4 papers
Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop
Valerie Bürger, Marlie Besouw, Jana Fehr +26
Meta-research and Trustworthy AI (TAI) share common goals, namely improving evidence, robustness, and transparency, yet there is very little interplay between the two fields. To in…
Transparency and Proportionality in Post-Processing Algorithmic Bias Correction
Juliett Suárez Ferreira, Marija Slavkovik, Jorge Casillas
Algorithmic decision-making systems sometimes produce errors or skewed predictions toward a particular group, leading to unfair results. Debiasing practices, applied at different s…
Uncovering Fairness through Data Complexity as an Early Indicator
Juliett Suárez Ferreira, Marija Slavkovik, Jorge Casillas
Fairness constitutes a concern within machine learning (ML) applications. Currently, there is no study on how disparities in classification complexity between privileged and unpriv…
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness
Juliett Suárez Ferreira, Marija Slavkovik, Jorge Casillas
Current fairness metrics and mitigation techniques provide tools for practitioners to asses how non-discriminatory Automatic Decision Making (ADM) systems are. What if I, as an ind…