Data Género’s ‘FEMINISMS IN ARTIFICIAL INTELLIGENCE: AUTOMATION TOOLS TOWARDS A FEMINIST JUDICIARY REFORM IN ARGENTINA AND MEXICO’ has been selected to go to the 6 month prototype phase.
‘FEMINISMS IN ARTIFICIAL INTELLIGENCE: AUTOMATION TOOLS TOWARDS A FEMINIST JUDICIARY REFORM IN ARGENTINA AND MEXICO’ deals with the lack of transparency in the judicial treatment of gender-based violence (GBV) against women and LGBTIQ+ people in Latin America results in low report levels, mistrust in the justice system, and thus, reduced access to justice. To address this pressing issue before GBV cases become feminicides, we propose to open the data from legal rulings as a step towards a feminist judiciary reform. We identify the potential of artificial intelligence (AI) models to generate and maintain anonymised datasets for understanding GBV, supporting policy making, and further fueling feminist collectives’ campaigns. In this paper, we describe our plan to create AymurAI, a semi-automated prototype that will collaborate with criminal court officials in Argentina and Mexico. From an intersectional feminist, anti-solutionist stance, this project seeks to set a precedent for the feminist design, implementation, and deployment of AI technologies from the Global South.
Over the 6 month prototyping phase they will build combine the theoretical influences of this project with its practical requirements, through a feminist data science approach, strongly guided by Data Feminism (D’Ignazio and Klein, 2020), Human-Centered Data Science (Aragon et al., 2022) and state-of-the-art guidelines on Human-Centred Machine Learning(Chancellor, 2018). We will address our research questions with an exploratory, iterative spirit centred on users (leveraging on and prioritising the knowledge of the criminal court staff and the local feminist organisations that work with justice data), with a solid presence of ethical questionings along the whole design and development process. For example, before deciding to add a new feature, the team will discuss possible misuses of such a feature, pondering benefits over risks, assessing whether risks could be mitigated, or whether the feature should be discarded. Design decisions will be documented and published via Github, rendering AymurAI’s
features traceable over time, and making the process auditable. The team that will prototype the tool is composed of this paper’s authors plus more members of DataGénero and experts in software development and machine learning/NLP, who are members of technology co-ops following the principles of open knowledge and free software.
During the first month we will start the development of the regex model and its metrics and of the machine learning model and its metrics. Given that we have 50 different categories, maybe we will need metrics for many of them, since they have different kinds of data inside of them. After comparing and evaluating the models, looking for the one with the best performance, we will start building the prototype. The evaluation of the models, after defining our metrics will be addressed during the third month of our project in collaboration with the development team. During the whole process, we will be looking for biases and inaccuracies. Finally, we will test the tool’s usability to
implement further refinements. This prototype can be tested in Criminal Court 10 that will work as a pilot in our project. As shown in the figure, to be feasible in six months, the project will prioritise the automatic creation of a human-approved, open database with anonymised information about legal rulings (as a first step, coming only from Criminal Court 10 in CABA), that can be later accessed by anyone. The long-term maintenance of this database, as well as the creation of interactive visualisations, infographics, or statistics on the most frequent queries, are out of the scope of this prototype and would be a certainly important part of future work in the context of linkage with the broader community.
They will reach out to partners in the judicial community to define and refine their tool.
To read the full paper: https://drive.google.com/file/d/1VKeeSdJz6-8DoEbetQ8f1wTH1ORqvIXV/view?usp=sharing
To watch short explainers: https://drive.google.com/file/d/1xxz5g562J_ZM3zecY0VmXfHfO53Tggaj/view?usp=sharing