# Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future

arXiv: https://arxiv.org/abs/2502.08650 · HTML: https://mlsanity.com/paper/2502.08650/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Built on by (most cited first, 12 of 12)

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- [Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs](https://mlsanity.com/paper/2511.01187/) (2025)
- [SafeR-CLIP: Mitigating NSFW Content in Vision-Language Models While Preserving Pre-Trained Knowledge](https://mlsanity.com/paper/2511.16743/) (2025)
- [Learning to Share: Selective Memory for Efficient Parallel Agentic Systems](https://mlsanity.com/paper/2602.05965/) (2026)
- [DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images](https://mlsanity.com/paper/2504.14108/) (2025)
- [MedRoute: RL-Based Dynamic Specialist Routing in Multi-Agent Medical Diagnosis](https://mlsanity.com/paper/2604.06180/) (2026)
- [Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models](https://mlsanity.com/paper/2604.08815/) (2026)

## Abstract (arXiv)

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.