Fairness, accountability and transparency: notes on algorithmic decision-making in criminal justice

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The resu...

On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i...

Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users ...

A Primer on Fairness in Criminal Justice Risk Assessments

Fairness and Entitlement in Majoritarian Decision-Making*

We experimentally test competing theories of three-player majoritarian bargaining models with fixed, known disagreement values. Subjects are randomly assigned to three roles: a proposer and two types of voters. Each role is randomly assigned a disagreement value, i.e. a given amount of money he/she will receive if the proposal is rejected. These values are known to all players before any decisi...