arXiv:2606.05857v1 Announce Type: new Abstract: Handling toxic retrieval in text-to-audio systems is challenging due to contextual dependencies. Existing strategies (e.g., rephrasing, summarization) risk altering intent or omitting details. We propose a post hoc causal debiasing framework with a sentiment-controlled mediator to preserve semantic relevance while suppressing harmful speech. Our approach is model-agnostic and integrates seamlessly with existing retrieval pipelines. We introduce two variants: Forgive, which re-ranks and filters toxic audio via logit adjustment, and Forget, which g
The proliferation of generative AI systems, particularly text-to-audio, necessitates advanced mechanisms to manage harmful or toxic outputs without compromising utility or intent.
This research addresses a critical ethical and practical challenge in AI development by proposing methods to mitigate bias and toxicity, which is vital for widespread AI adoption and responsible deployment.
AI systems can now potentially integrate more sophisticated, context-aware debiasing frameworks that preserve semantic relevance while filtering harmful content, moving beyond crude filtering mechanisms.
- · AI developers
- · Content moderation platforms
- · Ethical AI research
- · Platforms with unsophisticated content filters
- · Makers of bias propagation tools
Improved safety and usability of text-to-audio AI systems, particularly in sensitive applications.
Increased public trust in AI technologies as developers demonstrate commitment to responsible and ethical design.
New regulatory pressures on AI systems to incorporate similar sophisticated debiasing techniques, setting a higher standard for 'safe AI'.
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Read at arXiv cs.CL