Anonymity and Accountability

Related lectures (32)

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

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

Explores the development of faster architectures for saliency prediction using deep networks and pruning methods.

Delves into the impact of bias in machine learning models and the importance of evaluating potential harm in developing such systems.

Discusses anonymity, dark side issues, mitigation strategies, and governance in online forums.

Addresses the significance of framing and narratives in AI ethics and their impact on technology development and societal perceptions.

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Introduces the Critical Data Studies course, covering data models, trust, reproducibility, biases, and ethics.

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.

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