走向社会责任的AI：认知偏差意识的多目标学习
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
摘要：
人类社会有着长期的认知偏见，导致社会偏见和大规模不公正。大量历史数据中普遍存在的认知偏见可能会构成威胁，表现为不道德和看似不人道的预测，作为在这些数据上训练的人工智能系统的输出。为了缓解这个问题，我们提出了一个偏见意识的多目标学习框架，给定一组身份属性（例如性别，种族等）。以及预测输出的可能类别的敏感类别的子集，学习减少预测它们的某些组合的频率，例如预测诸如“大多数黑人使用辱骂性语言”或“恐惧是女性的美德”之类的刻板印象。我们在具有平衡类先验的情感预测任务上进行的实验表明，一组基线偏差不可知模型表现出与性别有关的认知偏差，例如女性倾向于害怕，而男性更倾向于愤怒。相比之下，我们提出的偏见意识的多目标学习方法，以减少这种偏见的预测情绪。
英文摘要：
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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