Search Results for author:
Found 6 papers, 3 papers with code
FairBelief - Assessing Harmful Beliefs in Language Models
Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing.
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions.
Correlation and Unintended Biases on Univariate and Multivariate Decision Trees
Decision Trees are accessible, interpretable, and well-performing classification models.
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective
While a substantial amount of work has recently been devoted to enhance the performance of computational Authorship Identification (AId) systems, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions.
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).
GLocalX -- From Local to Global Explanations of Black Box AI Models
Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other.
