Explore algorithmic decision-making and fairness in this Simons Institute symposium talk. Delve into the challenges of identifying bias in algorithmic decisions, focusing on a case study of pre-trial decision-making. Examine the limitations of benchmark tests and outcome tests, and understand the concept of infra-marginality. Investigate how to identify bias in human decisions and compare it to algorithmic decision-making. Analyze evidence from Broward County and discuss potential fairness concerns, including redlining and the insufficiency of calibration. Learn about sample bias, label bias, and subgroup validity. Evaluate the use of protected characteristics and statistical parity as measures of fairness. Understand the optimal rule for decision-making and the trade-offs between different fairness criteria. Draw analogies to tests for discrimination and explore the limitations of false positive rates. Gain insights into making fair decisions with algorithms and recognize the limitations of current approaches. Read more