Psychological factors underlying attitudes toward AI tools

De Freitas, Julian; Agarwal, Stuti; Schmitt, Bernd; Haslam, Nick

doi:10.1038/s41562-023-01734-2

Cited by 18 publications

(2 citation statements)

References 94 publications

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“…Nevertheless, it is imperative to recognize that AI algorithms are intended to fulfill the objectives of transparency in a context that transcends merely being available and accessible. Various scholars have also highlighted that transparency encompasses the extent to which the internal workings of a system, including the capacity to grasp and articulate the logic underpinning its decisions, are observable and intelligible to human users (Zerilli et al, 2019;De Freitas et al, 2023). Informed by these insights, our subsequent research endeavors will aim to refine the definition of transparency by incorporating additional pertinent dimensions such as explainability and comprehensibility into the AI decision-making framework.…”

Section: Discussionmentioning

confidence: 99%

Grading by AI makes me feel fairer? How different evaluators affect college students’ perception of fairness

Chai,

Ma,

Wang

et al. 2024

Front. Psychol.

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IntroductionIn the field of education, new technologies have enhanced the objectivity and scientificity of educational evaluation. However, concerns have been raised about the fairness of evaluators, such as artificial intelligence (AI) algorithms. This study aimed to assess college students’ perceptions of fairness in educational evaluation scenarios through three studies using experimental vignettes.MethodsThree studies were conducted involving 172 participants in Study 1, 149 in Study 2, and 145 in Study 3. Different evaluation contexts were used in each study to assess the influence of evaluators on students’ perception of fairness. Information transparency and explanations for evaluation outcomes were also examined as potential moderators.ResultsStudy 1 found that different evaluators could significantly influence the perception of fairness under three evaluation contexts. Students perceived AI algorithms as fairer evaluators than teachers. Study 2 revealed that information transparency was a mediator, indicating that students perceived higher fairness with AI algorithms due to increased transparency compared with teachers. Study 3 revealed that the explanation of evaluation outcomes moderated the effect of evaluator on students’ perception of fairness. Specifically, when provided with explanations for evaluation results, the effect of evaluator on students’ perception of fairness was lessened.DiscussionThis study emphasizes the importance of information transparency and comprehensive explanations in the evaluation process, which is more crucial than solely focusing on the type of evaluators. It also draws attention to potential risks like algorithmic hegemony and advocates for ethical considerations, including privacy regulations, in integrating new technologies into educational evaluation systems. Overall, this study provides valuable theoretical insights and practical guidance for conducting fairer educational evaluations in the era of new technologies.

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Section: Discussionmentioning

confidence: 99%

Grading by AI makes me feel fairer? How different evaluators affect college students’ perception of fairness

Chai,

Ma,

Wang

et al. 2024

Front. Psychol.

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“…It encompasses the user's feelings, opinions, and beliefs regarding the system's usefulness, ease of use, enjoyment, and satisfaction. Attitude towards the system reflects the user's positive or negative predisposition towards adopting and utilizing the technology (De Freitas et al, (2023); Wang et al, 2021). Indicators for Attitude Towards AI tools and its relevant Likert scale statements used in the study a model taken from Weng et al (2018).…”

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confidence: 99%

Understanding AI Adoption: The Mediating Role of Attitude in User Acceptance

Dr. A. Ameer Hussian,

2024

jier

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This study investigates the factors influencing the adoption of AI tools by examining the roles of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) in shaping Behavioural Intention to Use (BIU) through the mediating effect of Attitude Towards Use (ATU). Drawing on the Technology Acceptance Model (TAM), the study employed Mediation Analysis through user estimand method using IBM AMOS to analyze data collected from IT professionals, Bengaluru. The findings reveal that both PU and PEOU significantly affect BIU, both directly and indirectly via ATU. These results underscore the importance of both perceived usefulness and ease of use in fostering positive user attitudes and enhancing behavioural intentions towards AI tools. The study's implications highlight the need for developers and organizations to focus on improving the functional benefits and user-friendliness of AI tools while also addressing user attitudes through comprehensive training and effective communication strategies. By understanding and leveraging these mediating pathways, organizations can better facilitate the adoption and utilization of AI technologies. This research contributes to the theoretical understanding of technology acceptance and provides practical insights for promoting AI tool adoption.

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Understanding model power in social AI

Brandtzaeg,

Skjuve,

Følstad

2024

AI & Soc

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Given the widespread integration of Social AI like ChatGPT, Gemini, Copilot, and MyAI, in personal and professional contexts, it is crucial to understand their effects on information and knowledge processing, and individual autonomy. This paper builds on Bråten’s concept of model power, applying it to Social AI to offer a new perspective on the interaction dynamics between humans and AI. By reviewing recent user studies, we examine whether and how models of the world reflected in Social AI may disproportionately impact human-AI interactions, potentially leading to model monopolies where Social AI impacts human beliefs, behaviour and homogenize the worldviews of its users. The concept of model power provides a framework for critically evaluating the impact and influence that Social AI has on communication and meaning-making, thereby informing the development of future systems to support more balanced and meaningful human-AI interactions.

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Psychological factors underlying attitudes toward AI tools

Cited by 18 publications

References 94 publications

Grading by AI makes me feel fairer? How different evaluators affect college students’ perception of fairness

Grading by AI makes me feel fairer? How different evaluators affect college students’ perception of fairness

Understanding AI Adoption: The Mediating Role of Attitude in User Acceptance

Understanding model power in social AI

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