AI Ethics

Safeguarding AI Accountability: Assigning responsibility for AI outcomes. Critical in AI’s development, deployment, and use. Accountability is vital People trust AI systems more when there is accountability. Accountability ensures ethical use and mitigates potential harm. AI is a tool, not the final decision-maker. The paradox of accountability Increasing AI accountability can improve trust, yet extensive trust in AI can lead to misguided decisions. Example: Georgia Tech study, The tesla story Achieving accountability involves transparency and solving black box problem. Attributing reponsibility is the key. AI consumers can trust the models and outcomes, but they must verify too. Explainable AI (XAI) AI systems whose internal workings are understood by humans. Goal: Making AI decision-making clear, understandable, and explainable. The Central Pillars Transparency, fairness, and accountability are central. AI conclusions should be accessible and logical to humans