Faire avancer la recherche sur l'intelligence artificielle féministe pour faire progresser l'égalité des genres et l'inclusion

Approaches to bias assessment usually require such technical skills that, by design, they leave discrimination experts out. In this paper we present EDIA, a tool that facilitates that experts in discrimination explore social biases in word embeddings and masked language models. Experts can then characterize those biases so that their presence can be assessed more systematically, and actions can be planned to address them. They can work interactively to assess the effects of different characterizations of bias in a given word embedding or language model, which helps to specify informal intuitions in concrete resources for systematic testing.

Automatic processing of language is becoming pervasive in our lives, often taking central roles in our decision making, like choosing the wording for our messages and mails, translating our readings, or even having full conversations with us. Word embeddings are a key component of modern natural language processing systems. They provide a representation of words that has boosted the performance of many applications, working as a semblance of meaning. Word embeddings seem to capture a semblance of the meaning of words from raw text, but, at the same time, they also distill stereotypes and societal biases which are subsequently relayed to the final applications. Such biases can be discriminatory. It is very important to detect and mitigate those biases, to prevent discriminatory behaviors of automated processes, which can be much more harmful than in the case of humans because of their scale. There are currently many tools and techniques to detect and mitigate biases in word embeddings, but they present many barriers for the engagement of people without technical skills. As it happens, most of the experts in bias, either social scientists or people with deep knowledge of the context where bias is harmful, do not have such skills, and they cannot engage in the processes of bias detection because of the technical barriers. We have studied the barriers in existing tools and have explored their possibilities and limitations with different kinds of users. With this exploration, we propose to develop a tool that is specially aimed to lower the technical barriers and provide the exploration power to address the requirements of experts, scientists and people in general who are willing to audit these technologies.

Understanding the social implications arising from the design, development and deployment of artificial intelligence demands a multidisciplinary approach. This approach allows for the analysis of AI as a complex socio-technical system deeply entwined with power dynamics and societal structures. During the last three years, the Incubating Feminist AI project proposed multidisciplinary, decolonial, intersectional feminist perspectives to provide critical views and proactive ideas on AI technologies for/from three regions of the majority of the world, including ours. This final report offers a comprehensive summary of the activities conducted under the Incubating Feminist AI project in Latin America and the Caribbean (2021-2024). It chronicles the wide range of efforts and collaborations throughout the project, serving as a record of all actions taken and the incubated projects in the region.