Exploring the ethical implications of business analytics with a business ethics canvas

Vidgen, Richard; Hindle, Giles; Randolph, Ian

Abstract

The ethical aspects of data science and artificial intelligence have become a major issue. Organisations that deploy data scientists and operational researchers (OR) must address the ethical implications of their use of data and algorithms. We review the OR and data science literature on ethics and find that this work is pitched at the level of guiding principles and frameworks and fails to provide a practical and grounded approach that can be used by practitioners as part of the analytics development process. Further, given the advent of the General Data Protection Regulation (GDPR) an ethical dimension is likely to become an increasingly important aspect of analytics development. Drawing on the business analytics methodology (BAM) developed by Hindle and Vidgen (2018) we tackle this challenge through action research with a pseudonymous online travel company, EuroTravel. The method that emerges uses an opportunity canvas and a business ethics canvas to explore value creation and ethical aspects jointly. The business ethics canvas draws on the Markkula Center's five ethical principles (utility, rights, justice, common good, and virtue) to which explicit consideration of stakeholders is added. A contribution of the paper is to show how an ethical dimension can be embedded in the everyday exploration of analytics development opportunities, as distinct from a stand-alone ethical decision-making tool or as an overlay of a general set of guiding principles. We also propose that value and ethics should not be viewed as separate entities, rather they should be seen as inseparable and intertwined.

Citation

Vidgen, R., Hindle, G., & Randolph, I. (2020). Exploring the ethical implications of business analytics with a business ethics canvas. European journal of operational research, 281(3), 491-501. https://doi.org/10.1016/j.ejor.2019.04.036

• Journal Article Type: Article
• Acceptance Date: Apr 24, 2019
• Online Publication Date: Apr 27, 2019
• Publication Date: Mar 16, 2020
• Deposit Date: Jan 29, 2021
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 281
• Issue: 3
• Pages: 491-501
• DOI: https://doi.org/10.1016/j.ejor.2019.04.036
• Keywords: Business analytics; Data science; Business ethics canvas; Markkula; GDPR
• Public URL: https://hull-repository.worktribe.com/output/3598684
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S037722171930373X?via%3Dihub
• Related Public URLs: https://eprints.bbk.ac.uk/id/eprint/27315/