3 pernicious myths of responsible AI
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Responsible AI (RAI) is needed now more than ever. It is the key to driving everything from trust and adoption, to managing LLM hallucinations and eliminating toxic generative AI content. With effective RAI, companies can innovate faster, transform more parts of the business, comply with future AI regulation, and prevent fines, reputational damage, and competitive stagnation.
Unfortunately, confusion reigns as to what RAI actually is, what it delivers, and how to achieve it, with potentially catastrophic effects. Done poorly, RAI initiatives stymie innovation, creating hurdles that add delays and costs without actually improving safety. Well-meaning, but misguided, myths abound regarding the very definition and purpose of RAI. Organizations must shatter these myths if we are to turn RAI into a force for AI-driven value creation, instead of a costly, ineffectual time sink.
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