Data, Power and Bias in Artificial Intelligence

TL;DR

This paper reviews the challenges and approaches to mitigating societal bias in AI, emphasizing data justice, fairness, and the complex interplay of technical and policy solutions for ethical AI development.

Contribution

It provides a comprehensive overview of interdisciplinary efforts to understand and address bias in AI data and systems, highlighting the potential for bias to be used positively.

Findings

Bias in AI training data reflects social injustices

Addressing bias requires technical, social, and policy solutions

Bias may be leveraged for social good

Abstract

Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains exploring the interrelated dynamics of each and examining whether the inevitability of bias in AI training data may in fact be used for social good. We highlight the complexity associated with defining policies for dealing with bias. We also consider technical challenges in addressing issues of societal bias.