Fair and unbiased algorithmic decision making: Current state and future challenges
S Tolan - arXiv preprint arXiv:1901.04730, 2019 - arxiv.org
Machine learning algorithms are now frequently used in sensitive contexts that substantially
affect the course of human lives, such as credit lending or criminal justice. This is driven by …
affect the course of human lives, such as credit lending or criminal justice. This is driven by …
Detecting disparities in police deployments using dashcam data
Large-scale policing data is vital for detecting inequity in police behavior and policing
algorithms. However, one important type of policing data remains largely unavailable within …
algorithms. However, one important type of policing data remains largely unavailable within …
Artificial intelligence and algorithmic bias: Source, detection, mitigation, and implications
Artificial intelligence and machine learning (ML) algorithms are widely used throughout our
economy in making decisions that have far-reaching impacts on employment, education …
economy in making decisions that have far-reaching impacts on employment, education …
Estimated childhood lead exposure from drinking water in Chicago
Importance There is no level of lead in drinking water considered to be safe, yet lead service
lines are still commonly used in water systems across the US. Objective To identify the …
lines are still commonly used in water systems across the US. Objective To identify the …
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing
Decision-makers often observe the occurrence of events through a reporting process. City
governments, for example, rely on resident reports to find and then resolve urban …
governments, for example, rely on resident reports to find and then resolve urban …
Domain constraints improve risk prediction when outcome data is missing
Machine learning models are often trained to predict the outcome resulting from a human
decision. For example, if a doctor decides to test a patient for disease, will the patient test …
decision. For example, if a doctor decides to test a patient for disease, will the patient test …
A causal framework for observational studies of discrimination
In studies of discrimination, researchers often seek to estimate a causal effect of race or
gender on outcomes. For example, in the criminal justice context, one might ask whether …
gender on outcomes. For example, in the criminal justice context, one might ask whether …
Ai and algorithmic bias: Source, detection, mitigation and implications
Artificial intelligence (AI) and machine learning (ML) algorithms are widely used throughout
our economy in making decisions that have far-reaching impacts on employment, education …
our economy in making decisions that have far-reaching impacts on employment, education …
Blind justice: Algorithmically masking race in charging decisions
A prosecutor's decision to charge or dismiss a criminal case is a particularly high-stakes
choice. There is concern, however, that these judgements may suffer from explicit or implicit …
choice. There is concern, however, that these judgements may suffer from explicit or implicit …