Reducing race-based bias and increasing recidivism prediction accuracy by using past criminal history details

Author:

Jain Bhanu1,Huber Manfred1,Elmasri Ramez A.1,Fegaras Leonidas1

Affiliation:

1. University of Texas at Arlington

Publisher

ACM

Reference20 articles.

1. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica (2016). Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica (2016).

2. Julia Dressel and Hany Farid. 2018. The accuracy fairness and limits of predicting recidivism. Science advances 4 1 (2018) eaao5580. Julia Dressel and Hany Farid. 2018. The accuracy fairness and limits of predicting recidivism. Science advances 4 1 (2018) eaao5580.

3. Matthew R Durose Alexia D Cooper and Howard N Snyder. [n.d.]. Recidivism of prisoners released in 30 states in 2005: Patterns from 2005 to 2010. Matthew R Durose Alexia D Cooper and Howard N Snyder. [n.d.]. Recidivism of prisoners released in 30 states in 2005: Patterns from 2005 to 2010.

4. Out With the Old and in With the New? An Empirical Comparison of Supervised Learning Algorithms to Predict Recidivism

5. Fairness through awareness

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