A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers

Fauvel, Kevin; Masson, Véronique; Fromont, Élisa

doi:10.48550/arxiv.2005.14501

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…This mental model can be evaluated on criteria such as correctness, comprehensiveness, coherence, and usefulness. Fauvel et al [69] present a framework that assesses and benchmarks machine learning methods on both performance and explainability. Performance is measured compared to the state-of-the-art, best, similar, or below.…”

Section: How To Measure Explainability?mentioning

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.

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Section: How To Measure Explainability?mentioning

confidence: 99%

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

Hulsen

2023

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“…Fauvel at al. [45] present a framework that assesses and benchmarks machine learning methods on both performance and explainability. For measuring the explainability, they look at model comprehensibility, explanation granularity, information type, faithfulness and user category.…”

Section: How To Measure Explainability?mentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Healthcare

Hulsen¹

2023

Preprint

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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 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.

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“…Fauvel et al [121] position their framework as a significant advancement in the fourth step of the method described by Hall et al [122] regarding explainability. Chakrobartty et al [123] further extended this method by introducing two additional evaluation characteristics: fairness context and fairness.…”

Section: Xai For Bias Evaluationmentioning

confidence: 99%

FairXAI - A Taxonomy and Framework for Fairness and Explainability Synergy in Machine Learning

Ramachandranpillai,

Baeza-Yates,

Heintz

2023

Preprint

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<p>Explainable Artificial Intelligence (XAI) and Fair Learning have made significant strides in various application domains, including criminal recidivism predictions, healthcare settings, toxic comment detection, automatic speech detection, recommendation systems, and image segmentation. However, these two fields have largely evolved independently. Recent studies have demonstrated that incorporating explanations into decision-making processes enhances the transparency and trustworthiness of AI systems. In light of this, our objective is to conduct a systematic review of FairXAI, which explores the interplay between fairness and explainability frameworks. To commence, we propose a taxonomy of FairXAI that utilizes XAI as a means to both mitigate and evaluate bias. This taxonomy will serve as a base for machine learning researchers operating in diverse domains. Additionally, we will undertake an extensive review of existing articles, taking into account factors such as the purpose of the interaction, target audience, and domain and context. Moreover, we outline an interaction framework for FairXAI considering various fairness perceptions and propose a FairXAI wheel that encompasses four core properties that must be verified and evaluated. This will serve as a practical tool for researchers and practitioners, ensuring the fairness and transparency of their AI systems. Furthermore, we will identify challenges and conflicts in the interactions between fairness and explainability, which could potentially pave the way for enhancing the responsibility of AI systems. As the inaugural review of its kind, we hope that this survey will inspire scholars to address these challenges by scrutinizing current research in their respective domains. </p>

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A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers

Cited by 4 publications

References 20 publications

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

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

Explainable Artificial Intelligence (XAI) in Healthcare

FairXAI - A Taxonomy and Framework for Fairness and Explainability Synergy in Machine Learning

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