# Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task

arXiv: https://arxiv.org/abs/2410.11860 · HTML: https://mlsanity.com/paper/2410.11860/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 71 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [TAO: A Large-Scale Benchmark for Tracking Any Object](https://mlsanity.com/paper/2005.10356/) (2020)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://mlsanity.com/paper/2104.00194/) (2021)
- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play](https://mlsanity.com/paper/1810.09648/) (2018)
- [CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis](https://mlsanity.com/paper/2001.05149/) (2020)
- [Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration](https://mlsanity.com/paper/1801.09854/) (2018)

## Built on by (most cited first, 8 of 8)

- [Seeing Eye to Eye: Enabling Cognitive Alignment Through Shared First-Person Perspective in Human-AI Collaboration](https://mlsanity.com/paper/2603.12701/) (2026)
- [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026)
- [A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health](https://mlsanity.com/paper/2411.02594/) (2024)
- [Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective](https://mlsanity.com/paper/2504.10918/) (2025)
- [What Types of Human-AI Teams Exist?](https://mlsanity.com/paper/2607.02198/) (2026)
- [SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis](https://mlsanity.com/paper/2512.08953/) (2025)
- [AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness](https://mlsanity.com/paper/2605.28680/) (2026)
- [The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace](https://mlsanity.com/paper/2606.00182/) (2026)

## Abstract (arXiv)

When designing an AI-assisted decision-making system, there is often a tradeoff between precision and recall in the AI's recommendations. We argue that careful exploitation of this tradeoff can harness the complementary strengths in the human-AI collaboration to significantly improve team performance. We investigate a real-world video anonymization task for which recall is paramount and more costly to improve. We analyze the performance of 78 professional annotators working with a) no AI assistance, b) a high-precision "restrained" AI, and c) a high-recall "zealous" AI in over 3,466 person-hours of annotation work. In comparison, the zealous AI helps human teammates achieve significantly shorter task completion time and higher recall. In a follow-up study, we remove AI assistance for everyone and find negative training effects on annotators trained with the restrained AI. These findings and our analysis point to important implications for the design of AI assistance in recall-demanding scenarios.