Uncovering the Dark Side of Artificial Intelligence in Electronic Markets: A Systematic Literature Review

Uncovering the Dark Side of Artificial Intelligence in Electronic Markets: A Systematic Literature Review

Yunfei Xing, Lu Yu, Justin Z. Zhang, Leven J. Zheng
Copyright: © 2023 |Pages: 25
DOI: 10.4018/JOEUC.327278
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Abstract

The dark sides of artificial intelligence (AI) have attracted immense attention in recent years. This study produces a synthesis of current research on six dark sides of AI in electronic markets through a systematic literature review. The authors searched five different databases and summarized the dark sides of AI in electronic markets from six aspects: privacy concerns, security issues, ethical challenges, criminals and terrorists enabled by AI, trust issues between humans and machines, and AI biases. The literature review presented in this study has provided a rigorous and structured overview of research on AI's dark sides in the electronic markets through a combination of quantitative and qualitative analysis of the AI literature. As AI has made rich contributions to a variety of applications in electronic markets, special care should be taken regarding the dark side of AI. Governments and policymakers are recommended to establish legislation to ensure that AI-powered innovation and implementation are beneficial to the social good while limiting the threats caused by the dark side of AI.
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Background

In the literature addressing the dark sides of AI (see Table 1), Roche (2016) was the first to identify five conditions related to AI’s dark side: the destruction of employment, stimulating societal instability, enabling criminal and terrorist activities, losing autonomy and privacy, and fuelling a cyber arms race. Cheng et al. (2021) uncovered both the bright and dark sides of AI by identifying two primary concerns: uncertainty and the invasion of privacy. In an extensive and comprehensive study on AI techniques, Jabbarpour et al. (2021) found the following negative aspects: energy consumption, data issues, security and trust, privacy, fairness, safety, predictability, explainability, complexity, monopoly, and responsibility.

Table 1.
Dark sides of AI summarized in previous research
AuthorYearChallenges or Dark Sides of AIContribution
Roche2016• Destruction of employment
• Stimulating societal instability
• Enabling criminal and terrorist activities
• Losing autonomy and privacy
• Fuelling a cyber arms race		• Discussing both good and bad downstream consequences of AI
Wirtz et al.2020• AI society (workforce substitution & transformation; social acceptance & trust in AI; transformation of H2M interaction)
• AI law and regulation (AI rulemaking for human behavior; moral dilemmas; AI discrimination)
• AI ethics (privacy & safety; responsibility & accountability; governance of autonomous intelligence systems)		• Outlining the current state of AI governance
• Giving an overview of AI challenges and risks for public administration as well as previous AI governance or regulation frameworks
• Developing an integrated AI governance framework that organizes the key aspects of AI governance and regulation
Castillo et al.2020• Authenticity issues
• Cognition challenges
• Affective issues
• Functionality issues
• Integration conflicts		• Discussing how AI is transforming the service industry
• Exploring the theoretical concept of value co-destruction by adopting an S-D logic lens
• Discussing the proposed conceptualization of co-destruction in AI service settings
Esmaeilzadeh2020• Technological concerns (perceived performance anxiety; perceived communication barriers)
• Ethical concerns (perceived social biases; perceived privacy concerns; perceived mistrust in AI mechanisms)
• Regulatory concerns (perceived unregulated standards; perceived liability issues; perceived risks)		• Developing a model mainly based on value perceptions due to the specificity of the healthcare field
• Examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers’ perspectives
• Using an online survey to collect data from 307 individuals in the United States
Cheng et al.2021• Uncertainty
• Invasion of privacy		• Uncovering the interplay between the dark and bright sides of big data analytics and AI and the underlying mechanisms of cognitive appraisals for user behavior in ridesharing
Jabbarpour et al.2021• Energy consumption
• Data issues
• Security and trust
• Privacy
• Fairness
• Safety
• Beneficial
• Predictability
• Explainable AI
• The complexity issue
• Monopoly
• Responsibility challenges		• Discussing the general concepts of the CS problem and its variations
• Conducting an extensive and comprehensive study on the dark sides of AI techniques to extract the main technical dark sides
• Proposing a novel framework for the CS problem of ISs that considers the dark sides of AI

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