Viewpoint: Human-in-the-loop Artificial Intelligence
Open accessAbstract
Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future may have a possible dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers may need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Many learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, many of these workers are shooting themselves in the feet. In this paper, we propose Human-in-the-loop Artificial Intelligence (HitAI) as a fairer paradigm for AI systems. Recognizing that any AI system has humans in the loop, HitAI will reward these aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Merry Men, HitAI researchers should fight for a fairer Robin Hood Artificial Intelligence that gives back what it steals. This article is part of the special track on AI and Society.
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The standard response to new issues of legitimacy and control in ADM has been to reinsert human agency 'in-the-loop'.
“The standard response to new issues of legitimacy and control in ADM has been to reinsert human agency 'in- the- loop' (Enarsson et al. 2022; Zanzotto 2019).”
From Algorithmic Governance: Experimental Evidence on Citizens' and Public Administrators' Legitimacy Perceptions of Automated Decision‐Making (2026) · cites this paper as Zanzotto 2019
Section 2.2 describes leaving the final decision to human professionals as one form of human-in-the-loop AI. It says this form is confined to specialized workers; the Introduction instead proposes a novel approach focused on crediting knowledge producers. The paper does not establish that reinserting human decision-makers is the standard response to legitimacy and control issues in automated decision-making. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAnother advantage of using CBR is its interactive nature, which keeps human experts in the loop, who therefore remain responsible for the decision based on the results of the CBR application.
“Another advantage of using CBR is its interactive nature, which keeps human experts in the loop [24], who therefore remain responsible for the decision based on the results of the CBR application.”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [24]
Section 2.2 says explainable AI can keep specialized professionals in the loop by giving them the last word when they use machines as advisers. It does not identify case-based reasoning (CBR) or establish that CBR’s interactive nature produces this arrangement; no page number was provided in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- Zanzotto, F. M.
- Affiliations
- University of Rome Tor Vergata (IT)
- Corresponding author
- Zanzotto, Fabio Massimo
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2019-02-10
- Volume
- 64
- Pages
- 243-252
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2766005220
- Cited by
- 380
- References
- 29
- Citations by year
- 2026: 53 · 2025: 60 · 2024: 68 · 2023: 59 · 2022: 55 · 2021: 57 · 2020: 19 · 2019: 9
- FWCI
- 61.18
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Artificial Intelligence Research (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- knowledge ownership, algorithmic fairness, AI and society
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