The Allocation of Decision Authority to Human and Artificial Intelligence
Abstract
The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined.The principal trades off an AI's more aligned choice with the need to motivate the human agent to expend effort in learning choice payoffs.When agent effort is desired, it is shown that the principal is more likely to give that agent decision authority, reduce investment in AI reliability and adopt an AI that may be biased.Organizational design considerations are likely to impact on how AI's are trained.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This interaction includes clarifying who is authorized to decide what.
“This interaction (1a) includes clarifying who is authorized to decide what (Athey et al. 2020).”
From Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2023) · cites this paper as Athey et al. 2020
These changes affect organizational practices and are a continuous outcome of other organizational practices that deal with decisions on the distribution of agency between human and AI.
“These changes affect organizational practices and are a continuous outcome of other organizational practices that deal with decisions on the distribution of agency between human and AI (Athey et al. 2020).”
From Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2023) · cites this paper as Athey et al. 2020
Cites
Still being checked.
Cited by
Sorted by citations
Herrmann, T., & Pfeiffer, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01391-5 · Cited by 173 · see citing works
“These changes affect organizational practices and are a continuous outcome of other organizational practices that deal with decisions on the distribution of agency between human and AI (Athey et al. 2020).”
BackgroundInfluentialAn Economic Approach to Regulating Algorithms
Rambachan, A., Kleinberg, J. M., Mullainathan, S., & Ludwig, J. (2020) · National Bureau of Economic Research · doi:10.3386/w27111 · Cited by 54 · see citing works
Managing Intelligence: Skilled Experts and AI in Markets for Complex Products
B, J. G. A., Handel, B., Kina, S., & Kolstad, J. (2020) · National Bureau of Economic Research · doi:10.3386/w27038 · Cited by 35 · see citing works
The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology
You, Y., Kou, Y., Ding, X., & Gui, X. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445657 · Cited by 18 · see citing works
Jin, L., Zhao, Q., Fu, S., Cao, F., Hou, B., & Ma, J. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1092478 · Cited by 15 · see citing works
Competition and Cognition in the Market for Online News
Ray, A., Ghasemkhani, H., & Martinelli, C. (2024) · Journal of Management Information Systems · doi:10.1080/07421222.2024.2340824 · Cited by 9 · see citing works
On the Fairness of Machine-Assisted Human Decisions
McLaughlin, B. (2022) · AEA Randomized Controlled Trials · doi:10.1257/rct.10416 · Cited by 7 · see citing works
Does AI Cheapen Talk? Theory and Evidence from Global Entrepreneurship and Hiring
Cowgill, B., Hernández‐Lagos, P., & Wright, N. L. (2026) · Management Science · doi:10.1287/mnsc.2024.07027 · Cited by 6 · see citing works
On the intersection of analytical sociology and computational social science
Arvidsson, M., Hedström, P., Jarvis, B. F., & Keuschnigg, M. (2025) · Edward Elgar Publishing eBooks · doi:10.4337/9781802207309.00011 · Cited by 5 · see citing works
Agrawal, A., Gans, J. S., & Avi Goldfarb, B. (2022) · National Bureau of Economic Research · doi:10.3386/w30177 · Cited by 5 · see citing works
Self-citationJin, L., Zhao, Q., Fu, S., Zhang, Y., Wu, S., Li, X., & Cao, F. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1326009 · Cited by 4 · see citing works
Effective Task Allocation in Ad Hoc Human-Agent Teams
Abuhaimed, S., & Sen, S. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220197 · Cited by 4 · see citing works
Lazear, E. P., Shaw, K. L., Hayes, G., & Jedras, J. (2022) · National Bureau of Economic Research · doi:10.3386/w30734 · Cited by 2 · see citing works
The Use of Generative Artificial Intelligence for Business Decision-Making
Mummar, A., Pacheco, A., Saunders, S., Ratliff-Villarreal, L., & Schmitt, A. (2024) · The Pinnacle A Journal by Scholar-Practitioners · doi:10.61643/c16259 · Cited by 1 · see citing works
Sustainable Artificial Intelligence: In Search of Technological Resilience
Jastroch, N. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-25182-5_31 · Cited by 1 · see citing works
Sepasgozar, S. M. E. (2026) · Encyclopedia · doi:10.3390/encyclopedia6100225
- Authors
- Athey, S., Bryan, K., & Gans, J.
- Affiliations
- National Bureau of Economic Research (US)
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2020-01
- OpenAlex
- W4285717267
- Cited by
- 14
- References
- 0
- Citations by year
- 2026: 1 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 1 · 2020: 2
- Type
- report
- Language
- en
- Open access
- closed
- Locations
- National Bureau of Economic Research (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, human agents, agency theory, AI alignment, effort incentives, organizational design, AI training data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
