Deutsch
Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "Trustworthy AI: How Ethicswashing Undermines Consumer Trust",
Author= "Christian Peukert and Simon Kloker",
Doi= "https://doi.org/10.30844/wi_2020_j11-peukert",
Abstract= "Ethicswashing is a neologism that has, due to the release of ethical guidelines for trustworthy Artificial Intelligence (AI) by the European Union, recently gained in popularity. Although the term is closely related to the concept of greenwashing, it is currently primarily used to describe companies’ undertakings to keep ethical debates running in order to influence or avoid strict regulations. However, it is not clear yet whether ethicswashing has further implications similar to those already revealed for greenwashing or sharewashing. In an online survey with 94 participants, we find that perceived ethicswashing has a significant negative effect on consumer trust, whereby the effect is mediated by the perception of risk and consumer confusion (based on PLS SEM). With our results, we thus contribute a further flipside to the discussion of ethics in AI and provide a starting point for developing a comprehensive understanding of ethicswashing and its influence on trust.
",
Keywords= "Ethicswashing, AI, ethics, consumer trust, online survey.
",
}
Christian Peukert and Simon Kloker: Trustworthy AI: How Ethicswashing Undermines Consumer Trust. Online: https://doi.org/10.30844/wi_2020_j11-peukert (Abgerufen 24.04.24)
Open Access
Abstract
Abstract
Ethicswashing is a neologism that has, due to the release of ethical guidelines for trustworthy Artificial Intelligence (AI) by the European Union, recently gained in popularity. Although the term is closely related to the concept of greenwashing, it is currently primarily used to describe companies’ undertakings to keep ethical debates running in order to influence or avoid strict regulations. However, it is not clear yet whether ethicswashing has further implications similar to those already revealed for greenwashing or sharewashing. In an online survey with 94 participants, we find that perceived ethicswashing has a significant negative effect on consumer trust, whereby the effect is mediated by the perception of risk and consumer confusion (based on PLS SEM). With our results, we thus contribute a further flipside to the discussion of ethics in AI and provide a starting point for developing a comprehensive understanding of ethicswashing and its influence on trust.
Keywords
Schlüsselwörter
Ethicswashing, AI, ethics, consumer trust, online survey.
References
Referenzen
1. AI HLEG: Ethics Guidelines for Trustworthy AI (By the High-Level Expert Group on Artificial Intelligence (AI HLEG) set up by the European Commission). (2019).
2. Metzinger, T.: Ethics Washing Made in Europe, https://www.tagesspiegel.de/politik/euguidelines- ethics-washing-made-in-europe/24195496.html (Accessed: 06.08.2019), (2019).
3. Chen, Y.S., Chang, C.H.: Greenwash and Green Trust: The Mediation Effects of Green Consumer Confusion and Green Perceived Risk. J. Bus. Ethics. 114, 489–500 (2013).
4. Laufer, W.S.: Social Accounting and Corporate Greenwashing. J. Bus. Ethics. 43, 253–261 (2003).
5. Wagner, B.: Ethics as an Escape from Regulation: From “ethics-washing” to ethicsshopping? In: Hildebrandt, M. (ed.) Being Profiled, Cogitas Ergo Sum. pp. 84–90. Amsterdam University Press (2018).
6. Pieters, W.: Explanation and Trust: What to Tell the User in Security and AI? Ethics Inf. Technol. 13, 53–64 (2011).
7. Brady, D.: Ethics: IT Professional Pillar or Pillory. Mondo Digit. 13, 1–14 (2014).
8. Mingers, J., Walsham, G.: Toward Ethical Information Systems: The Contribution of Discourse Ethics. MIS Q. 34, 833–854 (2010).
9. Rouillard, C., Giroux, D.: Public Administration and the Managerialist Fervour For Values and Ethics: Of Collective Confusion in Control Societies. Adm. Theory Prax. 27, 330–357 (2005).
10. Hawlitschek, F., Stofberg, N., Teubner, T., Tu, P., Weinhardt, C.: How Corporate Sharewashing Practices Undermine Consumer Trust. Sustain. 10, 1–18 (2018).
11. Ganesan, S.: Determinants of Long-Term Orientation in Buyer-Seller Relationships. J. Mark. 58, 1–19 (1994).
12. Russell, S., Dewey, D., Tegmark, M.: Artificial Intelligence. AI Mag. 36, 105–114 (2015).
13. Johnson, D.G.: Technology with No Human Responsibility? J. Bus. Ethics. 127, 707–715 (2015).
14. Alaieri, F., Vellino, A.: Ethical Decision Making in Robots: Autonomy, Trust and Responsibility. In: Proceedings of the International Conference on Social Robotics 2016. pp. 159–168. Springer, Cham (2016).
15. Anderson, M., Anderson, S.L.: Machine Ethics: Creating an Ethical Intelligent Agent. AI Mag. 28, 15–26 (2007).
16. Hamann, R., Kapelus, P.: Corporate Social Responsibility in Mining in Southern Africa: Fair Accountability or just Greenwash? Development. 47, 85–92 (2004).
17. Lyon, T.P., Maxwell, J.W.: Greenwash: Corporate Environmental Disclosure Under Threat of Audit. J. Econ. Manag. Strateg. 20, 3–41 (2011).
18. Parguel, B., Benoît-Moreau, F., Larceneux, F.: How Sustainability Ratings Might Deter “Greenwashing”: A Closer Look at Ethical Corporate Communication. J. Bus. Ethics. 102, 15–28 (2011).
19. Huang, L.-S.: #WeWashing: When “Sharing” Is Renting and “Community” Is a Commodity, http://www.huffingtonpost.com/leesean-huang/wewashing-when-sharingis_ b_6879018.html (Accessed: 06.08.2019), (2015).
20. Netter, S.: Exploring the Sharing Economy. Copenhagen Business School, PhD series, Frederiksberg (2016).
21. Light, A., Miskelly, C.: Sharing Economy vs Sharing Cultures? Designing for Social, Economic and Environmental Good. Interact. Des. Archit. 24, 49–62 (2015).
22. Lankoski, L.: Corporate Responsibility Activities and Economic Performance: A Theory of Why and How They are Connected. Bus. Strateg. Environ. 17, 536–547 (2008).
23. Pope, S., Wæraas, A.: CSR-Washing is Rare: A Conceptual Framework, Literature Review, and Critique. J. Bus. Ethics. 137, 173–193 (2016).
24. Hooghiemstra, R.: Corporate Communication and Impression Management – New Perspectives Why Companies Engage in Corporate Social Reporting. J. Bus. Ethics. 27, 55–68 (2000).
25. Niskanen, J., Nieminen, T.: The Objectivity of Corporate Environmental Reporting: A Study of Finnish Listed Firms’ Environmental Disclosures. Bus. Strateg. Environ. 10, 29– 37 (2001).
26. Ashley-Cantello, W.: Advertising Watchdog Receives Record Complaints Over Corporate “Greenwash,” https://www.theguardian.com/environment/2008/may/01/corporatesocialresponsibility.ethic alliving (Accessed: 19.04.2019), (2008).
27. Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., Wagner, G.G.: Individual Risk Attitudes: Measurement, Determinants, and Behavioral Consequences. J. Eur. Econ. Assoc. 9, 522–550 (2011).
28. Pomering, A., Johnson, L.W.: Advertising Corporate Social Responsibility Initiatives to Communicate Corporate Image: Inhibiting Scepticism to Enhance Persuasion. Corp. Commun. An Int. J. 14, 101–118 (2009).
29. Helbing, D., Frey, B.S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., van den Hoven, J., Zicari, R. V., Zwitter, A.: Will Democracy Survive Big Data and Artificial Intelligence? In: Helbing, D. (ed.) Towards Digital Enlightenment. pp. 73–98. Springer, Cham (2018).
30. Huffman, C., Kahn, B.E.: Variety for Sale: Mass Customization or Mass Confusion? J. Retail. 74, 491–513 (1998).
31. Mitchell, V.-W., Papavassiliou, V.: Marketing Causes and Implications of Consumer Confusion. J. Prod. Brand Manag. 8, 319–342 (1999).
32. Walsh, G., Mitchell, V.-W.: The Effect of Consumer Confusion Proneness on Word of Mouth, Trust, and Customer Satisfaction. Eur. J. Mark. 44, 838–859 (2010).
33. Mitchell, V.-W.: Consumer Perceived Risk: Conceptualisations and Models. Eur. J. Mark. 33, 163–195 (1999).
34. Dinev, T., Hart, P.: An Extended Privacy Calculus Model for E-commerce Transactions. Inf. Syst. Res. 17, 61–80 (2006).
35. Diener, E., Emmons, R.A., Larsen, R.J., Griffin, S.: The Satisfaction With Life Scale. J. Personal. Assessment,. 49, 71–75 (1985).
36. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P.: Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol. 88, 879–903 (2003).
37. Simmering, M.J., Fuller, C.M., Richardson, H.A., Ocal, Y., Atinc, G.M.: Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration. Organ. Res. Methods. 18, 473–511 (2015).
38. Walsh, G., Hennig-Thurau, T., Mitchell, V.-W.: Consumer Confusion Proneness: Scale Development, Validation, and Application. J. Mark. Manag. 23, 697–721 (2007).
39. Chen, Y.S., Chang, C.H.: Enhance Green Purchase Intentions: The Roles of Green Perceived Value, Green Perceived Risk, and Green Trust. Manag. Decis. 50, 502–520 (2012).
40. Chen, Y.S.: The Drivers of Green Brand Equity: Green Brand Image, Green Satisfaction, and Green Trust. J. Bus. Ethics. 93, 307–319 (2010).
41. Gefen, D., Rigdon, E.E., Straub, D.: Editor’s Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research. MIS Q. 35, iii–xiv (2011).
42. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: Sage Publications (2016).
43. Fornell, C., Larcker, D.F.: Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res. 18, 382–388 (1981).
44. Henseler, J., Ringle, C.M., Sinkovics, R.R.: The Use of Partial Least Squares Path Modeling in International Marketing. Adv. Int. Mark. 20, 277–319 (2009).
45. Hayes, A.F.: Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium. Commun. Monogr. 76, 408–420 (2009).
46. Hayes, A.F.: PROCESS: A Versatile Computational Tool for Observed Variable Moderation, Mediation, and Conditional Process Modeling. White Pap. Retrieved from http//www.afhayes.com/public/process2012.pdf. 1–39 (2012).
47. Zhao, X., Lynch, J.G., Chen, Q.: Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. J. Consum. Res. 37, 197–206 (2010).
48. Gefen, D., Karahanna, E., Straub, D.W.: Trust and TAM in Online Shopping: An Integrated Model. MIS Q. 27, 51–90 (2003).
49. McKnight, D.H., Choudhury, V., Kacmar, C.: Developing and Validating Trust Measures for e-Commerce: An Intergrative Typology. Inf. Syst. Res. 3, 334–359 (2002).
50. Dinev, T., Bellotto, M., Hart, P., Russo, V., Serra, I., Colautti, C.: Privacy Calculus Model in E-commerce – A Study of Italy and the United States. Eur. J. Inf. Syst. 15, 389–402 (2006).