Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement
Bibliographic Data
| ID | 21642490 |
|---|---|
| Authors | Catalina Gómez (0000-0002-0971-1787, Johns Hopkins University, corresponding author), Mathias Unberath (0000-0002-0055-9950, Johns Hopkins University), Chien‐Ming Huang (0000-0002-6838-3701, Johns Hopkins University) |
| Year | 2023 |
| Volume | 172 |
| Pages | 102977 |
| Publication date | 2023-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Human-Computer Studies (JOURNAL) |
| Journal identifiers | ISSN: 1071-5819 • E-ISSN: 1095-9300 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijhcs.2022.102977 |
| OpenAlex | W4312055540 |
| Language | EN |
| Citations received | 8 |
| References cited | 43 |
Integrating artificial intelligence (AI) systems into decision-making tasks attempts to assist people by augmenting or complementing their abilities and ultimately improve task performance. However, when considering recommendations from modern “black box” intelligent systems, users are confronted with the decision of accepting or overriding AI’s recommendations. These decisions are even more challenging to make when there exists a significant knowledge imbalance between the users and the AI system—namely, when people lack necessary task knowledge and are therefore unable to accurately complete the task on their own. In this work, we aim to understand people’s behavior in AI-assisted decision-making tasks when faced with the challenge of knowledge imbalance and explore whether involving users in an AI’s prediction generation process makes them more willing to follow the AI’s recommendations and enhances their perception of collaboration. Our empirical study reveals that the involvement of users in generating AI recommendations during a task with notable knowledge imbalance causes them to be more willing to agree with the AI’s suggestions and to perceive the AI agent and their collaboration as a team more positively
Applications of artificial intelligence · Knowledge management · Perception · Big Data and Business Intelligence · Computer Science · Engineering · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Psychology · Artificial Intelligence
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| Unique citing works | 8 |
|---|---|
| Citations per year | 8 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |