Trust in AI: progress, challenges, and future directions
Abstract The increasing use of artificial intelligence (AI) systems in our daily lives through various applications, services, and products highlights the significance of trust and distrust in AI from a user perspective. AI-driven systems have significantly diffused into various aspects of our lives...
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| Format: | Article |
| Language: | English |
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Springer Nature
2024-11-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-024-04044-8 |
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| _version_ | 1832585972285440000 |
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| author | Saleh Afroogh Ali Akbari Emmie Malone Mohammadali Kargar Hananeh Alambeigi |
| author_facet | Saleh Afroogh Ali Akbari Emmie Malone Mohammadali Kargar Hananeh Alambeigi |
| author_sort | Saleh Afroogh |
| collection | DOAJ |
| description | Abstract The increasing use of artificial intelligence (AI) systems in our daily lives through various applications, services, and products highlights the significance of trust and distrust in AI from a user perspective. AI-driven systems have significantly diffused into various aspects of our lives, serving as beneficial “tools” used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust and distrust in AI serve as regulators and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, a variety of studies focused on the different dimensions of trust and distrust in AI and its relevant considerations. In this systematic literature review, after conceptualizing trust in the current AI literature, we will investigate trust in different types of human–machine interaction and its impact on technology acceptance in different domains. Additionally, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, along with some trustworthy measurements. Moreover, we examine major trust-breakers in AI (e.g., autonomy and dignity threats) and trustmakers; and propose some future directions and probable solutions for the transition to a trustworthy AI. |
| format | Article |
| id | doaj-art-23b4e9b6bb2049cbb2f48051b589f1e3 |
| institution | Kabale University |
| issn | 2662-9992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj-art-23b4e9b6bb2049cbb2f48051b589f1e32025-01-26T12:20:19ZengSpringer NatureHumanities & Social Sciences Communications2662-99922024-11-0111113010.1057/s41599-024-04044-8Trust in AI: progress, challenges, and future directionsSaleh Afroogh0Ali Akbari1Emmie Malone2Mohammadali Kargar3Hananeh Alambeigi4Urban Information Lab, The University of Texas at AustinStanford University School of MedicineDepartment of Philosophy, Lone Star College in HoustonDepartment of Mechanical Engineering, Texas A&M UniversityDepartment of Industrial and Systems Engineering, Texas A&M UniversityAbstract The increasing use of artificial intelligence (AI) systems in our daily lives through various applications, services, and products highlights the significance of trust and distrust in AI from a user perspective. AI-driven systems have significantly diffused into various aspects of our lives, serving as beneficial “tools” used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust and distrust in AI serve as regulators and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, a variety of studies focused on the different dimensions of trust and distrust in AI and its relevant considerations. In this systematic literature review, after conceptualizing trust in the current AI literature, we will investigate trust in different types of human–machine interaction and its impact on technology acceptance in different domains. Additionally, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, along with some trustworthy measurements. Moreover, we examine major trust-breakers in AI (e.g., autonomy and dignity threats) and trustmakers; and propose some future directions and probable solutions for the transition to a trustworthy AI.https://doi.org/10.1057/s41599-024-04044-8 |
| spellingShingle | Saleh Afroogh Ali Akbari Emmie Malone Mohammadali Kargar Hananeh Alambeigi Trust in AI: progress, challenges, and future directions Humanities & Social Sciences Communications |
| title | Trust in AI: progress, challenges, and future directions |
| title_full | Trust in AI: progress, challenges, and future directions |
| title_fullStr | Trust in AI: progress, challenges, and future directions |
| title_full_unstemmed | Trust in AI: progress, challenges, and future directions |
| title_short | Trust in AI: progress, challenges, and future directions |
| title_sort | trust in ai progress challenges and future directions |
| url | https://doi.org/10.1057/s41599-024-04044-8 |
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