Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning

• URL: http://arxiv.org/abs/2005.06618v2
• Date: Tue, 28 Jul 2020 07:20:09 GMT
• Title: Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
• Authors: Procheta Sen, Debasis Ganguly
• Abstract summary: Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice. We propose a bias-aware multi-objective learning framework that learns to reduce the frequency of predicting certain combinations of them.
• Score: 24.522730093209262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 inhuman 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 predictied emotions.

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