A.I. and Data-Driven Mobility at Volkswagen Financial Services AG

Jawed, Shayan; Arif, Mofassir ul Islam; Rashed, Ahmed Nabih Zaki; Madhusudhanan, Kiran; Elsayed, Shereen; Jameel, Mohsan; Volk, A.; Hintsches, Andre; Kornfeld, Marlies; Lange, Katrin; Schmidt-Thieme, Lars

doi:10.48550/arxiv.2202.04411

Cited by 1 publication

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“…Many modern economic bargaining environments are experiencing a surge in decision-making based on artificial intelligence (AI). In consumer markets, algorithms determine the allocation of scarce goods through differential pricing [27,29,36,37,72,73], automate financial transactions like credit-score loaning [107,138,151,164] or trading [19,32,43,74,110], augment decision processes in credence goods markets [96], and are primed to autonomously act in a variety of online auction processes such as selling used goods [85,130]. In organizations, AI-technologies can be leveraged to monitor, rate and reward employees [95], determine who gets hired [100,109], and must function within interdisciplinary team environments that involve everyday negotiations such as allocating workloads [10,70].…”

Section: Introductionmentioning

confidence: 99%

For What It’s Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

Erlei

Das

Meub

et al. 2022

CHI Conference on Human Factors in Computing Systems

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As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent's behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; User studies; Empirical studies in collaborative and social computing;• Applied computing → Economics; Psychology.

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