Teaching Offer at the Professorship of Societal Computing
(SOT86065, SOT86066) Machine Learning and Society
lecture
This lecture sheds light into the profound impact of machine learning (ML) on society, emphasizing the ethical, societal, and technical challenges posed by advanced algorithms. It aims to equip students with a critical understanding of the implications of ML technologies and the responsibility of practitioners in this field. 1. Algorithmic Bias Explore the concept of bias in machine learning algorithms. Understand how bias originates from data sources and algorithmic design. Examine case studies where algorithmic bias has impacted decision-making in areas like recruitment, criminal justice, and lending. 2. Fairness: Formal Definitions Introduce formal definitions of fairness in the context of machine learning. Analyze different models of fairness and their applicability in various scenarios. Discuss the trade-offs and challenges in implementing these models in real-world applications. 3. Alignment of Large Language Models (LLMs) Investigate the alignment challenges in large language models. Discuss strategies for aligning LLMs with human values and ethical norms. Evaluate the effectiveness and limitations of these alignment strategies through practical examples. 4. Red Teaming Learn about the practice of red teaming in the context of machine learning. Understand how adversarial approaches can identify vulnerabilities in ML systems. Study real-life instances where red teaming has been used to improve system robustness and safety. 5. Auditing Examine the role of auditing in ensuring the ethical use of machine learning. Discuss methodologies for conducting effective audits of ML systems. Assess the impact of regulatory and compliance requirements on ML auditing processes. 6. Limits of Prediction Investigate the theoretical and practical limits of predictive modeling in machine learning. Understand the implications of these limits on the reliability and utility of ML predictions. Explore scenarios where over-reliance on predictive models can lead to adverse outcomes. 7. Algorithmic Harms Define and categorize different types of algorithmic harms. Analyze the societal impact of these harms, with a focus on vulnerable and marginalized groups. Discuss strategies to mitigate and prevent algorithmic harms, including policy interventions and technical solutions. 8. ML in Society Understand how ML systems are influenced by and, in turn, influence social structures, norms, and values. Discuss case studies that illustrate the deep interconnection between ML applications and social dynamics. 9. ML as Society Understand the collaborative and often complex interactions between these stakeholders in the development and deployment of ML systems. Examine the roles and responsibilities of each stakeholder group, emphasizing the ethical considerations at each stage of ML development. Explore the concept of ML systems as socio-technical systems, where technical aspects are deeply intertwined with social, ethical, and organizational dimensions.
(SOT86065) Machine Learning and Society (part of 6 ECTS module)
seminar
Further information (ECTS, description of assessment methods, etc.) can be found in the module description in TUMonline.
PhD Kolloquium - Societal Computing
colloquium (free subject)
(SOT86074) Aligning Generative AI to Social Values (part of 6 ECTS module)
seminar
This module explores the intersection of generative AI and social values, focusing on ethical considerations, bias mitigation, and the development of AI systems that reflect diverse societal values. Students will engage with theoretical frameworks, practical tools, and case studies to understand and address the ethical challenges in generative AI.
(SOT86097, SOT86074) Aligning Generative AI to Social Values
lecture
This module explores the intersection of generative AI and social values, focusing on ethical considerations, bias mitigation, and the development of AI systems that reflect diverse societal values. Students will engage with theoretical frameworks, practical tools, and case studies to understand and address the ethical challenges in generative AI.
PhD Colloquium - Societal Computing
colloquium (free subject)
(SOT86065) Machine Learning and Society (6 ECTS)
lecture
This lecture sheds light into the profound impact of machine learning (ML) on society, emphasizing the ethical, societal, and technical challenges posed by advanced algorithms. It aims to equip students with a critical understanding of the implications of ML technologies and the responsibility of practitioners in this field.
(SOT86065) Machine Learning and Society (part of 6 ECTS module)
seminar
This lecture sheds light into the profound impact of machine learning (ML) on society, emphasizing the ethical, societal, and technical challenges posed by advanced algorithms. It aims to equip students with a critical understanding of the implications of ML technologies and the responsibility of practitioners in this field.
(SOT86066) Machine Learning and Society (lecture for 3 ECTS)
This lecture sheds light into the profound impact of machine learning (ML) on society, emphasizing the ethical, societal, and technical challenges posed by advanced algorithms. It aims to equip students with a critical understanding of the implications of ML technologies and the responsibility of practitioners in this field.