Fairness in algorithmic decision-making: trade-offs, policy choices, and procedural protections
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
This article discusses conceptions of fairness in algorithmic decision-making, within the context of the UK’s legal system. Using practical operational examples of algorithmic tools, it argues that such practices involve inherent technical trade-offs over multiple, competing notions of fairness, which are further exacerbated by policy choices made by those public authorities who use them. This raises major concerns regarding the ability of such choices to affect legal issues in decision-making, and transform legal protections, without adequate legal oversight, or a clear legal framework. This is not to say that the law does not have the capacity to regulate and ensure fairness, but that a more expansive idea of its function is required.
|Publication status||Published - 29 Oct 2019|
- algorithmic decision-making, machine learning, fairness, criminal justice, administrative law