Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
Autor: | Procheta Sen, Debasis Ganguly |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Class (computer programming) Computer Science - Machine Learning Virtue Computer Science - Computation and Language Computer science media_common.quotation_subject Contrast (statistics) Identity (social science) General Medicine Injustice Cognitive bias Task (project management) Machine Learning (cs.LG) Computer Science - Computers and Society Computers and Society (cs.CY) 10. No inequality Set (psychology) Prejudice Social responsibility Computation and Language (cs.CL) media_common Cognitive psychology |
Zdroj: | AAAI |
Popis: | Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice. The prevalent existence of cognitive biases in large volumes of historical data can pose a threat of being manifested as unethical and seemingly inhumane predictions as outputs of AI systems trained on such data. To alleviate this problem, we propose a bias-aware multi-objective learning framework that given a set of identity attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories of the possible classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as ‘most blacks use abusive language’, or ‘fear is a virtue of women’. Our experiments conducted on an emotion prediction task with balanced class priors shows that a set of baseline bias-agnostic models exhibit cognitive biases with respect to gender, such as women are prone to be afraid whereas men are more prone to be angry. In contrast, our proposed bias-aware multi-objective learning methodology is shown to reduce such biases in the predictid emotions. |
Databáze: | OpenAIRE |
Externí odkaz: |