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Found 23 papers, 12 papers with code
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging.
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable.
Navigating Explanatory Multiverse Through Counterfactual Path Geometry
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models.
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility
Ante-hoc interpretability has become the holy grail of explainable artificial intelligence for high-stakes domains such as healthcare; however, this notion is elusive, lacks a widely-accepted definition and depends on the operational context.
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike.
More Is Less: When Do Recommenders Underperform for Data-rich Users?
Users of recommender systems tend to differ in their level of interaction with these algorithms, which may affect the quality of recommendations they receive and lead to undesirable performance disparity.
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations
Explainable artificial intelligence techniques are developed at breakneck speed, but suitable evaluation approaches lag behind.
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
Over the past decade explainable artificial intelligence has evolved from a predominantly technical discipline into a field that is deeply intertwined with social sciences.
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components
Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable.
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life.
Simply Logical -- Intelligent Reasoning by Example (Fully Interactive Online Edition)
"Simply Logical -- Intelligent Reasoning by Example" by Peter Flach was first published by John Wiley in 1994.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.
Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity
While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences.
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence
This approach allows us to define explainability as (logical) reasoning applied to transparent insights (into, possibly black-box, predictive systems) interpreted under background knowledge and placed within a specific context -- a process that engenders understanding in a selected group of explainees.
BIG-bench Machine Learning
Explainable artificial intelligence
+3
You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source
We offer a proof-of-concept workflow that composes Jupyter Book (an online document), Jupyter Notebook (a computational narrative) and reveal. js slides from a single markdown source file.
Interpretable Representations in Explainable AI: From Theory to Practice
Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms.
LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes
Explainable machine learning provides tools to better understand predictive models and their decisions, but many such methods are limited to producing insights with respect to a single class.
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations -- a state-of-the-art approach to Interpretable Machine Learning.
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches
When used as a Work Sheet, our taxonomy can guide the development of new explainability approaches by aiding in their critical evaluation along the five proposed dimensions.
bLIMEy: Surrogate Prediction Explanations Beyond LIME
Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i. e., can be retrofitted).
FACE: Feasible and Actionable Counterfactual Explanations
First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e. g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives.
HyperStream: a Workflow Engine for Streaming Data
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities.
