Can Algorithms Bend the Arc Toward Justice?
Explore algorithmic fairness, intersectionality, and the challenges of balancing accuracy with equity in machine learning and public safety applications.
AI in Radiology: Pattern Detection, Fairness, and Clinical Applications - Lecture Series
Explore AI's ability to detect hidden patterns in X-ray images and its implications for radiologists. Delve into fairness considerations in chest X-ray diagnosis models and their potential impacts on clinical practice.
Fair Clustering: Notations and Algorithms
Explore fair clustering algorithms and their role in preventing discriminatory outcomes in machine learning applications, focusing on group fairness and equal representation across clusters.
The Limits of Group Fairness and Predictive Multiplicity
Explore information-theoretic approaches to group fairness and predictive multiplicity in machine learning, examining their limitations and implications for trustworthy AI systems.
Cynthia Dwork - Group Fairness and Individual Fairness - IPAM at UCLA
Explore group and individual fairness in algorithmic decision-making, examining their definitions, advantages, and limitations in the context of ethical AI and machine learning applications.
Translation Tutorial - A History of Quantitative Fairness in Testing
Explores historical quantitative fairness definitions in education and hiring, translating 1960s-70s concepts to modern machine learning notation and highlighting challenges for current fairness research.
Decoupled Classifiers for Group-Fair and Efficient Machine Learning
Explore group-fair machine learning with decoupled classifiers, balancing efficiency and fairness in algorithmic decision-making. Gain insights into innovative approaches for ethical AI development.
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.
Measurement and Fairness
Explore the intersection of measurement and fairness in computing, examining reliability, validity, and group fairness concepts to enhance ethical decision-making in technology.
Removing Spurious Features Can Hurt Accuracy and Affect Groups Disproportionately
Explore the impact of removing spurious features on model accuracy and group fairness in machine learning, examining potential unintended consequences and ethical implications.
Chasing Your Long Tails - Differentially Private Prediction in Health Care Settings
Explore differential privacy in healthcare ML, addressing unique challenges, extreme tradeoffs, and fairness considerations for underrepresented groups in medical datasets.
One Label, One Billion Faces - Usage and Consistency of Racial Categories in Computer Vision
Explores racial categorization in computer vision, examining usage, consistency, and implications for fairness in AI systems. Highlights challenges and potential biases in facial recognition technology.
Sequential Learning in Artificial Intelligence - Oral Session 4
Explore cutting-edge research in sequential learning, covering robust reinforcement learning, online learning in MDPs, fairness in predict-then-optimize settings, and constrained POMDPs.