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The Endless Tuning. An Artificial Intelligence Design To Avoid Human Replacement and Trace Back Responsibilities

arXiv 2507.14909 ↗First posted 20 Jul 2025cs.AI cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 92 references that are in the map. First posted on arXiv 20 Jul 2025.

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References · 28

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16 more
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  16. 2020Verifying the Causes of Adversarial Examplescited by 2

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