Data, Power and Bias in Artificial Intelligence

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Title: Data, Power and Bias in Artificial Intelligence
Authors: Leavy, SusanO'Sullivan, BarrySiapera, Eugenia
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Date: 21-Jul-2020
Online since: 2021-09-08T15:06:36Z
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.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Conference Publication
Keywords: Myth of objectivityBiasUnderrepresentationArtificial intelligenceGovernance
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Language: en
Status of Item: Peer reviewed
Conference Details: AI for Social Good: Harvard CRCS Workshop, Online, 20-21 July 2020
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Appears in Collections:Information and Communication Studies Research Collection
Insight Research Collection

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