Are "Intersectionally Fair" AI Algorithms Really Fair to Women of Color? - A Philosophical Analysis
Philosophical examination of AI algorithms' fairness towards women of color, challenging the concept of "intersectional fairness" in artificial intelligence.
Algorithmic Fairness and Vertical Equity - Income Fairness with Tax Audit Models
Explore income fairness in tax audit models, examining algorithmic fairness and vertical equity principles to enhance equitable tax enforcement strategies.
How to Fix AI - Solutions to ML Bias - And Why They Don't Matter
Exploring ML bias solutions and their limitations. Presents deep learning techniques for combating data bias, discusses algorithmic fairness, and challenges assumptions about AI bias solutions through real-world case studies.
Group Fairness - Independence Revisited
Explore the concept of group fairness and independence in algorithmic decision-making, examining its implications, limitations, and potential improvements for ethical AI systems.
Fairness, Rankings, and Behavioral Biases
Exploring fairness in ranking systems, behavioral biases, and interventions to improve diversity and equity in selection processes through mathematical modeling and analysis.
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
Exploring challenges and design opportunities for fairness and accountability in algorithmic public sector decision-making, with insights from machine learning practitioners across OECD countries.
Privacy Governance and Explainability in ML - AI
Explore privacy governance in ML/AI, addressing GDPR compliance, explainability challenges, bias identification, and strategies for enhancing consumer trust in complex data-driven decision-making processes.
Trust Me, I'm a Data Scientist - Ethics for Builders of Data-Based Applications
Explore ethical pitfalls in data science through a practical example, covering algorithmic fairness, model interpretability, and handling minority classes in AI applications.
Do Large Language Models Have a Duty to Tell the Truth?
Explore the ethical implications of LLMs' "careless speech" and potential solutions. Gain insights on AI ethics, misinformation risks, and proposed legal duties for truthful AI responses.
Stop Explaining Black Box ML Models for High Stakes Decisions and Use Interpretable Models
Explore the societal impact of black box ML models in high-stakes decisions and discover the benefits of using interpretable models for transparent decision-making.
Exploring Pillars of Trustworthy AI: Robustness and Fairness
Discover how to build robust and fair AI models by exploring vulnerabilities, implementing defense strategies, and addressing bias in machine learning systems through practical techniques.
Practical Individual Fairness Algorithms in Machine Learning
Explore algorithmic fairness in machine learning, focusing on Distributional Individual Fairness. Learn to train certifiably fair models using transport-based regularization for various ML tasks.
Black Loans Matter: Algorithms for Racial Justice in Financial Services
Explore AI fairness in lending, focusing on subgroup discrimination and proposing Distributionally Robust Fairness with SenSR algorithm to combat racial bias in financial services.
Fairness in Algorithmic Decision Making for Financial Services
Explore fairness in AI algorithms for financial services, addressing bias detection and mitigation to ensure ethical decision-making.
Responsible Data Management - Ethics and Legal Compliance in Data-Driven Systems
Explore ethical considerations in data-driven systems, focusing on responsible data collection, preparation, and lifecycle management for building fair and interpretable AI solutions.
Fostering Cooperation via Fairness in AI Systems
Explore fairness in AI decision-making, addressing efficiency, elicitation, and incentives. Learn theoretical foundations for fairness in voting, resource allocation, and machine learning.
Algorithms in Unjust Systems
Explore the impact of algorithms in unjust systems with Alexandra Chouldechova's keynote talk at UAI 2023, addressing critical issues in AI and fairness.
Algorithms are not Evil, they are just Clever | Aadya H | TEDxSoundarya Central School Youth
Ever blamed an algorithm for being “evil” when it’s just doing exactly what it was taught? Meet Aadya.H, a curious and observant thinker who thrives on exploring unanswered questions. She experiments, learns through trial and error, and reflects on how people and systems evolve. In her talk, “Algori