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Dangers of Bias in Data-Intensive Information Systems

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Next Generation Information Processing System

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1162 ))

Abstract

Data-intensive information systems (DIS) are pervasive and virtually affect people in all walks of life. Artificial intelligence and machine learning technologies are the backbone of DIS systems. Various types of biases embedded into DIS systems have serious significance and implications for individuals as well as the society at large. In this paper, we discuss various types of bias—both human and machine—and suggest ways to eliminate or minimize it. We also make a case for digital ethics education and outline ways to incorporate such education into computing curricula.

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Notes

  1. 1.

    http://tarotcardsoftech.artefactgroup.com/.

  2. 2.

    https://ai.google/research/teams/brain/pair.

  3. 3.

    https://homes.cs.washington.edu/~marcotcr/blog/lime/.

  4. 4.

    https://developer.ibm.com/open/projects/ai-fairness-360/.

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Author information

Authors and Affiliations

  1. Center for Survey Research, East Carolina University, Greenville, NC, 27855, USA

    Baekkwan Park

  2. Department of Biology, East Carolina University, Greenville, NC, 27855, USA

    Dhana L. Rao

  3. Department of Computer Science, East Carolina University, Greenville, NC, 27855, USA

    Venkat N. Gudivada

Authors
  1. Baekkwan Park
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  2. Dhana L. Rao
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  3. Venkat N. Gudivada
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Corresponding author

Correspondence to Venkat N. Gudivada .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Prachi Deshpande

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  4. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Park, B., Rao, D.L., Gudivada, V.N. (2021). Dangers of Bias in Data-Intensive Information Systems. In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore. https://doi.org/10.1007/978-981-15-4851-2_28

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