Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Raza, Shaina Qureshi, Rizwan Zahid, Anam Muneer, Amgad Zafar, Anas Kamawal, Safiullah Sadak, Ferhat Fioresi, Joseph Saeed, Muhammaed Sapkota, Ranjan Jain, Aditya Hassan, Muneeb Ul Zafar, Aizan Maqbool, Hasan Vayani, Ashmal Wu, Jia Shoman, Maged |
| author_facet | Raza, Shaina Qureshi, Rizwan Zahid, Anam Muneer, Amgad Zafar, Anas Kamawal, Safiullah Sadak, Ferhat Fioresi, Joseph Saeed, Muhammaed Sapkota, Ranjan Jain, Aditya Hassan, Muneeb Ul Zafar, Aizan Maqbool, Hasan Vayani, Ashmal Wu, Jia Shoman, Maged |
| contents | Generative AI is rapidly moving from research to deployment, elevating the need for responsible development, evaluation, and governance. We conduct a PRISMA guided review of 232 studies (November 2022 - December 2025), spanning large language models, vision language models, diffusion models, and agentic pipelines. We make four contributions: (1) the first survey bridging governance principles, technical evaluation, and domain deployment across all four system types; (2) a ten-criterion rubric (C1-C10) scoring major AI safety benchmarks on risk-surface coverage, paired with a policy crosswalk mapping benchmarks to regulatory requirements; (3) twelve lifecycle KPIs, explainability guidance for foundation models, and a testbed catalogue; and (4) domain-specific analysis across healthcare, finance, education, arts, agriculture, and defense. Three findings emerge: benchmark coverage is dense for bias and toxicity but sparse for privacy, provenance, deepfakes, and system-level failures in agentic settings; evaluations remain largely static and task local, limiting audit portability; and inconsistent documentation complicates cross-release comparison. We outline a research agenda prioritizing adaptive multimodal evaluation, privacy and provenance testing, deepfake risk assessment, calibration reporting, versioned artifacts, and continuous monitoring. This survey offers a structured path to align generative AI evaluation with governance needs for safe and accountable deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future Raza, Shaina Qureshi, Rizwan Zahid, Anam Muneer, Amgad Zafar, Anas Kamawal, Safiullah Sadak, Ferhat Fioresi, Joseph Saeed, Muhammaed Sapkota, Ranjan Jain, Aditya Hassan, Muneeb Ul Zafar, Aizan Maqbool, Hasan Vayani, Ashmal Wu, Jia Shoman, Maged Computers and Society Generative AI is rapidly moving from research to deployment, elevating the need for responsible development, evaluation, and governance. We conduct a PRISMA guided review of 232 studies (November 2022 - December 2025), spanning large language models, vision language models, diffusion models, and agentic pipelines. We make four contributions: (1) the first survey bridging governance principles, technical evaluation, and domain deployment across all four system types; (2) a ten-criterion rubric (C1-C10) scoring major AI safety benchmarks on risk-surface coverage, paired with a policy crosswalk mapping benchmarks to regulatory requirements; (3) twelve lifecycle KPIs, explainability guidance for foundation models, and a testbed catalogue; and (4) domain-specific analysis across healthcare, finance, education, arts, agriculture, and defense. Three findings emerge: benchmark coverage is dense for bias and toxicity but sparse for privacy, provenance, deepfakes, and system-level failures in agentic settings; evaluations remain largely static and task local, limiting audit portability; and inconsistent documentation complicates cross-release comparison. We outline a research agenda prioritizing adaptive multimodal evaluation, privacy and provenance testing, deepfake risk assessment, calibration reporting, versioned artifacts, and continuous monitoring. This survey offers a structured path to align generative AI evaluation with governance needs for safe and accountable deployment. |
| title | Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2502.08650 |