[HTML][HTML] Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2022 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …
uninterpretable classifiers. One of the most valuable types of explanation consists of …
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) …
A survey of algorithmic recourse: contrastive explanations and consequential recommendations
Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
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 …
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 …
[HTML][HTML] Principles and practice of explainable machine learning
V Belle, I Papantonis - Frontiers in big Data, 2021 - frontiersin.org
Artificial intelligence (AI) provides many opportunities to improve private and public life.
Discovering patterns and structures in large troves of data in an automated manner is a core …
Discovering patterns and structures in large troves of data in an automated manner is a core …
[PDF][PDF] Counterfactual explanations for machine learning: A review
Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
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 …
Questioning the AI: informing design practices for explainable AI user experiences
A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …
the topic. While many recognize the necessity to incorporate explainability features in AI …
Explainable artificial intelligence: a systematic review
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 deep …
years. This is due to the widespread application of machine learning, particularly deep …