Designing Conversational Assistants to Reduce Gender Bias

有偏见的技术使某些社会群体处于不利地位，例如基于种族或性别。最近，有偏见的机器学习受到了越来越多的关注。在这里，我们解决了一种不同类型的偏差，它不是从数据中学习到的，而是在设计过程中编码的。我们以对话助手为例来说明这个问题，比如亚马逊的Alexa、苹果的Siri、微软的Cortana或b谷歌的Assistant，这些助手主要是年轻、顺从的女性。教科文组织认为，这有可能强化性别陈规定型观念。在本提案中，我们将通过心理学研究来探讨对话性别（通过声音、内容和风格表达）如何在在线和离线互动中影响人类行为。基于所获得的见解，我们将建立一个原则性框架，用于设计和开发不太可能使偏见永久化的替代对话角色。人物角色可以被看作是身份（背景事实或用户简介）、语言行为和交互风格等元素的组合。该框架将包括最先进的数据高效NLP深度学习工具，用于生成与给定角色一致的对话响应。人物角色参数可以由非专业用户指定，以促进更具包容性的设计，并使更广泛的批判性讨论成为可能。

Biased technology disadvantages certain groups of society, e.g. based on their race or gender. Recently, biased machine learning has received increased attention. Here, we address a different type of bias which is not learnt from data, but encoded during the design process. We illustrate this problem on the example of Conversational Assistants, such as Amazon's Alexa, Apple's Siri, Microsoft's Cortana, or Google's Assistant, which are predominately modelled as young, submissive women. According to UNESCO, this bears the risk of reinforcing gender stereotypes.In this proposal, we will explore this claim via psychological studies on how conversational gendering (expressed through voice, content and style) influences human behaviour in both online and offline interactions. Based on the insights gained, we will establish a principled framework for designing and developing alternative conversational personas which are less likely to perpetuate bias. A persona can be viewed as a composite of elements of identity (background facts or user profile), language behaviour, and interaction style. This framework will include state-of-the-art data-efficient NLP deep learning tools for generating dialogue responses which are consistent with a given persona. The persona parameters can be specified by non-expert users in order to to facilitate more inclusive design, as well as to enable a wider critical discussion.