Explainable AI (XAI): Core ideas, techniques, and solutions
As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …
transparent and interpretable models. In addition, the ability to explain the model generally …
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 …
Trustworthy artificial intelligence: a review
Artificial intelligence (AI) and algorithmic decision making are having a profound impact on
our daily lives. These systems are vastly used in different high-stakes applications like …
our daily lives. These systems are vastly used in different high-stakes applications like …
Deep neural networks and tabular data: A survey
Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …
numerous critical and computationally demanding applications. On homogeneous datasets …
Explaining machine learning models with interactive natural language conversations using TalkToModel
Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …
more complex and harder to understand. To understand complex models, researchers have …
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 …
Counterfactual attention learning for fine-grained visual categorization and re-identification
Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …
tasks. In this paper, we present a counterfactual attention learning method to learn more …
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 …
[PDF][PDF] Ai transparency in the age of llms: A human-centered research roadmap
The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …
for innovation but also looming risks for individuals and society at large. We have reached a …
[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 …