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

By Procheta Sen, Debasis Ganguly. 2020

21 citations recorded by the source corpus.

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.

Citations in the corpus

• Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, neutral): “…Webster et al. (2019); Zmigrod et al. (2019); Gyamfi et al. (2020); Hube et al. (2020); Hutchinson et al. (2020); Kim et al. (2020); Nadeem et al. (2020); Papakyriakopoulos et al. (2020); Ravfogel et al. (2020); Rozado (2020); Sen and Ganguly (2020); Shin et al. (2020); Strengers et al. (2020).”
• Theories of “Gender” in NLP Bias Research (2022, unknown): “com/jsedoc/ [129] Procheta Sen and Debasis Ganguly.”