Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911869297491968 |
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| author | Chien, Jennifer Danks, David |
| author_facet | Chien, Jennifer Danks, David |
| contents | Algorithmic harms are commonly categorized as either allocative or representational. This study specifically addresses the latter, focusing on an examination of current definitions of representational harms to discern what is included and what is not. This analysis motivates our expansion beyond behavioral definitions to encompass harms to cognitive and affective states. The paper outlines high-level requirements for measurement: identifying the necessary expertise to implement this approach and illustrating it through a case study. Our work highlights the unique vulnerabilities of large language models to perpetrating representational harms, particularly when these harms go unmeasured and unmitigated. The work concludes by presenting proposed mitigations and delineating when to employ them. The overarching aim of this research is to establish a framework for broadening the definition of representational harms and to translate insights from fairness research into practical measurement and mitigation praxis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_01705 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation Chien, Jennifer Danks, David Computers and Society Artificial Intelligence Computation and Language Machine Learning Algorithmic harms are commonly categorized as either allocative or representational. This study specifically addresses the latter, focusing on an examination of current definitions of representational harms to discern what is included and what is not. This analysis motivates our expansion beyond behavioral definitions to encompass harms to cognitive and affective states. The paper outlines high-level requirements for measurement: identifying the necessary expertise to implement this approach and illustrating it through a case study. Our work highlights the unique vulnerabilities of large language models to perpetrating representational harms, particularly when these harms go unmeasured and unmitigated. The work concludes by presenting proposed mitigations and delineating when to employ them. The overarching aim of this research is to establish a framework for broadening the definition of representational harms and to translate insights from fairness research into practical measurement and mitigation praxis. |
| title | Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation |
| topic | Computers and Society Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.01705 |