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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| Accesso online: | https://doi.org/10.5281/zenodo.15570677 |
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| _version_ | 1866901197328220160 |
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| author | Khor, Ashley |
| author_facet | Khor, Ashley |
| contents | <p>This archive contains the presentation materials for <strong>“Beyond Compliance: Generative AI Safety Evaluation for Civil Society,”</strong> delivered by Ashley Khor at the IHS Summer Graduate Conference on May 31, 2025. The talk introduces a hybrid evaluation framework designed specifically for high-impact, community-driven AI use cases. Developed through both academic review and field practice, the framework incorporates five core dimensions of safety:</p> <ol> <li> <p><strong>Technical and content integrity</strong></p> </li> <li> <p><strong>Relational and ethical grounding</strong> (design justice, feminist, and trauma-informed principles)</p> </li> <li> <p><strong>Usability and accessibility</strong></p> </li> <li> <p><strong>Organizational readiness</strong></p> </li> <li> <p><strong>Contextual appropriateness</strong> for sensitive, user-directed interactions.</p> </li> </ol> <p>In addition to defining these dimensions, the project extends current GenAI safety practices through a participatory methodology that includes five test-a-thon methods: <strong>human-in-the-loop scoring</strong>, <strong>participatory red teaming</strong>, <strong>simulated user journeys</strong>, <strong>reflective feedback circles</strong>, and <strong>observer-based shadow scoring</strong>. Together, these methods offer a survivor-centered and emotionally attuned lens for evaluating AI systems — not only by their outputs, but by how they are experienced, trusted, and used.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15570677 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Beyond Compliance: Generative AI Safety Evaluation for Civil Society Khor, Ashley Artificial Intelligence Artificial Intelligence/standards Artificial Intelligence/ethics Community-Based Participatory Research/methods Design Justice Survivor-AI Generative AI Safety Trauma-Informed Computing LLM Evaluation Public Interest Technology AI Trust & Safety <p>This archive contains the presentation materials for <strong>“Beyond Compliance: Generative AI Safety Evaluation for Civil Society,”</strong> delivered by Ashley Khor at the IHS Summer Graduate Conference on May 31, 2025. The talk introduces a hybrid evaluation framework designed specifically for high-impact, community-driven AI use cases. Developed through both academic review and field practice, the framework incorporates five core dimensions of safety:</p> <ol> <li> <p><strong>Technical and content integrity</strong></p> </li> <li> <p><strong>Relational and ethical grounding</strong> (design justice, feminist, and trauma-informed principles)</p> </li> <li> <p><strong>Usability and accessibility</strong></p> </li> <li> <p><strong>Organizational readiness</strong></p> </li> <li> <p><strong>Contextual appropriateness</strong> for sensitive, user-directed interactions.</p> </li> </ol> <p>In addition to defining these dimensions, the project extends current GenAI safety practices through a participatory methodology that includes five test-a-thon methods: <strong>human-in-the-loop scoring</strong>, <strong>participatory red teaming</strong>, <strong>simulated user journeys</strong>, <strong>reflective feedback circles</strong>, and <strong>observer-based shadow scoring</strong>. Together, these methods offer a survivor-centered and emotionally attuned lens for evaluating AI systems — not only by their outputs, but by how they are experienced, trusted, and used.</p> |
| title | Beyond Compliance: Generative AI Safety Evaluation for Civil Society |
| topic | Artificial Intelligence Artificial Intelligence/standards Artificial Intelligence/ethics Community-Based Participatory Research/methods Design Justice Survivor-AI Generative AI Safety Trauma-Informed Computing LLM Evaluation Public Interest Technology AI Trust & Safety |
| url | https://doi.org/10.5281/zenodo.15570677 |