# Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control

arXiv: https://arxiv.org/abs/2303.12896 · HTML: https://mlsanity.com/paper/2303.12896/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2023.

## Built on by (most cited first, 12 of 12)

- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning](https://mlsanity.com/paper/2403.05911/) (2024)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process](https://mlsanity.com/paper/2406.08959/) (2024)
- [Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior?](https://mlsanity.com/paper/2508.03168/) (2025)
- [Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study](https://mlsanity.com/paper/2510.23578/) (2025)
- [Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes](https://mlsanity.com/paper/2602.08227/) (2026)
- [User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models](https://mlsanity.com/paper/2312.06826/) (2023)
- [Effects of Generative AI Errors on User Reliance Across Task Difficulty](https://mlsanity.com/paper/2604.04319/) (2026)
- [Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions](https://mlsanity.com/paper/2602.00259/) (2026)
- [Designing AI-Supported Focus Groups: A Role x Modality Playbook](https://mlsanity.com/paper/2606.11835/) (2026)
- [Addressing Trust in AI Systems through Education: A Didactic Perspective](https://mlsanity.com/paper/2609.02453/) (2026)

## Abstract (arXiv)

Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Studies show that giving users outcome control by providing agency over how models' predictions are incorporated into decision-making mitigates algorithm aversion. We study whether algorithm aversion is mitigated by process control, wherein users can decide what input factors and algorithms to use in model training. We conduct a replication study of outcome control, and test novel process control study conditions on Amazon Mechanical Turk (MTurk) and Prolific. Our results partly confirm prior findings on the mitigating effects of outcome control, while also forefronting reproducibility challenges. We find that process control in the form of choosing the training algorithm mitigates algorithm aversion, but changing inputs does not. Furthermore, giving users both outcome and process control does not reduce algorithm aversion more than outcome or process control alone. This study contributes to design considerations around mitigating algorithm aversion.