1 citations · 3 across the 8 of their papers we have counts for
8 papers
Intersectional Fairness via Mixed-Integer Optimization
Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2
The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent. While regulatory frameworks,…
Tractable Probabilistic Models for Investment Planning
Nicolas M. Cuadrado A., Mohannad Takrouri, Jiří Němeček +2
Investment planning in power utilities, such as generation and transmission expansion, requires decisions under substantial uncertainty over decade--long horizons for policies, dem…
humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models
German M. Matilla, Jiri Nemecek, Illia Kryvoviaz +1
There is a strong recent emphasis on trustworthy AI. In particular, international regulations, such as the AI Act, demand that AI practitioners measure data quality on the input an…
Plausible Counterfactual Explanations of Recommendations
Jakub Černý, Jiří Němeček, Ivan Dovica +1
Explanations play a variety of roles in various recommender systems, from a legally mandated afterthought, through an integral element of user experience, to a key to persuasivenes…
Bias Detection via Maximum Subgroup Discrepancy
Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2
Bias evaluation is fundamental to trustworthy AI, both in terms of checking data quality and in terms of checking the outputs of AI systems. In testing data quality, for example, o…
Generating Likely Counterfactuals Using Sum-Product Networks
Jiri Nemecek, Tomas Pevny, Jakub Marecek
The need to explain decisions made by AI systems is driven by both recent regulation and user demand. The decisions are often explainable only post hoc. In counterfactual explanati…
Piecewise Polynomial Regression of Tame Functions via Integer Programming
Gilles Bareilles, Johannes Aspman, Jiri Nemecek +1
Tame functions are a class of nonsmooth, nonconvex functions, which feature in a wide range of applications: functions encountered in the training of deep neural networks with all…
Improving the Validity of Decision Trees as Explanations
Jiri Nemecek, Tomas Pevny, Jakub Marecek
In classification and forecasting with tabular data, one often utilizes tree-based models. Those can be competitive with deep neural networks on tabular data and, under some condit…