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  5. Data, Power and Bias in Artificial Intelligence
 
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Data, Power and Bias in Artificial Intelligence

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Download DataPowerBias_AI.pdf120.48 KB
Author(s)
Leavy, Susan 
O'Sullivan, Barry 
Siapera, Eugenia 
Uri
http://hdl.handle.net/10197/12457
Date Issued
21 July 2020
Date Available
08T15:06:36Z September 2021
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.
Sponsorship
European Commission - European Regional Development Fund
Science Foundation Ireland
Type of Material
Conference Publication
Keywords
  • Myth of objectivity

  • Bias

  • Underrepresentation

  • Artificial intelligen...

  • Governance

Web versions
https://aiforgood2020.github.io/
https://crcs.seas.harvard.edu/publications/data-power-and-bias-artificial-intelligence
Language
English
Status of Item
Peer reviewed
Description
AI for Social Good: Harvard CRCS Workshop, Online, 20-21 July 2020
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Information and Communication Studies Research Collection
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