Trust in Numbers: An Ethical (and Practical) Standard for Identity-Driven Algorithms

Author:

Kiser Mike

Publisher

Springer Science and Business Media LLC

Subject

General Energy

Reference16 articles.

1. S. Miletich, “Seattle law-school grad’s bright future outshines her rough past, state high court says,” 5 April 2018. [Online]. Available: https://www.seattletimes.com/seattle-news/seattle-law-school-grads-bright-future-outshines-her-rough-past-state-high-court-says/.

2. D. Jackson, “The Netflix Prize: How a $1 Million Contest Changed Binge-Watching Forver,” 7 7 2017. [Online]. Available: https://www.thrillist.com/entertainment/nation/the-netflix-prize.

3. K. Waddell, “The Atlantic,” 2 December 2016. [Online]. Available: https://www.theatlantic.com/technology/archive/2016/12/how-algorithms-can-bring-down-minorities-credit-scores/509333/.

4. J. Angwin, J. Larson, S. Mattu and L. Kirchner, “Machine Bias,” 23 May 2016. [Online]. Available: [1] ProPublica, “Machine Bias,” ProPublica, 23-May-2016. [Online]. Available: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

5. A. Cutler, M. Pribić and L. Humphrey, “Everyday Ethics for Artifical Intelligence,” IBM Corp, New York City, 2019.

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