|
Fairness in Trustworthy Machine Learning.| title | Fairness in Trustworthy Machine Learning. |
|---|
| start_date | 2026/01/16 |
|---|
| schedule | 14h-16h |
|---|
| online | no |
|---|
| location_info | salle des Colloques |
|---|
| summary | The goal of this talk is to give an overview of my research activities on fairness in trustworthy machine learning and more precisely in classification. I will start the presentation with a high level introduction to fairness in machine learning, explaining where biases may come from, how they may be measured, and how they may be mitigated. Then, I will present some recent work where we study fairness as it interacts with other trustworthiness concepts. |
|---|
| responsibles | Fournier |
|---|
Workflow history| from state (1) | to state | comment | date |
|---|
| submitted | published | | 2026/02/11 13:49 UTC |
| |
|