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Ethics and Bias in Machine Learning: A Technical Study of What Makes Us “Good”

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Abstract

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the highest algorithmic functionality. To assist in determining the best solution, we turn to machine ethics.

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Authors and Affiliations

  1. John Jay College of Criminal Justice, New York City, NY, USA

    Nicole Shadowen

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  1. Nicole Shadowen
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Editors and Affiliations

  1. California Transhumanist Party, Los Angeles, CA, USA, Institute for Education Research, and Scholarships, Los Angeles, CA, USA

    Newton Lee

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Shadowen, N. (2019). Ethics and Bias in Machine Learning: A Technical Study of What Makes Us “Good”. In: Lee, N. (eds) The Transhumanism Handbook. Springer, Cham. https://doi.org/10.1007/978-3-030-16920-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-16920-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16919-0

  • Online ISBN: 978-3-030-16920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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