Ethics and Fairness in Machine Learning

Related lectures (31)

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

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

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

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

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Delves into ethics and law in AI, focusing on values, normative ambitions, and ethical challenges.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers empirical risk minimization, statistical learning, and examples of cancer prediction, house pricing, and image generation.

Explores challenges in developing normative news recommender systems and the ethical considerations in AI, journalism, and diversity.