通过双向镜像设计，让AI决策可追溯且不取代人类。
The Endless Tuning. An Artificial Intelligence Design To Avoid Human Replacement and Trace Back Responsibilities
- 采用双镜像机制，让人类始终掌握控制权。
- 在贷款、诊断等场景中，用户感知到完全控制感。
- 适合关注AI责任归属与伦理的开发者和政策制定者。
Endless Tuning 是一种基于双重镜像过程的AI可靠部署设计方法，旨在避免人类被替代并填补所谓的责任空白（Matthias 2004）。该方法最初由 Fabris 等人（2024）提出，并在此基础上发展为一个可执行协议，已在贷款审批、肺炎诊断和艺术风格识别三个决策场景中实现并测试，涉及多位领域专家。本文逐步展示该协议，结合具体案例揭示一种不同的伦理声音（Gilligan 1993），并提供技术选择的哲学解释（如XAI算法的逆向与诠释性部署）。实验聚焦用户体验而非统计准确性，结果显示：尽管深度学习模型被广泛使用，受访用户仍普遍感受到对决策过程的全面掌控；同时，在损害发生时，问责与追责之间似乎建立了可行桥梁。
原文摘要 · Abstract (English)
The Endless Tuning is a design method for a reliable deployment of artificial intelligence based on a double mirroring process, which pursues both the goals of avoiding human replacement and filling the so-called responsibility gap (Matthias 2004). Originally depicted in (Fabris et al. 2024) and ensuing the relational approach urged therein, it was then actualized in a protocol, implemented in three prototypical applications regarding decision-making processes (respectively: loan granting, pneumonia diagnosis, and art style recognition) and tested with such as many domain experts. Step by step illustrating the protocol, giving insights concretely showing a different voice (Gilligan 1993) in the ethics of artificial intelligence, a philosophical account of technical choices (e.g., a reversed and hermeneutic deployment of XAI algorithms) will be provided in the present study together with the results of the experiments, focusing on user experience rather than statistical accuracy. Even thoroughly employing deep learning models, full control was perceived by the interviewees in the decision-making setting, while it appeared that a bridge can be built between accountability and liability in case of damage.
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