Towards explainable, compliant and adaptive human-automation interaction

• AI-based systems use trained machine learning models to make important decisions in critical contexts. The EU guidelines for trustworthy AI emphasise the respect for human autonomy, prevention of harm, fairness, and explicability. Many successful machine learning methods, however, deliver opaque models where the reasons for decisions remain unclear to the end user. Hence, accountability and trust are difficult to ascertain. In this position paper, we focus on AI systems that are expected to interact with humans and we propose our visionary architecture, called ECA-HAI (Explainable, Compliant and Adaptive Human-Automation Interaction)-RefArch. ECA-HAI-RefArch allows for building intelligent systems where humans and AIs form teams, able to learn from data but also to learn from each other by playing “serious games”, for a continuous improvement of the overall system. Finally, conclusions are drawn.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Compliant AI

Explainable AI

Programme synthesis

Serious games

Intelligent systems

Machine learning

AI systems

Building intelligent systems

Continuous improvements

EU guidelines

Human-automation interactions

Machine learning methods

Machine learning models

Position papers

Man machine systems

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)