Data, Power and Bias in Artificial Intelligence

Files in This Item:
 File SizeFormat
DownloadDataPowerBias_AI.pdf120.48 kBAdobe PDF
Title: Data, Power and Bias in Artificial Intelligence
Authors: Leavy, SusanO'Sullivan, BarrySiapera, Eugenia
Permanent link: http://hdl.handle.net/10197/12457
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
Other versions: https://aiforgood2020.github.io/
https://crcs.seas.harvard.edu/publications/data-power-and-bias-artificial-intelligence
Language: en
Status of Item: Peer reviewed
Conference Details: 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/
Appears in Collections:Information and Communication Studies Research Collection
Insight Research Collection

Show full item record

Page view(s)

144
checked on Sep 20, 2021

Download(s)

7
checked on Sep 20, 2021

Google ScholarTM

Check


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.