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Found 8 papers, 4 papers with code
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs, or by manipulating the training data.
A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions
The ML framework guides how child welfare agencies might conceptualize a target problem that ML can solve; vet available administrative data for building ML; formulate and develop ML specifications that mirror relevant populations and interventions the agencies are undertaking; deploy, evaluate, and monitor ML as child welfare context, policy, and practice change over time.
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in their design, resulting in limited conclusions of methods' real-world utility.
An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings
Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems.
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy
Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities.
Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions
The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses.
A Clinical Approach to Training Effective Data Scientists
Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems.
Aequitas: A Bias and Fairness Audit Toolkit
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics.
