A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
Open accessAbstract
The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this paper, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We focus on the question: Are humans capable of identifying cases in which the machine is wrong, and of overriding those recommendations? We first show that humans do alter their behavior when the tool is deployed. Then, we show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk, even when overriding the recommendation requires supervisory approval. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy.
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In some real-world applications of human-AI collaboration, a Machine Learning model is trained to perform the same classification task as the human expert.
“In some real-world applications of HAIC1,21, an ML model is trained to perform the same classification task as the human expert.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
The paper’s Predictive model section says the tool estimates the probability of a child’s future referral or out-of-home placement. The Discussion says call workers decide whether to screen a referral in for investigation. In the real-world application this paper studies, the model therefore predicts different outcomes from the decision the human experts make, rather than performing the same classification task. (LLM, conf=0.84)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredPrior research establishes irreducible uncertainty in public-sector outcomes, with both humans and algorithms likely to make mistakes.
“Prior research has also established that an irreducible degree of uncertainty exists with respect to the outcomes in the public sector [21,34,57] with both humans and algorithms likely to make mistakes.”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [34]
The Background and Related Work section (page not provided) says that prognostic tasks have irreducible uncertainty and that human and machine predictions are bound to make mistakes. Its statement is about predicting future outcomes, including in public-sector settings; it does not establish the claim for public-sector outcomes generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDiagnostic tasks entail circumstances where participants are given access to well-defined and comprehensive information about the current task, allowing them to make precise judgments.
“Diagnostic tasks entail circumstances where participants are given access to well-defned and comprehensive information about the current task, allowing them to make precise judgments [29].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [29]
The Background and Related Work section describes diagnostic tasks as having a ground truth that is, in principle, knowable to humans. It does not say participants are given comprehensive, well-defined information or that this enables precise judgments. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrognostic tasks involve scenarios in which participants are presented with restricted or unclear data and need to generate predictions regarding future outcomes.
“Prognostic tasks, on the other hand, involve scenarios in which participants are presented with restricted or unclear data and need to generate predictions regarding future outcomes [29].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [29]
The Background and Related Work section defines prognostic tasks as predictions about what will happen in the future. The Introduction says information can be vague or inconclusive in some child-welfare calls, but the paper does not make restricted or unclear data a defining feature of prognostic tasks. Page numbers were not provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWith the presence of uncertainty in a task, individuals may lack sufficient capability to verify the correctness of AI advice and therefore rely more heavily on the AI systems.
“With the presence of uncertainty in a task, individuals may lack sufcient capability to verify the correctness of AI advice and therefore rely more heavily on the AI systems [29], leading to reduced appropriate reliance on AI systems.”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [29]
The Background and Related Work section says complex tasks and time pressure may increase overreliance on decision support, and that prognostic tasks involve irreducible uncertainty. It does not say that uncertainty prevents people from verifying AI advice or that this inability causes greater reliance. (LLM, conf=0.82)
Experimental — accuracy not yet measured
The AFST is a risk assessment that predicts the likelihood that children will be removed from their home due to child neglect and abuse in the next two years.
“The AFST is a risk assessment that predicts the likelihood that children will be removed from their home due to child neglect and abuse in the next two years.”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as De-Arteaga et al., 2020
The Introduction identifies the AFST as a child-welfare risk assessment tool, and the Predictive model section says one of its models estimates the probability of out-of-home placement after an investigation. The score shown to workers also incorporates a separate prediction of future referrals. Our copy does not specify a two-year prediction window or say that placement is necessarily due to neglect or abuse; Deployment Setup and Data describes outcomes in the months following a referral. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBy calling teachers' attention to discrepancies between their own judgments and those of an AIEd system, the system may promote productive teacher reflection and learning.
“By calling teachers' attention to discrepancies between their own judgments and those of an AIEd system, the system may promote productive teacher reflection and learning”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as De-Arteaga et al., 2020
The Discussion reports that child-maltreatment call workers sometimes questioned or overrode algorithmic recommendations, and the design discussion advocates helping humans identify machine mistakes (page unavailable in our copy). It does not examine a system that calls teachers’ attention to discrepancies, or whether doing so promotes teacher reflection and learning. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe expert's performance is significantly impacted by seeing the Machine Learning model's score along with the rest of the information pertaining to each instance.
“It has been shown that the expert’s performance is significantly impacted by seeing the ML model’s score along with the rest of the information pertaining to each instance1,21,22.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
In ‘Change in call workers’ behavior’ (page unavailable), the paper reports that child-welfare call workers changed which cases they screened in after the tool was deployed. ‘Overrides of erroneous scores’ (page unavailable) reports higher post-deployment precision. These findings support an effect on decisions and one measure of performance, but the paper does not establish a statistically significant performance effect from seeing the score itself, separate from the tool’s mandatory-screen-in policy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe training scenarios create a realistic dataset of expert predictions resulting from collecting a single prediction per instance, as is often the case in real-world systems.
