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Evidence record 14115 · automatically gathered

Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM

arXiv:2508.05775v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language generation and understanding. Meanwhile, they pose risks by inadvertently producing toxic, offensive, or biased content. This dual role of LLMs, both as powerful tools for text generation and as potential sources of harmful language, presents a pressing sociotechnical challenge. In this survey

Record details

Published: 29 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness
Retrieved: 29 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening

arXiv cs.CY · 29 July 2026

Shared topic: Bias & fairness

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arXiv cs.CY · 29 July 2026

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

Parameterized Fair Resource Allocation under Diversity Constraints

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Shared topic: Bias & fairness

ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform

arXiv · 29 July 2026

Shared topic: Bias & fairness

Learning faults in time: sequential behavioural modelling for complex fault detection in multi-robot systems

Frontiers in Robotics and AI · 29 July 2026

How to cite this record

ethics.ai (29 July 2026), “Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM,” evidence record 14115, https://ethics.ai/record/14115 (originally published by arXiv cs.CY).

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