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Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Growing regulatory and societal pressures demand increased transparency in AI, particularly in understanding the decisions made by complex machine learning models. Counterfactual Explanations (CFs) have emerged as a promising technique within Explain
Externí odkaz:
http://arxiv.org/abs/2405.17642
We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization
Externí odkaz:
http://arxiv.org/abs/2405.17640
Autor:
Wielopolski, Patryk1 (AUTHOR) patryk.wielopolski@pwr.edu.pl, Furman, Oleksii1 (AUTHOR), Zięba, Maciej1,2 (AUTHOR)
Publikováno v:
Entropy. Jul2024, Vol. 26 Issue 7, p593. 16p.