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

TL;DR

The paper introduces the Endless Tuning design method for AI deployment, emphasizing human oversight and responsibility tracing through a double mirroring process, validated in decision-making applications with positive user control perceptions.

Contribution

It presents a novel protocol for AI design that enhances human control and responsibility accountability, demonstrated through practical prototypes and philosophical analysis.

Findings

Full control perceived by users in decision tasks

A bridge between accountability and liability is feasible

Effective in diverse domains like finance, healthcare, and art

Abstract

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.