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1
Introduction
2
Group fairness notions
3
Machine learning pipeline
4
Fairness gerrymandering
5
Intersectionality
6
Fairness vs Accuracy
7
Remarks
8
Discussion
9
Transparency
10
Barriers of Meaning
11
Arnold Public Safety Assessment
12
Barrier of Meaning
13
Study Results
14
Accuracy
15
Predictive Policing
16
What happens to the algorithm
17
Rearrest rates
18
Questions
19
Question for Chris
20
Question for Michael
Description:
Explore a virtual workshop panel discussion featuring computer scientists Michael Kearns and Cristopher Moore as they delve into the complex relationship between algorithms and social justice. Examine concepts such as group versus individual fairness, intersectionality, risk assessment, and pretrial detention. Gain insights into fairness notions in machine learning pipelines, fairness gerrymandering, and the trade-offs between fairness and accuracy. Analyze the barriers of meaning in algorithmic decision-making, focusing on the Arnold Public Safety Assessment and its implications. Investigate predictive policing, rearrest rates, and the evolving nature of algorithms in the justice system. Engage with thought-provoking questions addressed to both speakers, enhancing your understanding of the potential for algorithms to influence societal equity.

Can Algorithms Bend the Arc Toward Justice?

Santa Fe Institute
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