Gabriela Arriagada
Especialidad:
Ética de inteligencia artificial, ética de datos, justicia, explicabilidad, sesgos, transparencia.
Email:
gcarriagada@uc.cl
Gabriela Arriagada está cursando un PhD en Philosophy con especialización en éticas aplicadas en la University of Leeds, con fecha estimada de obtención en 2025. Ha sido Co-investigadora en varios proyectos interdisciplinarios. Entre ellos se encuentran "Diagnósticos con perspectiva de género" (InES género 210011, 2023), y “Se ha perdido un cholito”, un análisis desde la ciencia de datos y las humanidades digitales sobre avisos de fuga históricos (Proyecto II202420, VRI UC 2024). También ha participado como Co-investigadora en proyectos FONDEDOC 2024 sobre el diagnóstico del uso de IA generativa en evaluaciones sumativas en Educación Superior y el desarrollo de una experiencia de aprendizaje activo basado en Flipped Classroom con uso de IA. Fue Investigadora asociada en el Fondo Chile Compromiso de Todos 2021 "Querámonos mejor", que promovió la igualdad de género y la prevención de la violencia contra las mujeres en adolescentes vulnerables a través de Instagram.
PUBLICACIONES
We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrences linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote the adoption of an uncritical approach to understanding the influence of biases in developers’ decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list” project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development.
The affective scaffolding framework underlies differing perspectives on how griefbots affect the grieving process. While some researchers are optimistic, others draw more cautious conclusions. We endorse the view that griefbots are affective scaffolds. However, we draw a pessimistic conclusion about human-griefbot interaction. There are two main views on successful grief: the “Freudian view” and the continuing bonds view. The “Freudian view” paves the way to the pessimistic conclusion. In this paper, we propose a free-energy approach of the “Freudian view”. Predictive processing is often regarded as providing a process theory for the free-energy principle. In this framework, model evidence must be maximised by updating and optimising models. Griefbots are not recommendable since they are scaffolds that obstruct and lengthen the maximisation of model evidence, making models more rigid and poorly adaptable to changing dynamics. Building on this approach, we examine the ethical dimensions of griefbot’s use and design. We argue that there is something inherently morally wrong in the very conception of griefbots. Their functionality predisposes them to cause harm by disrupting the grief process.
Edificio de Innovación UC, Piso 2
Vicuña Mackenna 4860
Macul, Chile
Vicuña Mackenna 4860
Macul, Chile