A systematic review and taxonomy of explanations in decision support and recommender systems
With the recent advances in the field of artificial intelligence, an increasing number of
decision-making tasks are delegated to software systems. A key requirement for the success …
decision-making tasks are delegated to software systems. A key requirement for the success …
Exploring explainability: a definition, a model, and a knowledge catalogue
L Chazette, W Brunotte, T Speith - 2021 IEEE 29th international …, 2021 - ieeexplore.ieee.org
The growing complexity of software systems and the influence of software-supported
decisions in our society awoke the need for software that is transparent, accountable, and …
decisions in our society awoke the need for software that is transparent, accountable, and …
Designing theory-driven user-centric explainable AI
From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-
consequence human decisions. This has spurred the field of explainable AI (XAI). This …
consequence human decisions. This has spurred the field of explainable AI (XAI). This …
Explanations as mechanisms for supporting algorithmic transparency
Transparency can empower users to make informed choices about how they use an
algorithmic decision-making system and judge its potential consequences. However …
algorithmic decision-making system and judge its potential consequences. However …
A survey of data-driven and knowledge-aware explainable ai
We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …
Principles of explanatory debugging to personalize interactive machine learning
How can end users efficiently influence the predictions that machine learning systems make
on their behalf? This paper presents Explanatory Debugging, an approach in which the …
on their behalf? This paper presents Explanatory Debugging, an approach in which the …
Explaining recommendations: Design and evaluation
N Tintarev, J Masthoff - Recommender systems handbook, 2015 - Springer
In recent years, there has been an increased interest in more user-centered evaluation
metrics for recommender systems such as those mentioned in [49]. It has also been …
metrics for recommender systems such as those mentioned in [49]. It has also been …
Measuring the business value of recommender systems
Recommender Systems are nowadays successfully used by all major web sites—from e-
commerce to social media—to filter content and make suggestions in a personalized way …
commerce to social media—to filter content and make suggestions in a personalized way …
Recommender systems: from algorithms to user experience
JA Konstan, J Riedl - User modeling and user-adapted interaction, 2012 - Springer
Since their introduction in the early 1990's, automated recommender systems have
revolutionized the marketing and delivery of commerce and content by providing …
revolutionized the marketing and delivery of commerce and content by providing …
Explainable artificial intelligence: Evaluating the objective and subjective impacts of xai on human-agent interaction
Intelligent agents must be able to communicate intentions and explain their decision-making
processes to build trust, foster confidence, and improve human-agent team dynamics …
processes to build trust, foster confidence, and improve human-agent team dynamics …