Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2024 - 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 …
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 of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
A number of algorithms in the field of artificial intelligence offer poorly interpretable
decisions. To disclose the reasoning behind such algorithms, their output can be explained …
decisions. To disclose the reasoning behind such algorithms, their output can be explained …
[HTML][HTML] Evaluating XAI: A comparison of rule-based and example-based explanations
Abstract Current developments in Artificial Intelligence (AI) led to a resurgence of
Explainable AI (XAI). New methods are being researched to obtain information from AI …
Explainable AI (XAI). New methods are being researched to obtain information from AI …
FACE: feasible and actionable counterfactual explanations
Work in Counterfactual Explanations tends to focus on the principle of" the closest possible
world" that identifies small changes leading to the desired outcome. In this paper we argue …
world" that identifies small changes leading to the desired outcome. In this paper we argue …
[HTML][HTML] Human-centered XAI: Developing design patterns for explanations of clinical decision support systems
TAJ Schoonderwoerd, W Jorritsma, MA Neerincx… - International Journal of …, 2021 - Elsevier
Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing
transparency of machine learning models. More recently, the focus on human-centered …
transparency of machine learning models. More recently, the focus on human-centered …
Towards a theory of longitudinal trust calibration in human–robot teams
The introduction of artificial teammates in the form of autonomous social robots, with fewer
social abilities compared to humans, presents new challenges for human–robot team …
social abilities compared to humans, presents new challenges for human–robot team …
On completeness-aware concept-based explanations in deep neural networks
Human explanations of high-level decisions are often expressed in terms of key concepts
the decisions are based on. In this paper, we study such concept-based explainability for …
the decisions are based on. In this paper, we study such concept-based explainability for …
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
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 …
Do people engage cognitively with AI? Impact of AI assistance on incidental learning
KZ Gajos, L Mamykina - … of the 27th International Conference on …, 2022 - dl.acm.org
When people receive advice while making difficult decisions, they often make better
decisions in the moment and also increase their knowledge in the process. However, such …
decisions in the moment and also increase their knowledge in the process. However, such …