Fetching the paper…
Reading the bibliography…
In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations.
When do humans heed AI agents’ advice? When should they?
Dunning, R. E., Fischhoff, B., and Davis, A. L. (2024) · 1927
Earlier work this paper cites.
Trust in close relationships
Rempel, J. K., Holmes, J. G., and Zanna, M. P. (1985) · 1985
Earlier work this paper cites.
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., and Riley, V. (1997) · 1997
Earlier work this paper cites.
The role of trust in automation reliance
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., and Beck, H. P. (2003) · 2003
Earlier work this paper cites.
Trust in automation: Designing for appropriate reliance
Lee, J. D., and See, K. A. (2004) · 2004
Earlier work this paper cites.
A probabilistic interpretation of precision, recall and F-score, with implication for evaluation
Goutte, C., and Gaussier, E. (2005) · 2005
Earlier work this paper cites.
Automated decision making comes of age
Harris, J. G., and Davenport, T. H. (2005) · 2005
Earlier work this paper cites.
Effects of imperfect automation on decision making in a simulated command and control task
Rovira, E., McGarry, K., and Parasuraman, R. (2007) · 2007
Earlier work this paper cites.
The benefits of imperfect diagnostic automation: A synthesis of the literature
Wickens, C. D., and Dixon, S. R. (2007) · 2007
Earlier work this paper cites.
Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload
de Visser, E., and Parasuraman, R. (2011) · 2011
Earlier work this paper cites.
A framework for explaining reliance on decision aids
Van Dongen, K., and Van Maanen, P.-P. (2013) · 2013
Earlier work this paper cites.
In hiring, algorithms beat instinct
Kuncel, N. R., Klieger, D. M., and Ones, D. S. (2014) · 2014
Earlier work this paper cites.
Adjusted F-measure and kernel scaling for imbalanced data learning
Maratea, A., Petrosino, A., and Manzo, M. (2014) · 2014
Earlier work this paper cites.
Directions in hybrid intelligence: Complementing AI systems with human intelligence
Kamar, E. (2016) · 2016
Earlier work this paper cites.
A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems
Schaefer, K. E., Chen, J. Y., Szalma, J. L., and Hancock, P. A. (2016) · 2016
Earlier work this paper cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
Adadi, A., and Berrada, M. (2018) · 2018
Earlier work this paper cites.
Explaining explanations: An overview of interpretability of machine learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L. (2018) · 2018
Earlier work this paper cites.
Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., and Vinck, P. (2018) · 2018
Earlier work this paper cites.
Explanations as mechanisms for supporting algorithmic transparency
Rader, E., Cotter, K., and Cho, J. (2018) · 2018
Earlier work this paper cites.
Operator reliance on automation: Theory and data
Riley, V. (2018) · 2018
Earlier work this paper cites.
Explaining models: An empirical study of how explanations impact fairness judgment
Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K., and Dugan, C. (2019) · 2019
Earlier work this paper cites.
The principles and limits of algorithm-in-the-loop decision making
Green, B., and Chen, Y. (2019) · 2019
Earlier work this paper cites.
DARPA’s explainable artificial intelligence (XAI) program
Gunning, D., and Aha, D. (2019) · 2019
Cited alongside, same era.
On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Lai, V., and Tan, C. (2019) · 2019
Cited alongside, same era.
Extending three existing models to analysis of trust in automation: Signal detection, statistical parameter estimation, and model-based control
Sheridan, T. B. (2019) · 2019
Cited alongside, same era.
Role of fairness, accountability, and transparency in algorithmic affordance
Shin, D., and Park, Y. J. (2019) · 2019
Cited alongside, same era.
Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020) · 2020
Cited alongside, same era.
How to evaluate trust in AI-assisted decision making? A survey of empirical methodologies
Vereschak, O., Bailly, G., and Caramiaux, B. (2021) · 2021
Later among the works it cites.
AI-moderated decision-making: Capturing and balancing anchoring bias in sequential decision tasks
Echterhoff, J. M., Yarmand, M., and McAuley, J. (2022) · 2022
Later among the works it cites.
Forming effective human-AI teams: Building machine learning models that complement the capabilities of multiple experts
Hemmer, P., Schellhammer, S., Vössing, M., Jakubik, J., and Satzger, G. (2022) · 2022
Later among the works it cites.
