Counterfactual explanations and algorithmic recourses for machine learning: A review
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 …
difficult or impossible to understand by human stakeholders. Explaining, in a human …
Decision trees: from efficient prediction to responsible AI
This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …
data science over roughly four decades. It sketches the evolution of decision tree research …
Causal machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
Robust counterfactual explanations for neural networks with probabilistic guarantees
There is an emerging interest in generating robust counterfactual explanations that would
remain valid if the model is updated or changed even slightly. Towards finding robust …
remain valid if the model is updated or changed even slightly. Towards finding robust …
Robust counterfactual explanations for tree-based ensembles
Counterfactual explanations inform ways to achieve a desired outcome from a machine
learning model. However, such explanations are not robust to certain real-world changes in …
learning model. However, such explanations are not robust to certain real-world changes in …
The inadequacy of Shapley values for explainability
This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …
AI (XAI) will necessarily yield provably misleading information about the relative importance …
From shapley values to generalized additive models and back
In explainable machine learning, local post-hoc explanation algorithms and inherently
interpretable models are often seen as competing approaches. This work offers a partial …
interpretable models are often seen as competing approaches. This work offers a partial …
Efficient xai techniques: A taxonomic survey
Recently, there has been a growing demand for the deployment of Explainable Artificial
Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods …
Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods …
Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
This research utilized machine learning to analyze experiments conducted in an open
channel laboratory setting to predict microplastic transport with varying discharge, velocity …
channel laboratory setting to predict microplastic transport with varying discharge, velocity …
Manifold-based shapley explanations for high dimensional correlated features
Explainable artificial intelligence (XAI) holds significant importance in enhancing the
reliability and transparency of network decision-making. SHapley Additive exPlanations …
reliability and transparency of network decision-making. SHapley Additive exPlanations …