# Evaluating Human-AI Collaboration: A Review and Methodological Framework

arXiv: https://arxiv.org/abs/2407.19098 · HTML: https://mlsanity.com/paper/2407.19098/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 155 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 30 of 41)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Do Large Language Models Know What They Don't Know?](https://mlsanity.com/paper/2305.18153/) (2023)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning](https://mlsanity.com/paper/2307.09474/) (2023)
- [Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems](https://mlsanity.com/paper/2204.07666/) (2022)
- [Determinants of LLM-assisted Decision-Making](https://mlsanity.com/paper/2402.17385/) (2024)
- [Supporting Human-AI Collaboration in Auditing LLMs with LLMs](https://mlsanity.com/paper/2304.09991/) (2023)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts](https://mlsanity.com/paper/2206.07948/) (2022)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021)
- [Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making](https://mlsanity.com/paper/2403.16812/) (2024)
- [Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making](https://mlsanity.com/paper/2208.07960/) (2022)
- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness](https://mlsanity.com/paper/2203.07653/) (2022)
- [Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advice](https://mlsanity.com/paper/2103.02381/) (2021)

## Built on by (most cited first, 27 of 27)

- [CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding](https://mlsanity.com/paper/2512.04111/) (2025)
- [Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications](https://mlsanity.com/paper/2508.12358/) (2025)
- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [Interaction Dynamics as a Reward Signal for LLMs](https://mlsanity.com/paper/2511.08394/) (2025)
- [When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration](https://mlsanity.com/paper/2506.05579/) (2025)
- [Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement](https://mlsanity.com/paper/2603.00539/) (2026)
- [Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care](https://mlsanity.com/paper/2503.16456/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Causal Responsibility Attribution for Human-AI Collaboration](https://mlsanity.com/paper/2411.03275/) (2024)
- [A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models](https://mlsanity.com/paper/2504.02793/) (2025)
- [Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach](https://mlsanity.com/paper/2508.03673/) (2025)
- [Collaborative Causal Sensemaking: Closing the Complementarity Gap in Human-AI Decision Support](https://mlsanity.com/paper/2512.07801/) (2025)
- [AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science](https://mlsanity.com/paper/2603.19005/) (2026)
- [HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization](https://mlsanity.com/paper/2606.26614/) (2026)
- [Towards Conversational AI for Human-Machine Collaborative MLOps](https://mlsanity.com/paper/2504.12477/) (2025)
- [Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate](https://mlsanity.com/paper/2603.08017/) (2026)
- ["I Didn't Make the Micro Decisions": Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration](https://mlsanity.com/paper/2605.21363/) (2026)
- [From Task Success to Productive Success: Evaluating Human-AI Collaboration by Quality and Cost](https://mlsanity.com/paper/2609.21117/) (2026)
- [AI-Empowered Human Research Integrating Brain Science and Social Sciences Insights](https://mlsanity.com/paper/2411.12761/) (2024)
- [YRC-Bench: A Benchmark for Learning to Coordinate with Experts](https://mlsanity.com/paper/2502.09583/) (2025)
- [The Human-Data-Model Interaction Canvas for Visual Analytics](https://mlsanity.com/paper/2505.07534/) (2025)
- [Epitome: Pioneering an Experimental Platform for AI-Social Science Integration](https://mlsanity.com/paper/2507.01061/) (2025)
- [Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study](https://mlsanity.com/paper/2510.25016/) (2025)
- [Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains](https://mlsanity.com/paper/2605.26146/) (2026)
- [Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence](https://mlsanity.com/paper/2606.12441/) (2026)
- [Judgment-Grounded Expansion for Peer Review Generation](https://mlsanity.com/paper/2606.23233/) (2026)
- [AI Evaluation Should Work With Humans](https://mlsanity.com/paper/2608.13577/) (2026)

## Abstract (arXiv)

The use of artificial intelligence (AI) in working environments with individuals, known as Human-AI Collaboration (HAIC), has become essential in a variety of domains, boosting decision-making, efficiency, and innovation. Despite HAIC's wide potential, evaluating its effectiveness remains challenging due to the complex interaction of components involved. This paper provides a detailed analysis of existing HAIC evaluation approaches and develops a fresh paradigm for more effectively evaluating these systems. Our framework includes a structured decision tree which assists to select relevant metrics based on distinct HAIC modes (AI-Centric, Human-Centric, and Symbiotic). By including both quantitative and qualitative metrics, the framework seeks to represent HAIC's dynamic and reciprocal nature, enabling the assessment of its impact and success. This framework's practicality can be examined by its application in an array of domains, including manufacturing, healthcare, finance, and education, each of which has unique challenges and requirements. Our hope is that this study will facilitate further research on the systematic evaluation of HAIC in real-world applications.