Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C Rudin - Nature machine intelligence, 2019 - nature.com
Black box machine learning models are currently being used for high-stakes decision
making throughout society, causing problems in healthcare, criminal justice and other …
making throughout society, causing problems in healthcare, criminal justice and other …
Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …
A survey on the explainability of supervised machine learning
N Burkart, MF Huber - Journal of Artificial Intelligence Research, 2021 - jair.org
Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …
often perceive the models as black boxes. Insights about the decision making are mostly …
A survey of methods for explaining black box models
In recent years, many accurate decision support systems have been constructed as black
boxes, that is as systems that hide their internal logic to the user. This lack of explanation …
boxes, that is as systems that hide their internal logic to the user. This lack of explanation …
[PDF][PDF] The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.
ZC Lipton - Queue, 2018 - dl.acm.org
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both
important and slippery. Page 1 acmqueue | may-june 2018 1 machine learning Supervised …
important and slippery. Page 1 acmqueue | may-june 2018 1 machine learning Supervised …
[HTML][HTML] Notions of explainability and evaluation approaches for explainable artificial intelligence
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …
the last few years. This is due to the widespread application of machine learning, particularly …
Interpretable machine learning–a brief history, state-of-the-art and challenges
We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
Evaluating the quality of machine learning explanations: A survey on methods and metrics
The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …
users, and even experts are often unable to understand the rationale behind their decisions …
From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
black boxes raised the question of how to evaluate explanations of machine learning (ML) …