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Bias and Discrimination in Artificial Intelligence: Emergence and Impact in E-Business

Bias and Discrimination in Artificial Intelligence: Emergence and Impact in E-Business

Jan C. Weyerer, Paul F. Langer
Copyright: © 2020 |Pages: 28
ISBN13: 9781799818793|ISBN10: 1799818799|ISBN13 Softcover: 9781799851486|EISBN13: 9781799818809
DOI: 10.4018/978-1-7998-1879-3.ch011
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MLA

Weyerer, Jan C., and Paul F. Langer. "Bias and Discrimination in Artificial Intelligence: Emergence and Impact in E-Business." Interdisciplinary Approaches to Digital Transformation and Innovation, edited by Rocci Luppicini, IGI Global, 2020, pp. 256-283. https://doi.org/10.4018/978-1-7998-1879-3.ch011

APA

Weyerer, J. C. & Langer, P. F. (2020). Bias and Discrimination in Artificial Intelligence: Emergence and Impact in E-Business. In R. Luppicini (Ed.), Interdisciplinary Approaches to Digital Transformation and Innovation (pp. 256-283). IGI Global. https://doi.org/10.4018/978-1-7998-1879-3.ch011

Chicago

Weyerer, Jan C., and Paul F. Langer. "Bias and Discrimination in Artificial Intelligence: Emergence and Impact in E-Business." In Interdisciplinary Approaches to Digital Transformation and Innovation, edited by Rocci Luppicini, 256-283. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1879-3.ch011

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Abstract

Artificial intelligence (AI) has become an integral part of e-business and our lives, promising significant benefits to e-business companies and society. However, at the same time, AI systems in e-business may produce biased outcomes, leading to discrimination of minorities and violating human rights. Against this background, this chapter first describes the foundations of bias and discrimination in AI, highlighting its scientific and practical relevance, as well as describing its meaning, emergence, functioning, and impact in the context of e-business. Based on these foundations, the chapter further provides implications for research and practice on how to deal with AI-related bias and discrimination in the future, opening up future research directions as well as outlining solutions and recommendations for eliminating and preventing AI-related bias and discrimination in e-business.

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