Assessing and Addressing Algorithmic Bias - But Before We Get There

TL;DR

This paper discusses the challenges of assessing and mitigating algorithmic bias in practice, proposing an early interdisciplinary approach to help teams evaluate data and algorithm fairness.

Contribution

It introduces a practical process framework for practitioners to assess and address algorithmic and data biases, bridging scattered literature and interdisciplinary efforts.

Findings

Identified key challenges in bias assessment and mitigation.

Proposed an initial process framework for practical bias evaluation.

Highlighted the need for interdisciplinary approaches in bias mitigation.

Abstract

Algorithmic and data bias are gaining attention as a pressing issue in popular press - and rightly so. However, beyond these calls to action, standard processes and tools for practitioners do not readily exist to assess and address unfair algorithmic and data biases. The literature is relatively scattered and the needed interdisciplinary approach means that very different communities are working on the topic. We here provide a number of challenges encountered in assessing and addressing algorithmic and data bias in practice. We describe an early approach that attempts to translate the literature into processes for (production) teams wanting to assess both intended data and algorithm characteristics and unintended, unfair biases.