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| Format: | Recurso digital |
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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15773292 |
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| _version_ | 1866901877156741120 |
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| author | Poama, Andrei Fosch-Villaronga, Eduard |
| author_facet | Poama, Andrei Fosch-Villaronga, Eduard |
| contents | <p>This chapter examines how artificial intelligence (AI) bias can undermine legal (or legally relevant) norms and standards. It does so by introducing a conceptual distinction between <em>bias in AI</em> (arising from flawed data, programming choices, or emergent algorithmic behaviour) and <em>bias towards AI</em> (where human decision-makers either overtrust or unjustifiably dismiss AI outputs). This distinction can equip legal practitioners with a deeper, yet straightforward understanding of various AI biases and the risks they raise. To mitigate these risks, the chapter explores preventive and corrective strategies, including regulatory sandboxes, fairness-aware AI design, auditing laws, and legal oversight mechanisms. Addressing AI bias is not merely a technical challenge—it is a professional responsibility for legal practitioners who seek to properly navigate the relationship between law and AI.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15773292 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI bias Poama, Andrei Fosch-Villaronga, Eduard <p>This chapter examines how artificial intelligence (AI) bias can undermine legal (or legally relevant) norms and standards. It does so by introducing a conceptual distinction between <em>bias in AI</em> (arising from flawed data, programming choices, or emergent algorithmic behaviour) and <em>bias towards AI</em> (where human decision-makers either overtrust or unjustifiably dismiss AI outputs). This distinction can equip legal practitioners with a deeper, yet straightforward understanding of various AI biases and the risks they raise. To mitigate these risks, the chapter explores preventive and corrective strategies, including regulatory sandboxes, fairness-aware AI design, auditing laws, and legal oversight mechanisms. Addressing AI bias is not merely a technical challenge—it is a professional responsibility for legal practitioners who seek to properly navigate the relationship between law and AI.</p> |
| title | AI bias |
| url | https://doi.org/10.5281/zenodo.15773292 |