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Found 11 papers, 3 papers with code
Model Agnostic Explainable Selective Regression via Uncertainty Estimation
Such a framework is known as selective prediction.
Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics
Deontological ethics, specifically understood through Immanuel Kant, provides a moral framework that emphasizes the importance of duties and principles, rather than the consequences of action.
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI
As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing.
Explanation Shift: How Did the Distribution Shift Impact the Model?
Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions.
Beyond Demographic Parity: Redefining Equal Treatment
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics.
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples.
Introducing explainable supervised machine learning into interactive feedback loops for statistical production system
For this approach we make use of explainable supervised machine learning to (a) identify the types of exceptions and (b) to prioritize which exceptions are more likely to require an intervention or correction by the NCBs.
Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available.
Fairness Implications of Encoding Protected Categorical Attributes
We investigate the interaction between categorical encodings and target encoding regularization methods that reduce unfairness.
Desiderata for Explainable AI in statistical production systems of the European Central Bank
Explainable AI constitutes a fundamental step towards establishing fairness and addressing bias in algorithmic decision-making.
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures.
