Assessing Gender Bias in Predictive Algorithms using eXplainable AI
| Autor: | Silvia Ramis, Cristina Manresa-Yee |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Facial expression recognition Order (exchange) Computer science Or education Gender bias Computer Science - Human-Computer Interaction Predictive analytics Cognitive psychology Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) |
| Zdroj: | Interacción |
| DOI: | 10.48550/arxiv.2203.10264 |
| Popis: | Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial). In order to illustrate how important is to count with a diverse training dataset to avoid bias, we manipulate a well-known facial expression recognition dataset to explore gender bias and discuss its implications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|