To Trust or Not To Trust: How a Conversational Interface Affects Trust in a Decision Support System

Gupta, Akshit; Basu, Debadeep; Ghantasala, Ramya; Qiu, Sihang; Gadiraju, Ujwal

doi:10.1145/3485447.3512248

Cited by 21 publications

(10 citation statements)

References 38 publications

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“…In contrast, most autonomous driving studies included information on the automation capabilities such as conditional automation levels (e.g., Level 3 -roughly 75% automation), except for one study, which included a highly automated system (Level 4) [61]. However, we found five studies that employed automation empowered by machine learning [71,109,110,132,141] and eight which specifically referred to artificial intelligence [11,12,43,83,100,118,144,145]. Some studies also referred to fictitious automated systems [94] and others to a Wizard-of-Oz manipulations [58].…”

Section: Security and Safetymentioning

confidence: 92%

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

Wischnewski¹,

Krämer²

2023

Preprint

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Trust has been recognized as a central variable to explain the resistance to using automated systems (under-trust) and the overreliance on automated systems (over-trust). To achieve appropriate reliance, users’ trust should be calibrated to reflect a system’s capabilities. Studies from various disciplines have examined different interventions to attain such trust calibration. Based on a literature body of 1000+ papers, we identified 96 relevant publications which aimed to calibrate users’ trust in automated systems. To provide an in-depth overview of the state-of-the-art, we reviewed and summarized measurements of the trust calibration, interventions, and results of these efforts. For the numerous promising calibration interventions, we extract common design choices and structure these into four dimensions of trust calibration interventions to guide future studies. Our findings indicate that the measurement of the trust calibration often limits the interpretation of the effects of different interventions. We suggest future directions for this problem.

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Section: Security and Safetymentioning

confidence: 92%

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

Wischnewski¹,

Krämer²

2023

Preprint

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“…Not resembles to Dempster-Shafer [25] evidence theory finding degrees of belief in the attribute evaluation grades must be converted into fundamental probability masses using equation (6). The fundamental probability mass represents the precise belief provided to an attribute's n-th evaluation grade.…”

Section: Basic Probability Assignmentmentioning

confidence: 99%

“…After that, the AHP [5] approach is used to fully investigate the house hunting problem. As a result, the paper discusses the design, development, and implementation of a Decision Support System [6] that can accurately find a suitable house in a short amount of time at a low cost but this paper could not address the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A Decision Concept to Support House Hunting

Mahmud¹,

Islam²,

Begum³

et al. 2022

IJACSA

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House hunting, or the act of seeking for a place to live, is one of the most significant responsibilities for many families around the world. There are numerous criteria/factors that must be evaluated and investigated. These traits can be both statistically and qualitatively quantified and expressed. There is also a hierarchical link between the elements. Furthermore, objectively/quantitatively assessing qualitative characteristics is difficult, resulting in data inconsistency and, as a result, uncertainty. As a result, ambiguity must be dealt with using the necessary processes; otherwise, the decision to live in a particular property would be incorrect. To compare criteria, the Analytic Hierarchy Process (AHP) is employed, evidential reasoning is used to evaluate houses based on each criterion, and TOPSIS is used to rank house sites for selection. It was necessary to analyze qualitative and quantitative elements, as well as economic and social features of these residences, in order to arrive at the final order of houses, which was not an easy process. As a result, the authors developed a decision support model to aid decision makers in the management of activities related to finding a suitable dwelling. This study describes the development of a decision support system (DSS) capable of providing an overall judgment on the location of a house to live in while taking into account both qualitative and quantitative factors.

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“…This also entails an understanding of how the introduction of AI-systems will influence traditional decision patterns. Consequently, there has been a lot of behavioral research focusing on how humans make use of or rely algorithmic and AI-systems [42,46,114], or cooperate with them [41,66,117,149,168]. In many bargaining situations, however, people are not users or collaborators, but competitors, or simply the ones affected by the machine's decision.…”

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

Self Cite

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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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To Trust or Not To Trust: How a Conversational Interface Affects Trust in a Decision Support System

Cited by 21 publications

References 38 publications

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

A Decision Concept to Support House Hunting

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

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