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.

快速发展的人工智能以人工指导为基础的合作副驾驶模式，与仅仅是工具的角色相比，正在改变知识工作的各个方面，并将其影响扩展到日常生活和专业领域。本论文旨在从与人机交互和人因工程学等学科相关的研究和文献中强调在人工智能交互中保持人类监督的重要性，并呼吁通过强调积极的人类参与、控制和技能提升来设计人工智能合作伙伴关系，以促进和谐、有效和赋能的人工智能关系。

AI 副驾驶崛起：来自航空领域及其他领域的设计经验教训

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

Like many optimizers, Bayesian optimization often falls short of gaining user
trust due to opacity. While attempts have been made to develop human-centric
optimizers, they typically assume user knowledge is well-specified and
error-free, employing users mainly as supervisors of the optimization process.
We relax these assumptions and propose a more balanced human-AI partnership
with our Collaborative and Explainable Bayesian Optimization (CoExBO)
framework. Instead of explicitly requiring a user to provide a knowledge model,
CoExBO employs preference learning to seamlessly integrate human insights into
the optimization, resulting in algorithmic suggestions that resonate with user
preference. CoExBO explains its candidate selection every iteration to foster
trust, empowering users with a clearer grasp of the optimization. Furthermore,
CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with
extreme adversarial interventions, the algorithm converges asymptotically to a
vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI
teaming experiments in lithium-ion battery design, highlighting substantial
improvements over conventional methods.

我们提出了一种协作和可解释的贝叶斯优化框架（CoExBO），它通过优先学习无缝地将人类洞察力整合到优化中，为用户提供与其偏好相符的算法建议，并通过解释候选项选择来建立信任，使用户对优化有更清晰的掌握。此外，CoExBO 提供不会造成损害的保证，允许用户犯错误；即使进行极端对抗性干预，该算法也渐进地收敛到一个标准的贝叶斯优化方法。通过在锂离子电池设计中进行人工智能协作实验，我们验证了 CoExBO 的有效性，并凸显了与传统方法相比的显著改进。