The Dark Sides of AI

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    Privacy Discrimination: What it is and why it matters
    ( 2022-01-04) Hillebrand, Luis ; Hermes, Sebastian ; Böhm, Markus
    We argue that online companies are able to exploit users’ varying levels of privacy needs. We show that by employing data analytics methods on a comparatively small amount of data it is possible to predict how high information privacy concerns of specific users are. We argue that online companies might be able to introduce “privacy discrimination”, in the sense that they might apply varying levels of privacy protection to users, based on their privacy concerns. Users indifferent about privacy could be presented with limited privacy options, adjusted terms and conditions or might be driven to disclose more personal information.
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    Fair Engineering of Machine Learning Systems – Lessons Learned from a Literature Review
    ( 2022-01-04) Sengewald, Julian ; Lackes, Richard
    With the growing prevalence of AI algorithms and their use to prepare and even execute decisions, there is increasing debate about whether the results of machine learning systems tend to be fairer or more unfair. When faced with engineering a fair machine learning solution in practice, trade-offs arise between conflicting fairness notions. We conduct a literature review on this topic. The results of our review indicate that a slight consensus exists that the human concept of fairness is much broader than what lies in the scope of current fairness metrics. We discuss the context of judging fairness metrics. We also find that, albeit much research already has been done, there is room for improvement when seeking to generalize the findings across different scenarios.
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    Does Customers’ Emotion toward Voice-based Service AI Cause Negative Reactions? Empirical Evidence from a Call Center
    ( 2022-01-04) Li, Bin ; Liu, Luning ; Mao, Weicheng ; Qu, Yongcumu
    Many companies are introducing voice-based artificial intelligence (AI) into their call centers. Little is known about the relationship between customers’ emotions to voice-based AI service and customers’ negative reactions. This study investigates the link between customers’ emotions toward voice-based AI service and customers’ negative reactions. Our results reveal that customers’ emotion toward voice-based AI service could significantly affect their complaint behavior, and customers’ complaints differ among emotion types. Customers’ negative and positive emotions toward voice-based AI services have a significantly negative and positive effect, respectively, on customer complaint behavior than neutral emotions. We also find that the exchange round of human-computer interaction moderates the effect of the customer emotion by attenuating its effect on customer complaints. This study is the first to empirically test the impact of customers’ emotions toward voice-based AI service on customers’ complaint behavior in the service industry.
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    Introduction to the Minitrack on The Dark Sides of AI
    ( 2022-01-04) Xiao, Lin ; Shen, Xiao-Liang ; Cheng, Xusen