AI and Ethics

  • Francesco CoreaEmail author
Part of the Studies in Big Data book series (SBD, volume 50)


This chapter analyzes issues that are usually not associated with the business performance but that instead have a profound impact on the company’s financial results. Ethics is indeed a strong component in the algorithmic development and should be managed with care. The chapter will discuss the most common ethics problems and data biases and propose some food for thoughts rather than solutions. It will also talk about the control problem, the accounting and explainability issues, and the development of a safe AI.


  1. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete Problems in AI Safety. arXiv:1606.06565v2.
  2. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology, 144(1), 114–126.CrossRefGoogle Scholar
  3. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2016). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Available at SSRN: or

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ManagementCa’ Foscari UniversityVeniceItaly

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