Feminist Perspective on Robot Learning Processes

Abstract

As different research works report and daily life experiences confirm, learning models can result in biased outcomes. The biased learned models usually replicate historical discrimination in society and typically negatively affect the less represented identities. Robots are equipped with these models that allow them to operate, performing tasks more complex every day. The learning process consists of different stages depending on human judgments. Moreover, the resulting learned models for robot decisions rely on recorded labeled data or demonstrations. Therefore, the robot learning process is susceptible to bias linked to human behavior in society. This imposes a potential danger, especially when robots operate around humans and the learning process can reflect the social unfairness present today. Different feminist proposals study social inequality and provide essential perspectives towards removing bias in various fields. What is more, feminism allowed and still allows to reconfigure numerous social dynamics and stereotypes advocating for equality across people through their diversity. Consequently, we provide a feminist perspective on the robot learning process in this work. We base our discussion on intersectional feminism, community feminism, decolonial feminism, and pedagogy perspectives, and we frame our work in a feminist robotics approach. In this paper, we present an initial discussion to emphasize the relevance of feminist perspectives to explore, foresee, en eventually correct the biased robot decisions.

Gender role stereotypes at work in humanoid robots

“…Discrimination in relation to artificial systems has been analysed from different point of view, also from a feminist perspective, as a phenomenon that may depend on several causal factors and having various consequences (Bardzell 2010;Esposito et al 2020). It has been pointed out, for example, that it is important to ensure that artificial agents implemented with learning algorithms do not have biases within these same algorithms regarding some characteristics of the target populations (Hurtado and Mejia 2022). Otherwise, they could lead to the exclusion of some contexts, or parts of the population, from their learning algorithms, and thus perpetuate or emphasise some features and domains at the expense of others.…”