Explainability-by-Design: A Methodology to Support Explanations in Decision-Making Systems

Huynh, Trung Dong; Tsakalakis, Nikolaos; Helal, Abdelsalam; Stalla-Bourdillon, Sophie; Moreau, Luc

doi:10.48550/arxiv.2206.06251

Cited by 5 publications

(4 citation statements)

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“…The number of as well as the dependencies between aspects require XAI to be approached "by design". If an AI system is developed whose outcomes have a major impact on stakeholders such as customers, professionals, and regulators, it is crucial to consider what and how to explain to those stakeholders from the outset [43]. Our conceptual model can serve as a starting point to develop a methodology for XAI by design and as a taxonomy for method engineering of XAI related methods, techniques, and tools.…”

Section: Discussionmentioning

confidence: 99%

A Conceptual Model for Implementing Explainable AI by Design: Results of an Empirical Study

Van Den Berg,

Kuiper,

Van Der Haas

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.

show abstract

Section: Discussionmentioning

confidence: 99%

A Conceptual Model for Implementing Explainable AI by Design: Results of an Empirical Study

Van Den Berg,

Kuiper,

Van Der Haas

et al. 2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…Ongoing research in the field of XAI might make it possible that new techniques will be developed that make it easier to explain and understand complex AI models. For example, Explainability-by-Design [72] takes proactive measures to include explanation capability in the design of decision-making systems so that no post-hoc explanations are needed. However, there is also the possibility that the complexity of AI models will overtake our ability to understand and explain them.…”

Section: Increasing Complexity In the Futurementioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Hulsen

2023

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Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

show abstract

“…Ongoing research in the field of XAI might make it possible that new techniques will be developed that make it easier to explain and understand and complex AI models. For example, Explainability-by-Design [48] takes proactive measures to include explanation capability in the design of decision-making systems, so that no post-hoc explanations are needed. However, there is also the possibility that the complexity of AI models will overtake our ability to understand and explain them.…”

Section: Increasing Complexity In the Futurementioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Healthcare

Hulsen¹

2023

Preprint

View full text Add to dashboard Cite

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

show abstract

Explainability-by-Design: A Methodology to Support Explanations in Decision-Making Systems

Cited by 5 publications

References 0 publications

A Conceptual Model for Implementing Explainable AI by Design: Results of an Empirical Study

A Conceptual Model for Implementing Explainable AI by Design: Results of an Empirical Study

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Explainable Artificial Intelligence (XAI) in Healthcare

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