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Artificial fairness? Trust in algorithmic police decision-making
Zoë Hobson1, Julia A Yesberg1, Ben Bradford1
1Institute for Global City Policing, Department of Security and Crime Science, University College London, 35 Tavistock Square, WC1H 9EZ London, UK.
Journal of Experimental Criminology
|September 20, 2021
Summary
Algorithmic policing decisions are perceived as less fair than human ones. However, witnessing successful algorithmic use can increase public trust and support for this technology.
Area of Science:
- Social Sciences
- Technology and Society
- Criminology
Background:
- Algorithmic decision-making is increasingly used in policing.
- Public trust and acceptance are crucial for technology adoption.
- Understanding public perception of algorithmic policing is essential.
Purpose of the Study:
- To compare trust in algorithmic vs. human policing decisions.
- To assess the impact of algorithmic policing exposure on general support.
- To investigate trust as a heuristic for understanding algorithmic policing.
Main Methods:
- An online experiment was conducted.
- Participants evaluated policing decisions made by algorithms and officers.
- Factors such as decision method, outcome, and scenario type were varied.
Main Results:
- Algorithmic decisions were rated as less fair and appropriate than officer decisions.
- Perceptions of fairness predicted support for algorithmic policing.
- Exposure to successful algorithmic use, mediated by trust, increased support.
Conclusions:
- Sole reliance on algorithms may erode public trust in policing.
- Repeated exposure to successful algorithmic policing enhances its acceptability.
- Balancing algorithmic efficiency with public trust is key for implementation.
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