Implications Tutorial - Building Community Governance of Risk Assessment
Exploring community-driven auditing of pretrial risk assessment algorithms, addressing concerns and developing socially informed validation processes for fair decision-making frameworks.
Learning Algorithms
Exploration of fairness in machine learning algorithms, focusing on disparity assessment, classification with fairness constraints, and comparative studies of fairness-enhancing interventions.
Beyond Algorithmic Scapegoating - Fostering Cultures of Algorithmic Responsibility Through Administrative Law and Design
Explore algorithmic responsibility in administrative law and design, addressing challenges, interventions, and strategies for fostering public participation in technology governance.
Systems and Measurement
Exploring online A/B testing, data sharing ethics, fairness in programming, and model performance reporting. Insights into cutting-edge research on responsible AI and data science practices.
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.
The Cost of Fairness in Binary Classification
Explore the cost of fairness in binary classification, analyzing risk, optimal classifiers, and fairness frontiers with implications for stereotypes and decision-making.
Gender Shades - Intersectional Accuracy Disparities in Commercial Gender Classification
Explore intersectional accuracy disparities in commercial gender classification systems, examining biases and implications for AI fairness and ethics in facial recognition technology.
Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies
Explore techniques to identify and mitigate gender stereotypes in Bollywood cinema, focusing on data analysis, detection methods, and strategies for promoting more balanced representations.
Meaningful Information and the Right to Explanation
Explore the right to explanation in automated decision-making, focusing on GDPR's Article 22 and its implications for AI systems and data protection regulations.
Interpretable Active Learning
Explore interpretable active learning techniques for machine learning models, focusing on transparency and understanding of model decisions in real-world applications.
Interventions over Predictions - Reframing the Ethical Debate for Actuarial Risk Assessment
Reframing ethical debates on actuarial risk assessment, focusing on interventions over predictions. Explores historical context, machine learning implications, and causal inference in risk evaluation.
Potential for Discrimination in Online Targeted Advertising
Explores potential discrimination in online targeted advertising, focusing on Facebook's ad targeting mechanisms and their implications for sensitive attributes and audience selection.
Towards Intersectional Feminist and Participatory ML - A Case Study in Supporting Feminicide Counterdata Collection
Explore intersectional feminist approaches to machine learning through a case study on feminicide data collection, addressing biases and promoting participatory methods.
Subverting Fair Image Search with Generative Adversarial Perturbations
Explore techniques to manipulate fair image search algorithms using adversarial perturbations, examining methods, experiments, and implications for algorithmic fairness.
Models for Classifying AI Systems - The Switch, the Ladder, and the Matrix
Explore three models for classifying AI systems: the Switch, the Ladder, and the Matrix. Gain insights into AI governance and evaluation criteria for effective classification frameworks.
What is Proxy Discrimination?
Explore automated decision-making, proxy discrimination, and its various forms, including causal descendants, influence, and capacity-induced use in algorithmic systems.
Can You Fake It Until You Make It? - Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness
Explore the impact of differentially private synthetic data on classification fairness, examining its potential for preserving privacy while maintaining data utility in machine learning.
The Algorithmic Leviathan - Arbitrariness, Unfairness, and Opportunity in Algorithmic Decision Making
Explore the ethical implications of algorithmic decision-making systems, focusing on arbitrariness, unfairness, and potential solutions in automated processes across various domains.
How Can I Choose an Explainer? An Application-Grounded Evaluation of Post-Hoc Explanations
Evaluates post-hoc explanations for machine learning models, comparing different explainers to help practitioners choose the most suitable one for their specific application and context.
The Ethics of Emotion in Artificial Intelligence Systems
Explore ethical implications of emotion in AI systems, covering theories, proxy data, and concerns. Gain insights into the intersection of AI, emotions, and ethics.
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.