AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry

Author:

Belenguer LorenzoORCID

Funder

No funding was received

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Reference104 articles.

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2. Angwin, J., et al.: (2016) Machine bias: there’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing (2016). Accessed 28 Mar 2021

3. Alpaydin, E. (2020). Introduction to machine learning. MIT press.

4. Bageri, V., Katsoulacos, Y., Spagnolo, G.: The distortive effects of antitrust fines based on revenue. Econ. J. 123(572), F545–F557 (2013)

5. Bagilhole, B.: Being different is a very difficult row to hoe: survival strategies of women academics. In: Davies, S., Lubelska, C., Quinn, J. (eds.) Changing the Subject, pp. 15–28. Taylor & Francis, London (2017)

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