Following Wrong Suggestions: Self-blame in Human and Computer Scenarios

Beretta, Andrea; Zancanaro, Massimo; Lepri, Bruno

doi:10.1007/978-3-030-29387-1_31

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Participants are initially resistant to incorrect advice, but consistency in such advice resets their perception. This observation aligns with prior studies in the field of algorithm aversion [50,51,53]. Confidence increases were not uniform across demographics.…”

Section: Resultssupporting

confidence: 88%

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Metta,

Beretta,

Guidotti

et al. 2024

Diagnostics

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A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI methods are evaluated on broad classifiers and fail to address complex, real-world issues, such as medical diagnosis. In our study, we focus on enhancing user trust and confidence in automated AI decision-making systems, particularly for diagnosing skin lesions, by tailoring an XAI method to explain an AI model’s ability to identify various skin lesion types. We generate explanations using synthetic images of skin lesions as examples and counterexamples, offering a method for practitioners to pinpoint the critical features influencing the classification outcome. A validation survey involving domain experts, novices, and laypersons has demonstrated that explanations increase trust and confidence in the automated decision system. Furthermore, our exploration of the model’s latent space reveals clear separations among the most common skin lesion classes, a distinction that likely arises from the unique characteristics of each class and could assist in correcting frequent misdiagnoses by human professionals.

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Section: Resultssupporting

confidence: 88%

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Metta,

Beretta,

Guidotti

et al. 2024

Diagnostics

Self Cite

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“…In part, this is because AIs have no social accountability if they are wrong. When relying on an AI leads to negative outcomes, causality tends to be attributed to the AI system, leading to a decrease in trust (Beretta et al, 2019; Horvath et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts

et al. 2022

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Objective This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model. Background Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations). Methods In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance). Results AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity ( d’), and reliance increased in the high reliability AI condition, but there was no effect on response bias ( c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition. Conclusion In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets). Application In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions.

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How AI Impacts Marketing and Business Jobs and Decision-Making Processes in the Leading AI Economy and in a Technostress Environment

Goudira

2024

Advances in Marketing, Customer Relationship Management, and E-Services

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This research delves into the effect of artificial intelligence on white-collar occupations, specifically within marketing and business. As the integration of AI tools in the workforce accelerates, professionals in these sectors grapple with the consequences of technostress. It is a widely held concern that implementing AI technology in the corporate sector may lead to job displacement despite its ability to enhance productivity and bolster profitability. Conversely, others argue that AI will create new opportunities and emphasise the importance of a comprehensive understanding of AI tools for career advancement. This study investigates the potential impact of AI technology on the careers of marketers and business professionals, how AI impacts decision-making processes, and examines the techno stressors that might affect employees' mental health, as well as concerns such as authenticity and transhumanism. The findings will be based on thorough secondary research.

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Following Wrong Suggestions: Self-blame in Human and Computer Scenarios

Cited by 3 publications

References 31 publications

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts

How AI Impacts Marketing and Business Jobs and Decision-Making Processes in the Leading AI Economy and in a Technostress Environment

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