Towards an Explanation Space to Align Humans and Explainable-AI Teamwork

Cabour, Garrick; Morales, Andrés; Ledoux, Élise; Bassetto, Samuel

doi:10.48550/arxiv.2106.01503

Cited by 2 publications

(1 citation statement)

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“…In conclusion, engaging humans in XAI is fundamental, as they are the target of the explanations and improving our understanding of their behaviour when interacting with explanations and models is beneficial to improving the design and development of explanations. Furthermore, it is desirable to design flexible explanation approaches and explainability methods able to properly convey model behaviour depending on "who" the human is [91,93,94]. A categorisation of the main user groups is provided by Turró [93].…”

Section: Understanding the Human's Perspective In Explainable Aimentioning

confidence: 99%

The Role of Human Knowledge in Explainable AI

Tocchetti

Brambilla

2022

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As the performance and complexity of machine learning models have grown significantly over the last years, there has been an increasing need to develop methodologies to describe their behaviour. Such a need has mainly arisen due to the widespread use of black-box models, i.e., high-performing models whose internal logic is challenging to describe and understand. Therefore, the machine learning and AI field is facing a new challenge: making models more explainable through appropriate techniques. The final goal of an explainability method is to faithfully describe the behaviour of a (black-box) model to users who can get a better understanding of its logic, thus increasing the trust and acceptance of the system. Unfortunately, state-of-the-art explainability approaches may not be enough to guarantee the full understandability of explanations from a human perspective. For this reason, human-in-the-loop methods have been widely employed to enhance and/or evaluate explanations of machine learning models. These approaches focus on collecting human knowledge that AI systems can then employ or involving humans to achieve their objectives (e.g., evaluating or improving the system). This article aims to present a literature overview on collecting and employing human knowledge to improve and evaluate the understandability of machine learning models through human-in-the-loop approaches. Furthermore, a discussion on the challenges, state-of-the-art, and future trends in explainability is also provided.

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Section: Understanding the Human's Perspective In Explainable Aimentioning

confidence: 99%