# Offloading Score: Measuring AI Reliance Through Counterfactual Workflows

arXiv: https://arxiv.org/abs/2605.29392 · HTML: https://mlsanity.com/paper/2605.29392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 23 of 23)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [CollabLLM: From Passive Responders to Active Collaborators](https://mlsanity.com/paper/2502.00640/) (2025)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Grounding Gaps in Language Model Generations](https://mlsanity.com/paper/2311.09144/) (2023)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [SWE-chat: Coding Agent Interactions From Real Users in the Wild](https://mlsanity.com/paper/2604.20779/) (2026)
- [Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows](https://mlsanity.com/paper/2507.08149/) (2025)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations](https://mlsanity.com/paper/2510.22780/) (2025)
- [Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance](https://mlsanity.com/paper/2407.07950/) (2024)
- [Who's in Charge? Disempowerment Patterns in Real-World LLM Usage](https://mlsanity.com/paper/2601.19062/) (2026)
- [De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Design](https://mlsanity.com/paper/2503.03924/) (2025)
- [Learning Next Action Predictors from Human-Computer Interaction](https://mlsanity.com/paper/2603.05923/) (2026)
- [AI-Assisted Programming Decreases the Productivity of Experienced Developers by Increasing the Technical Debt and Maintenance Burden](https://mlsanity.com/paper/2510.10165/) (2025)
- [Students' Reliance on AI in Higher Education: Identifying Contributing Factors](https://mlsanity.com/paper/2506.13845/) (2025)
- [AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluators](https://mlsanity.com/paper/2512.17267/) (2025)
- [Behavioral Indicators of Overreliance During Interaction with Conversational Language Models](https://mlsanity.com/paper/2602.11567/) (2026)
- [Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology](https://mlsanity.com/paper/2411.00998/) (2024)
- [Investigating the Effects of LLM Use on Critical Thinking Under Time Constraints: Access Timing and Time Availability](https://mlsanity.com/paper/2603.08849/) (2026)
- [How Performance Pressure Influences AI-Assisted Decision Making](https://mlsanity.com/paper/2410.16560/) (2024)
- [Understanding the Effects of AI-Assisted Critical Thinking on Human-AI Decision Making](https://mlsanity.com/paper/2602.10222/) (2026)

## Built on by (most cited first, 1 of 1)

- [LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions](https://mlsanity.com/paper/2609.14849/) (2026)

## Abstract (arXiv)

AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between users and tools. Here, we introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool. Offloading Score is simulation-based -- we construct a counterfactual workflow by estimating how the user would have completed the task without the tool, and then computing the fraction of steps saved by using the tool. We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study (n=40) with developers performing programming tasks using AI tools. We vary time pressure to test whether reliance measures capture the known increase in reliance under time pressure. We show that offloading score detects significantly higher reliance in time-constrained settings (+43%, p=0.018), while usage-based and self-reported baseline measures of reliance do not distinguish the conditions. We complement this with descriptive insights showing that higher reliance manifests as greater delegation of subtasks to the tool and more direct reuse of AI outputs. Finally, we demonstrate an approach of using offloading score in combination with target outcomes of a task (e.g., code understanding) to identify when reliance may be (in)appropriate. Our framework offers two contributions: an instrument users can apply to measure and reflect on their own reliance, and a quantitative signal that agent designers can utilize to mitigate overreliance.