Bias as Boundary Object: Unpacking The Politics Of An Austerity Algorithm Using Bias Frameworks

Grill, Gabriel; Fischer, Fabian; Cech, Florian

doi:10.1145/3593013.3594120

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Notwithstanding the widespread claim that algorithmic bias is a mirror of existing disparities [45], it is rarer for algorithmic bias to be taken seriously as a source of evidence to emancipate and support marginalized communities. Bias has helpfully been identified as a boundary object [32], an entity which, despite disagreement about its nature, provides a lens for critique and confrontation of different stakeholders' views. In this paper, we take this characterization of bias a step further; namely, we want to move from bias as an object of and for critique to a pragmatic device to address the disparities it mirrors.…”

Section: Background and Related Work 21 Algorithmic Bias: From An Obj...mentioning

confidence: 99%

Evidence of What, for Whom? The Socially Contested Role of Algorithmic Bias in a Predictive Policing Tool

Ziosi,

Pruss

2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

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This paper presents a critical, qualitative study of the social role of algorithmic bias in the context of the Chicago crime prediction algorithm, a predictive policing tool that forecasts when and where in the city crime is most likely to occur. Through interviews with 18 Chicago-area community organizations, academic researchers, and public sector actors, we show that stakeholders from different groups articulate diverse problem diagnoses of the tool's algorithmic bias, strategically using it as evidence to advance criminal justice interventions that align with stakeholders' positionality and political ends. Drawing inspiration from Catherine D'Ignazio's taxonomy of "refusing and using" data, we find that stakeholders use evidence of algorithmic bias to reform the policies around police patrol allocation; reject algorithm-based policing interventions; reframe crime as a structural rather than interpersonal problem; reveal data on authority figures in an effort to subvert their power; repair and heal families and communities; and, in the case of more powerful actors, to reaffirm their own authority or existing power structures. We identify the implicit assumptions and scope of these varied uses of algorithmic bias as evidence, showing that they require different (and sometimes conflicting) values about policing and AI. This divergence reflects long-standing tensions in the criminal justice reform landscape between the values of liberation and healing often centered by system-impacted communities and the values of surveillance and deterrence often instantiated in datadriven reform measures. We advocate for centering the interests and experiential knowledge of communities impacted by incarceration to ensure that evidence of algorithmic bias can serve as a device to challenge the status quo. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI.

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Section: Background and Related Work 21 Algorithmic Bias: From An Obj...mentioning

confidence: 99%

Evidence of What, for Whom? The Socially Contested Role of Algorithmic Bias in a Predictive Policing Tool

Ziosi,

Pruss

2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

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Constructing Capabilities: The Politics of Testing Infrastructures for Generative AI

Grill

2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

Bias as Boundary Object: Unpacking The Politics Of An Austerity Algorithm Using Bias Frameworks

Cited by 2 publications

References 17 publications

Evidence of What, for Whom? The Socially Contested Role of Algorithmic Bias in a Predictive Policing Tool

Evidence of What, for Whom? The Socially Contested Role of Algorithmic Bias in a Predictive Policing Tool

Constructing Capabilities: The Politics of Testing Infrastructures for Generative AI

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