An empirical evaluation of predicted outcomes as explanations in human-AI decision-making
Jakubik, J., Schoeffer, J., Hoge, V., Voessing, M., and Kuehl, N. (2023) · 2022
Later among the works it cites.
Combining the strengths of radiologists and AI for breast cancer screening: A retrospective analysis
Leibig, C., Brehmer, M., Bunk, S., Byng, D., Pinker, K., et al. (2022) · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores
De-Arteaga, M., Fogliato, R., and Chouldechova, A. (2020) · 2020
Cited alongside, same era.
Towards a theory of longitudinal trust calibration in human-robot teams
de Visser, E. J., Peeters, M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., and Neerincx, M. A. (2020) · 2020
Cited alongside, same era.
Human trust in artificial intelligence: Review of empirical research
Glikson, E., and Woolley, A. W. (2020) · 2020
Cited alongside, same era.
“Why is ‘Chicago’ deceptive?” Towards building model-driven tutorials for humans
Lai, V., Liu, H., and Tan, C. (2020) · 2020
Cited alongside, same era.
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
Zhang, Y., Liao, Q. V., and Bellamy, R. K. (2020) · 2020
Cited alongside, same era.
Does explainable artificial intelligence improve human decision-making?
Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., and Kantarcioglu, M. (2021) · 2021
Cited alongside, same era.
A unified bi-directional model for natural and artificial trust in human–robot collaboration
Azevedo-Sa, H., Yang, X. J., Robert, L. P., and Tilbury, D. M. (2021) · 2021
Cited alongside, same era.
It’s complicated: The relationship between user trust, model accuracy and explanations in AI
Papenmeier, A., Kern, D., Englebienne, G., and Seifert, C. (2022) · 2022
Later among the works it cites.
Overreliance on AI: Literature review
Passi, S., and Vorvoreanu, M. (2022) · 2022
Later among the works it cites.
A meta-analysis of the utility of explainable artificial intelligence in human-AI decision-making
Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., and Vössing, M. (2022) · 2022
Later among the works it cites.
AI-assisted decision-making: A cognitive modeling approach to infer latent reliance strategies
Tejeda, H., Kumar, A., Smyth, P., and Steyvers, M. (2022) · 2022
Later among the works it cites.
AI shall have no dominion: On how to measure technology dominance in AI-supported human decision-making
Cabitza, F., Campagner, A., Angius, R., Natali, C., and Reverberi, C. (2023) · 2023
Later among the works it cites.
Improving human-AI collaboration with descriptions of AI behavior
Cabrera, Á. A., Perer, A., and Hong, J. I. (2023) · 2023
Later among the works it cites.
When algorithms err: Differential impact of early vs. late errors on users’ reliance on algorithms
Kim, A., Yang, M., and Zhang, J. (2023) · 2023
Later among the works it cites.
Towards a science of human-AI decision making: An overview of design space in empirical human-subject studies
Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., and Tan, C. (2023) · 2023
Later among the works it cites.
Appropriate reliance on AI advice: Conceptualization and the effect of explanations
Schemmer, M., Kuehl, N., Benz, C., Bartos, A., and Satzger, G. (2023) · 2023
Later among the works it cites.
On the interdependence of reliance behavior and accuracy in AI-assisted decision-making
Schoeffer, J., Jakubik, J., Voessing, M., Kuehl, N., and Satzger, G. (2023) · 2023
Later among the works it cites.
Explanations can reduce overreliance on AI systems during decision-making
Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023) · 2023
Later among the works it cites.
Eckhardt, S., Kühl, N., Dolata, M., and Schwabe, G. (2024) · 2024
Later among the works it cites.
In search of verifiability: Explanations rarely enable complementary performance in AI-advised decision making
Fok, R., and Weld, D. S. (2024) · 2024
Later among the works it cites.
A decision theoretic framework for measuring AI reliance
Guo, Z., Wu, Y., Hartline, J. D., and Hullman, J. (2024) · 2024
Later among the works it cites.
Effective human oversight of AI-based systems: A signal detection perspective on the detection of inaccurate and unfair outputs
Langer, M., Baum, K., and Schlicker, N. (2024) · 2024
Later among the works it cites.
Explanations, fairness, and appropriate reliance in human-AI decision-making
Schoeffer, J., De-Arteaga, M., and Kuehl, N. (2024) · 2024
Later among the works it cites.