;Stark, 2019), hate speech detection algorithms identifying Black and queer vernacular as toxic (Sap et al, 2019), new recruitment tools discriminating against women (Dastin, 2018), automated airport screening-systems systematically flagging trans bodies for security checks (Costanza-Chock, 2018), and predictive algorithms used to purport that queerness can be identified from facial images alone (Agüera y Arcas et al, 2018).…”

Section: Site 1: Algorithmic Decision Systemsmentioning

confidence: 99%

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

2020

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This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial Intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations. Whilst the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us; ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all. Keywords decolonisation • coloniality • sociotechnical foresight • intercultural ethics • critical technical practice • artificial intelligence • affective community

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Section: Site 1: Algorithmic Decision Systemsmentioning

confidence: 99%

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

2020

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“…In response, Equivant published a technical report [41] refuting the claims of bias made by ProPublica and concluded that COMPAS is sufficiently calibrated, in the sense that it satisfies Predictive Parity at key thresholds. As previous research has shown [81,113,160], modern policing tactics center around targeting a small number of neighborhoods-often disproportionately populated by non-white and low income residents-with recurring patrols and stops. This uneven distribution of police attention, as well as other factors such as funding for pretrial services [101,168], can be rephrased in the language of CBNs as indicating the presence of a direct path 𝐴 → 𝑌 (through unobserved neighborhood) in the CBN representing the data-generation mechanism, as well as an influence of 𝐴 on 𝑌 through the set of variables containing number of prior arrests that are used to form a prediction Ŷ of 𝑌 .…”

Section: Causal Bayesian Network: a Visual Tool For (Un)fairnessmentioning

confidence: 99%

Fairness in Machine Learning

Oneto

Chiappa

2020

Studies in Computational Intelligence

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Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteristics such as e.g. race, gender, disabilities, and sexual or political orientation. In this manuscript, we discuss some of the limitations present in the current reasoning about fairness and in methods that deal with it, and describe some work done by the authors to address them. More specifically, we show how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios. We describe how optimal transport theory can be used to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes, overcoming the limitation of most approaches that approximate fairness desiderata by imposing constraints on the lower order moments or other functions of those distributions. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria, and that enjoys strong theoretical guarantees. We introduce an approach to learn fair representations that can generalize to unseen tasks. Finally, we describe a technique that accounts for legal restrictions about the use of sensitive attributes.

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“…These data often reflect and directly encode some of the social, gender, and racial biases of societies. For instance, the U.S. criminal justice system increasingly relies on AI in its decision processes (Isaac 2017). These algorithms, however, are trained on criminal data sets that show patterns of discriminatory policing (Angwin et al 2017, Richardson andSchultz 2019).…”

Section: Responsible Service Analyticsmentioning

confidence: 99%

Frontiers in Service Science: The Management of Data Analytics Services: New Challenges and Future Directions

Véricourt

Perakis

2020

Service Science

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In this short paper, we discuss the impact of data analytics in services and delineate future research directions for the field. After illustrating how data analytics are transforming different service sectors, we consider the provision of data analysis as a service in its own right. We discuss how the very nature of data and certain features of the machine learning method give rise to new issues and pitfalls for the management of these services, which delineates as many future research directions. We also discuss the coproduction of services by humans and machines and call for more research on responsible data analytics services to tackle some of the most pressing ethical issues in our societies.

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Hope, Hype, and Fear: The Promise and Potential Pitfalls of the Big Data Era in Criminal Justice

Cited by 15 publications

References 5 publications

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

Fairness in Machine Learning

Frontiers in Service Science: The Management of Data Analytics Services: New Challenges and Future Directions

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