AI Ethics: Bias, Responsibility, and Accountability

Related lectures (30)

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Delves into the challenges of ensuring fairness in algorithmic decision-making processes, addressing biases and historical injustices in data.

Explores the ethical implications of deploying machine learning algorithms and emphasizes the importance of fairness in decision-making processes.

Covers ethical considerations in NLP, focusing on bias, toxicity, and misinformation.

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Explores data bias, fairness, and justice in AI systems, highlighting the ethical challenges and considerations involved.

Explores data ethics, focusing on fairness, justice, and Al systems' broader implications.

Discusses trustworthy AI, ethical principles, and US AI regulation.

Introduces key human rights concepts, explores discrimination, social norms, and the impact of algorithms on rights.

Explores global data justice, social fairness in data governance, algorithmic ethics, and the political responsibility of researchers.

Explores legal and ethical aspects of AI, focusing on autonomy, responsibility, and societal impact.