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Found 6 papers, 2 papers with code
FairGBM: Gradient Boosting with Fairness Constraints
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy.
Weakly Supervised Multi-task Learning for Concept-based Explainability
In ML-aided decision-making tasks, such as fraud detection or medical diagnosis, the human-in-the-loop, usually a domain-expert without technical ML knowledge, prefers high-level concept-based explanations instead of low-level explanations based on model features.
Promoting Fairness through Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce.
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
We conducted an experiment following XAI Test to evaluate three popular post-hoc explanation methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
Teaching the Machine to Explain Itself using Domain Knowledge
Moreover, we collect the domain feedback from a pool of certified experts and use it to ameliorate the model (human teaching), hence promoting seamless and better suited explanations.
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
Hence, coupled with the lack of tools for ML practitioners, real-world adoption of bias reduction methods is still scarce.
