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

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  • 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.

Suggested Citation

  • Vidgen, Richard & Hindle, Giles & Randolph, Ian, 2020. "Exploring the ethical implications of business analytics with a business ethics canvas," European Journal of Operational Research, Elsevier, vol. 281(3), pages 491-501.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:491-501
    DOI: 10.1016/j.ejor.2019.04.036
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    2. Xueling Li & Yujie Long & Meixi Fan & Yong Chen, 2022. "Drilling down artificial intelligence in entrepreneurial management: A bibliometric perspective," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 379-396, May.
    3. Bonsón, Enrique & Bednárová, Michaela & Perea, David, 2023. "Disclosures about algorithmic decision making in the corporate reports of Western European companies," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    4. Tarcia Camily Cavalcante Quezado & Nuno Fortes & William Quezado Figueiredo Cavalcante, 2022. "The Influence of Corporate Social Responsibility and Business Ethics on Brand Fidelity: The Importance of Brand Love and Brand Attitude," Sustainability, MDPI, vol. 14(5), pages 1-20, March.
    5. Colak, Ozlem & Enoch, Marcus & Morton, Craig, 2023. "Airport business models and the COVID-19 pandemic: An exploration of the UK case study," Journal of Air Transport Management, Elsevier, vol. 108(C).
    6. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    7. Akter, Shahriar & Motamarri, Saradhi & Hani, Umme & Shams, Riad & Fernando, Mario & Mohiuddin Babu, Mujahid & Ning Shen, Kathy, 2020. "Building dynamic service analytics capabilities for the digital marketplace," Journal of Business Research, Elsevier, vol. 118(C), pages 177-188.
    8. Wullianallur Raghupathi & Viju Raghupathi, 2021. "Contemporary Business Analytics: An Overview," Data, MDPI, vol. 6(8), pages 1-11, August.

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