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Tag: joshi
Bibliography items where occurs: 10
- The Cambridge Law Corpus: A Corpus for Legal AI Research / 2309.12269 / ISBN:https://doi.org/10.48550/arXiv.2309.12269 / Published by ArXiv / Version released on 2024-01-01 / on (web) Publishing site
- Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates / 2312.06861 / ISBN:https://doi.org/10.48550/arXiv.2312.06861 / Published by ArXiv / Version released on 2023-12-11 / on (web) Publishing site
- The Narrow Depth and Breadth of Corporate Responsible AI Research / 2405.12193 / ISBN:https://doi.org/10.48550/arXiv.2405.12193 / Published by ArXiv / Version released on 2024-05-20 / on (web) Publishing site
- Bots against Bias: Critical Next Steps for Human-Robot Interaction / 2412.12542 / ISBN:https://doi.org/10.1017/9781009386708.023 / Published by ArXiv / Version released on 2024-12-17 / on (web) Publishing site
- Safety at Scale: A Comprehensive Survey of Large Model Safety / 2502.05206 / ISBN:https://doi.org/10.48550/arXiv.2502.05206 / Published by ArXiv / Version released on 2025-03-19 / on (web) Publishing site
- AI Literacy for Legal AI Systems: A practical approach / 2505.18006 / ISBN:https://doi.org/10.48550/arXiv.2505.18006 / Published by ArXiv / Version released on 2025-05-23 / on (web) Publishing site
- A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications / 2506.12594 / ISBN:https://doi.org/10.48550/arXiv.2506.12594 / Published by ArXiv / Version released on 2025-06-14 / on (web) Publishing site
- Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection / 2506.17494 / ISBN:https://doi.org/10.48550/arXiv.2506.17494 / Published by ArXiv / Version released on 2025-06-20 / on (web) Publishing site
- Context, Credibility, and Control: User Reflections on AI Assisted Misinformation Tools
/ 2506.22940 / ISBN:https://doi.org/10.48550/arXiv.2506.22940 / Published by ArXiv / Version released on 2025-06-28 / on (web) Publishing site
- AI Human Impact: Toward a Model for Ethical Investing in AI-Intensive Companies / 2507.07703 / ISBN:https://doi.org/10.48550/arXiv.2507.07703 / Published by ArXiv / Version released on 2025-07-10 / on (web) Publishing site