Tag: fairness (21 references)
The Illusion of Artificial Inclusion
Terms-we-serve-with: Five dimensions for anticipating and repairing algorithmic harm
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems. Based on a scoping review of computing research (n=172), we present an applied taxonomy of sociotechnical harms to support a more systematic surfacing of potential harms in algorithmic systems. The final taxonomy builds on and refers to existing taxonomies, classifications, and terminologies. Five major themes related to sociotechnical harms — representational, allocative, quality-of-service, interpersonal harms, and social system/societal harms — and sub-themes are presented along with a description of these categories. We conclude with a discussion of challenges and opportunities for future research.
Datamodels: Predicting Predictions from Training Data
Proposes datamodels that predict model outputs as a function of training data subsets, providing a framework for understanding data attribution through retraining experiments.
The Fallacy of AI Functionality
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Excavating AI: The Politics of Images in Machine Learning Training Sets
Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects
Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations
Fairness and Abstraction in Sociotechnical Systems
A Reductions Approach to Fair Classification
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Big Data's Disparate Impact
Causal Inference in Statistics, Social, and Biomedical Sciences
Comprehensive treatment of causal inference methods for observational and experimental data. Covers randomized experiments, matching, propensity scores, instrumental variables, and regression discontinuity designs.
Causality: Models, Reasoning, and Inference
Foundational book on causal inference introducing structural causal models, do-calculus, and counterfactual reasoning. Unifies graphical models with potential outcomes framework. Second edition with expanded coverage.
Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
Comprehensive overview of the potential outcomes framework for causal inference. Covers experimental design, observational studies, propensity scores, and the fundamental problem of causal inference.