# The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond

arXiv: https://arxiv.org/abs/2311.14713 · HTML: https://mlsanity.com/paper/2311.14713/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging](https://mlsanity.com/paper/2205.09696/) (2022)
- [When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming](https://mlsanity.com/paper/2306.04930/) (2023)
- [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, 15 of 15)

- [NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription](https://mlsanity.com/paper/2401.08887/) (2024)
- [Build Agent Advocates, Not Platform Agents](https://mlsanity.com/paper/2505.04345/) (2025)
- [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026)
- [Generative AI Uses and Risks for Knowledge Workers in a Science Organization](https://mlsanity.com/paper/2501.16577/) (2025)
- [Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software](https://mlsanity.com/paper/2504.15549/) (2025)
- [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [ChatGPT in Data Visualization Education: A Student Perspective](https://mlsanity.com/paper/2405.00748/) (2024)
- [Design and evaluation of AI copilots -- case studies of retail copilot templates](https://mlsanity.com/paper/2407.09512/) (2024)
- [Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2505.21907/) (2025)
- [Can Agents Judge Systematic Reviews Like Humans? Evaluating SLRs with LLM-based Multi-Agent System](https://mlsanity.com/paper/2509.17240/) (2025)
- [The Psychology of Learning from Machines: Anthropomorphic AI and the Paradox of Automation in Education](https://mlsanity.com/paper/2601.06172/) (2026)
- [Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and Acacias](https://mlsanity.com/paper/2407.11360/) (2024)
- [The Model Mastery Lifecycle: A Framework for Designing Human-AI Interaction](https://mlsanity.com/paper/2408.12781/) (2024)
- [From Individual Prompts to Collective Intelligence: Mainstreaming Generative AI in the Classroom](https://mlsanity.com/paper/2601.06171/) (2026)

## Abstract (arXiv)

The fast pace of advances in AI promises to revolutionize various aspects of knowledge work, extending its influence to daily life and professional fields alike. We advocate for a paradigm where AI is seen as a collaborative co-pilot, working under human guidance rather than as a mere tool. Drawing from relevant research and literature in the disciplines of Human-Computer Interaction and Human Factors Engineering, we highlight the criticality of maintaining human oversight in AI interactions. Reflecting on lessons from aviation, we address the dangers of over-relying on automation, such as diminished human vigilance and skill erosion. Our paper proposes a design approach that emphasizes active human engagement, control, and skill enhancement in the AI partnership, aiming to foster a harmonious, effective, and empowering human-AI relationship. We particularly call out the critical need to design AI interaction capabilities and software applications to enable and celebrate the primacy of human agency. This calls for designs for human-AI partnership that cede ultimate control and responsibility to the human user as pilot, with the AI co-pilot acting in a well-defined supporting role.