“This creates a realistic dataset of expert predictions resulting from collecting a single prediction per instance, as is often the case in real-world systems1,19.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
The Deployment Setup and Data and Data sections describe a real-world child-welfare hotline where a call worker makes a screening determination for each referral. That supports the single-expert-judgment aspect, but the paper studies decisions rather than a dataset of expert predictions and does not establish that this collection pattern is common across real-world systems. Page numbers were not available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn real-world systems, typically only one expert makes a decision per instance.
“Finally, we observe that current L2D research overlooks the limitations of human work capacity, despite these being a challenge in real-world systems1,19, where typically only one expert makes a decision per instance.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
The Deployment Setup and Data section describes an Allegheny County hotline where a call worker decides whether to screen in a referral, supporting the claim as an example of a real-world system. The Deployment section notes that some decisions require supervisory approval. The paper does not establish that one expert per instance is typical across real-world systems. (LLM, conf=0.83)
Experimental — accuracy not yet measuredCurrent learning-to-defer research overlooks the limitations of human work capacity.
“Finally, we observe that current L2D research overlooks the limitations of human work capacity, despite these being a challenge in real-world systems1,19, where typically only one expert makes a decision per instance.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
The Analysis section (page unavailable) says the agency investigates as many cases as its resources allow, supporting the point that capacity constrains this real-world system. The Background and Related Work section describes learning to defer but does not say that research overlooks human work-capacity limits. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAlgorithmic decision tools are used in high-stakes child-welfare decisions such as assessing child-abuse risk and determining placement stability.
“As such, algorithmic decision-making tools are now being used in high-stakes CWS situations, including making risk assessments of child abuse [34] and determining placement stability [86].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [34]
AFST predictions are presented to child welfare workers to inform their decisions about which cases to investigate.
“These predictions are presented to child welfare workers to inform their decisions about which cases to investigate( De-Arteaga et al., 2020 ;Eubanks, 2018 ).”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as De-Arteaga et al., 2020
The negative impact of hallucinations on data quality is well-documented, and incorrect AI recommendations have been shown to harm users in decision-making contexts.
“The negative impact of hallucinations on data quality is well-documented [33] —incorrect AI recommendations have been shown to harm users in decision-making contexts [13].”
From Emerging Reliance Behaviors in Human-AI Content Grounded Data Generation: The Role of Cognitive Forcing Functions and Hallucinations (2025) · cites this paper as [13]
There are relevant examples where human operators successfully identified errors made by automated systems.
“There are relevant examples where human operators successfully identified errors made by automated systems [14, 54].”
From On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives (2024) · cites this paper as [14]
Humans and AI have complementary strengths and weaknesses.
“Recently, a growing body of research has focused on human-AI collaboration (HAIC), with several authors arguing that humans and AI have complementary strengths and weaknesses1,2.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
Humans can correct model predictions in specific instances.
“Collaborative systems have shown that humans can correct model predictions in specific instances1, and that humans, in collaboration with a Machine Learning (ML) model, can achieve higher performance than either the expert or the model alone3.”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [1]
Prior work argues that public-sector algorithms are a distinct domain and must be characterized differently from private-sector algorithms where outcomes are well-defined.
“Prior work has argued that algorithms in the public sector is a domain in its own right [34,62,107,109,121] and must be characterized differently as compared to algorithms in the private sector where the decision outcomes are well-defined.”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [34]
The Introduction discusses algorithmic systems in sensitive societal domains, and the discussion of study limitations distinguishes real social-policy decisions from laboratory tasks. Neither passage argues that public-sector algorithms are a domain in their own right or compares their outcomes with well-defined private-sector outcomes. Searches for those terms found no passage making the attributed comparison. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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“Prior research has also established that an irreducible degree of uncertainty exists with respect to the outcomes in the public sector [21, 34, 57] with both humans and algorithms likely to make mistakes.”
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Self-citationHow does HCI Understand Human Agency and Autonomy?
Bennett, D., Metatla, O., Roudaut, A., & Mekler, E. D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580651 · Cited by 111 · see citing works
Measuring algorithmically infused societies
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Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Deng, W. H., Nagireddy, M., Lee, M. S. A., Singh, J., Wu, Z. S., Holstein, K., & Zhu, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533113 · Cited by 99 · see citing works
On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives
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“Discussing them is beyond the scope of this paper; however, prima facie , there appears to be no inherent conflict between our proposal and these objectives.”
BackgroundInfluentialHow Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions
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Improving Human-AI Collaboration With Descriptions of AI Behavior
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“Human-AI collaboration is finding real-world use in tasks ranging from diagnosing prostate cancer [12] to screening calls to child welfare hotlines [19, 27].”
