The Promise and Limitation of Explainable AI in Smart Cities: A Sociotechnical Perspective
Bibliographic record
Abstract
Artificial Intelligence tools (AITs) designed for multiuser systems, such as smart cities, often cause a conflict between the fairness perceived by individual users and the group goal embedded into the AI algorithm. This study investigates how explainable AI (XAI) affects user collaboration with AITs in smart cities when fairness conflicts arise between individual and group goals. Drawing on Fairness Heuristics Theory (FHT), we conducted scenario surveys to assess users' intentions to collaborate with smart city AITs while facing the fairness conflict. Our results indicate that XAI's effects vary depending on the specific context and the nature of XAI provided. In a multiuser system like a smart city, when XAI emphasizes group-level prioritization, it can exacerbate the individual user’s fairness conflicts, negatively affecting user-AIT collaboration. Conversely, it can enhance user-AIT collaboration by highlighting individual user features or supported societal goals. Furthermore, there is currently no empirical evidence regarding the effect of XAI during emergencies. These findings underscore both XAI's potential benefits and limitations in promoting user collaboration within multiuser systems. This research contributes to the expanding XAI literature. It also provides insights that can assist practitioners in designing AI technologies that enhance user collaboration in complex environments, such as smart cities.<br /><br />Acknowledgements: This research is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC) Grant #430- 2022-00504. In addition, this work is supported in part by the Institute for Computing, Information and Cognitive Systems (ICICS) at UBC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".