Having the Final Say: Machine Support of Ethical Decisions of Doctors

Inthorn, Julia; Tabacchi, Marco Elio; Seising, Rudolf

doi:10.1007/978-3-319-08108-3_12

Cited by 6 publications

(5 citation statements)

References 48 publications

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“…Computers, robots, algorithms, and other forms of automation are quickly becoming a fundamental part of many decision-making processes in both personal and professional contexts. From forecasting product sales (Fildes, Goodwin, Lawrence, & Nikolopoulos, 2009) to informing medical and management decisions (Esmaeilzadeh, Sambasivan, Kumar, & Nezakati, 2015;Inthorn, Tabacchi, & Seising, 2015;Prahl, Dexter, Braun, & Van Swol, 2013), people frequently seek and receive advice from nonhuman (automation) sources when facing important decisions. Yet, despite seeking advice from automation, decision makers frequently discount advice obtained from it, especially when compared to advice from a human advisor (Önkal, Goodwin, Thomson, Gönül, & Pollock, 2009).…”

Section: Introductionmentioning

confidence: 99%

Understanding algorithm aversion: When is advice from automation discounted?

Prahl

Swol

2017

Journal of Forecasting

171

104

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Forecasting advice from human advisors is often utilized more than advice from automation. There is little understanding of why “algorithm aversion” occurs, or specific conditions that may exaggerate it. This paper first reviews literature from two fields—interpersonal advice and human–automation trust—that can inform our understanding of the underlying causes of the phenomenon. Then, an experiment is conducted to search for these underlying causes. We do not replicate the finding that human advice is generally utilized more than automated advice. However, after receiving bad advice, utilization of automated advice decreased significantly more than advice from humans. We also find that decision makers describe themselves as having much more in common with human than automated advisors despite there being no interpersonal relationship in our study. Results are discussed in relation to other findings from the forecasting and human–automation trust fields and provide a new perspective on what causes and exaggerates algorithm aversion.

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Section: Introductionmentioning

confidence: 99%

Understanding algorithm aversion: When is advice from automation discounted?

Prahl

Swol

2017

Journal of Forecasting

171

104

View full text Add to dashboard Cite

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“…This is an individual's tendency to overweight one's own opinion relative to that of an advisor when deciding whether to accept an advice (Harvey and Fischer 1997;Yaniv and Kleinberger 2000; for a review see Bonaccio and Dalal 2006). In addition, even though individuals seek automated advice to aid important high-stakes decisions in several contexts, including medical ones (e.g., Esmaeilzadeh et al 2015;Inthorn, Tabacchi, and Seising 2015;), they suffer from "algorithm aversion." This is a tendency to irrationally, and systematically, discount advice that is generated automatically and communicated by computer algorithms (e.g., Dietvorst, Simmons, and Massey 2015;Goodwin, Gönül, and Önkal 2013).…”

mentioning

confidence: 99%

Whose Algorithm Says So: The Relationships between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice

Lourenco

Dellaert

Donkers

2020

Journal of Interactive Marketing

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Financial advisors seek to accurately measure individuals' risk preferences and provide sound personalized investment advice. Both advice tasks are increasingly offered through automated online technologies. Little is known, however, about what drives individuals' acceptance of such automated financial advice and, from a consumer point of view, which firms may be best positioned to provide such advice.We generate novel insights on these questions by conducting a real-world empirical study using an interactive automated online tool that employs an innovative computer algorithm to build pension investment profiles, the "Pension Builder," and a large, representative sample.We focus on the role that two key firm characteristics have on consumer acceptance of pension investment advice generated by computer algorithms running on automated interactive online tools: profit orientation and role in the sales channel.We find that consumers' perceptions of trust and expertise of the firm providing the automated advice are important drivers of advice acceptance (besides a strong impact of the satisfaction with the consumer-online tool interaction), and that these constructs themselves are clearly influenced by the for-profit vs. not-for-profit orientation and the product provider vs. advisor only role in the sales channel of the firm providing the advice.We discuss the implications of our findings for marketers and policy makers and provide suggestions for future research.

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“…Computers and algorithms become increasing important components of decision-making processes (Esmaeilzadeh et al 2015;Inthorn et al 2015). Although individuals consistently rely on technological support to make decisions, they tend to rely less on algorithm-generated information than on human-generated information (Önkal et al 2009).…”

Section: Hypothesismentioning

confidence: 99%

The Social Dilemma of Big Data: Donating Personal Data to Promote Social Welfare

Hillebrand

Hornuf

2021

SSRN Journal

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When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Regulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1,696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are processed by a humansupervised or a self-learning smart assistant.

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Having the Final Say: Machine Support of Ethical Decisions of Doctors

Cited by 6 publications

References 48 publications

Understanding algorithm aversion: When is advice from automation discounted?

Understanding algorithm aversion: When is advice from automation discounted?

Whose Algorithm Says So: The Relationships between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice

The Social Dilemma of Big Data: Donating Personal Data to Promote Social Welfare

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