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How Beginning Programmers and Code LLMs (Mis)read Each Other
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Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
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Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems
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You’d Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift
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The extent of algorithm aversion in decision-making situations with varying gravity
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Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement
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Towards Unbiased and Accurate Deferral to Multiple Experts
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“With the presence of uncertainty in a task, individuals may lack sufficient capability to verify the correctness of AI advice and therefore rely more heavily on the AI systems [29], leading to reduced appropriate reliance on AI systems.”
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Self-citationEvaluating the impact of human explanation strategies on human-AI visual decision-making
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Arcila, B. B. (2024) · Computer law & security review · doi:10.1016/j.clsr.2024.106012 · Cited by 38 · see citing works
The rise of people analytics and the future of organizational research
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The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool
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Designing equitable algorithms
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Role of Human-AI Interaction in Selective Prediction
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Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
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Self-citationBridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector
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User Characteristics in Explainable AI: The Rabbit Hole of Personalization?
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When Algorithms Err : Differential Impact of Early vs. Late Errors on Users’ Reliance on Algorithms
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Regulating Government AI and the Challenge of Sociotechnical Design
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Ghosting the Machine: Judicial Resistance to a Recidivism Risk Assessment Instrument
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Leveraging Expert Consistency to Improve Algorithmic Decision Support
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Self-fulfilling prophecies and machine learning in resuscitation science
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Self-citationCapturing Humans’ Mental Models of AI: An Item Response Theory Approach
Kelly, M., Kumar, A., Smyth, P., & Steyvers, M. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594111 · Cited by 26 · see citing works
“Computer says no”: Algorithmic decision support and organisational responsibility
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Understanding Choice Independence and Error Types in Human-AI Collaboration
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Human-AI Collaboration with Bandit Feedback
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Dignity and use of algorithm in performance evaluation
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Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control
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Self-citationHeterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
Cheng, L., & Chouldechova, A. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555101 · Cited by 23 · see citing works
Self-citationHeterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
Cheng, L., & Chouldechova, A. (2022) · arXiv · doi:10.48550/arxiv.2204.05478 · Cited by 20 · see citing works
Self-citationInvisible clinical labor driving the successful integration of AI in healthcare
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The Externalities of Exploration and How Data Diversity Helps Exploitation
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The Fall of an Algorithm: Characterizing the Dynamics Toward Abandonment
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Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making
Chohlas-Wood, A., Coots, M., Zhu, H., Brunskill, E., & Goel, S. (2024) · Management Science · doi:10.1287/mnsc.2022.00345 · Cited by 19 · see citing works
Selection Problems in the Presence of Implicit Bias
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603209 · Cited by 21 · see citing works
The perceived value of human-AI collaboration in early shape exploration: An exploratory assessment
Arias–Rosales, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274496 · Cited by 20 · see citing works
XAI for learning: Narrowing down the digital divide between “new” and “old” experts
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“Following prior research that describes the potential of ML algorithms to outperform human experts on prediction tasks [17, 20, 34, 39], researchers have investigated the feasibility of deploying these ML-based decision support systems to improve decision makings.”
BackgroundMethodCan Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces
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Fortifying the algorithmic management provisions in the proposed Platform Work Directive
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A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
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Emotion AI Use in U.S. Mental Healthcare: Potentially Unjust and Techno-Solutionist
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Bias, fairness, and assurance in AI: overview and synthesis
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Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
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AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
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A comparative user study of human predictions in algorithm-supported recidivism risk assessment
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Fair Machine Guidance to Enhance Fair Decision Making in Biased People
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Workshop on Trust and Reliance in AI-Human Teams (TRAIT)
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A benchmarking framework and dataset for learning to defer in human-AI decision-making
Alves, J. V., Leitão, D., Jesus, S., Sampaio, M. O. P., Liébana, J., Saleiro, P., Figueiredo, M. A. T., & Bizarro, P. (2025) · Scientific Data · doi:10.1038/s41597-025-04664-y · Cited by 10 · see citing works
“However, in real-world settings, due to limitations in human work capacity and legal restrictions, it is common to use an ML model to screen instances, raising alerts that are then reviewed by human experts 1,28 .”
Influential
- Authors
- De-Arteaga, M., Fogliato, R., & Chouldechova, A.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-12
- OpenAlex
- W3007910004
- Cited by
- 221
- References
- 33
- Citations by year
- 2026: 24 · 2025: 35 · 2024: 34 · 2023: 59 · 2022: 40 · 2021: 24 · 2020: 3 · 2019: 2
- FWCI
- 32.68
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Hillman Foundation
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- human-in-the-loop, algorithmic decision support, child maltreatment screening, child protection services, human-machine decision-making, risk prediction
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.
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