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Mechanics of Materials I: Fundamentals of Stress & Strain and Axial Loading
Philosophy, Science and Religion: Religion and Science
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Explores multidisciplinary approaches to identifying and addressing discrimination in online personalization systems, examining legal, technical, and ethical perspectives on algorithmic fairness.
Explore the impact of AI and technology on society, policy, and human values with Harvard's Latanya Sweeney. Examine unforeseen consequences and discuss how to shape our technological future responsibly.
Explore a framework for responsible data stewardship, focusing on dataset deprecation and ethical considerations in AI development and research.
Explore how predictive algorithms reshape legal subjectivity, challenging traditional notions of individual agency and responsibility in the justice system.
Explore perceptions of algorithmic accountability among instant loan app users in India, examining user trust, understanding, and attitudes towards AI-driven lending decisions.
Explore techniques for generating privacy-preserving explanations of machine learning models, balancing transparency with data protection in AI systems.
Explore a value-based assessment framework for algorithmic systems, considering multiple stakeholders and their perspectives on ethical and societal impacts.
Explore the critical issue of bias in automated speaker recognition systems, examining its impact and potential solutions for fairer AI-driven voice technology.
Explore key strategies for identifying, investigating, and communicating limitations in machine learning research to enhance transparency and foster responsible AI development.
Explore the political and practical aspects of disclosure datasets, examining their role in accountability and transparency in data-driven decision-making processes.
Explore centralized delegation mechanisms in liquid democracy, analyzing their properties and trade-offs for improved democratic decision-making processes.
Philosophical examination of AI algorithms' fairness towards women of color, challenging the concept of "intersectional fairness" in artificial intelligence.
Explore the limitations of individual consent and alternative models of distributed consent in online social networks, examining ethical implications and potential solutions.
Explore bounds and inference in treatment effect risk analysis, focusing on statistical methods for causal inference and risk assessment in experimental studies.
Explore ethical implications of AI predictions and fairness constraints in different contexts, examining moral distinctions and their impact on algorithmic decision-making.
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