Designing Bias Suppressing Robots for `fair' Robot moderated Human-Human Interactions.

Research has shown that data-driven robots deployed in social settings are likely to unconsciously perpetuate systemic social biases. Despite this, robots can also be deployed to promote fair behaviour in humans. These phenomena have led to the development of two broad sub-disciplines in HRI concerning ‘fairness’: a data-centric approach to ensuring robots operate fairly and a human-centric approach which aims to use robots as interventions to promote fairness in society. To date, these two fields have developed independently, thus it is unknown how data-driven robots can be used to suppress biases in human-human interactions. In this paper, we present a conceptual framework and hypothetical example of how robots might deploy data-driven fairness interventions, to actively suppress social biases in human-human interactions.

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Publication Details

Published
2024-11-20
DOI
https://doi.org/10.1145/3687272.3690877
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.53
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article

Designing Bias Suppressing Robots for `fair' Robot moderated Human-Human Interactions.

Muneeb Ahmad, Takayuki Kanda, Matthew Roach, Peter Daish
1 citations
Ethics and Social Impacts of AI
0.53
article

Designing Bias Suppressing Robots for `fair' Robot moderated Human-Human Interactions.

Muneeb Ahmad, Takayuki Kanda, Matthew Roach, Peter Daish
article en
1 citations

Abstract

Research has shown that data-driven robots deployed in social settings are likely to unconsciously perpetuate systemic social biases. Despite this, robots can also be deployed to promote fair behaviour in humans. These phenomena have led to the development of two broad sub-disciplines in HRI concerning ‘fairness’: a data-centric approach to ensuring robots operate fairly and a human-centric approach which aims to use robots as interventions to promote fairness in society. To date, these two fields have developed independently, thus it is unknown how data-driven robots can be used to suppress biases in human-human interactions. In this paper, we present a conceptual framework and hypothetical example of how robots might deploy data-driven fairness interventions, to actively suppress social biases in human-human interactions.

Kyoto University (JP), Swansea University (GB)
Openalex Percentile: Top 25%
Ethics and Social Impacts of AI
0.53
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