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AI Ethics, Bias, Safety & Governance
Algorithmic fairness, explainability, hallucination, and red-teaming
As AI systems take on critical societal roles in hiring, criminal justice, and medicine, understanding algorithmic bias, model explainability, and safety alignment is mandatory.
Key Challenges in AI Governance
Training Data Bias: Models inherit historical discrimination present in human training corpora. If hiring data historically favored a demographic, an unaligned model will penalize non-majority applicants.
Explainability & The Black Box: Deep neural networks have billions of uninterpretable weights. Explainable AI (XAI) tools like SHAP and LIME attribute prediction importance to individual input features.
Hallucination and Groundedness: Generative models probabilistically generate fluent assertions that have no factual grounding. Mitigation strategies include Retrieval-Augmented Generation (RAG) and self-consistency verification.
Red-teaming involves adversarial practitioners intentionally probing models with jailbreaks and edge cases to identify security and safety vulnerabilities prior to deployment.
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Test Yourself With Exercise
What technique uses adversarial prompting and intentional stress testing to discover vulnerabilities before deploying an AI model?