Descripción del proyecto
Hacia sistemas inteligentes de recomendación de grupos fiables y transparentes
Los ecosistemas de comercio electrónico y las plataformas de redes sociales emplean herramientas inteligentes de recomendación de contenidos y moderación de publicaciones. Los sistemas de recomendación de grupos (SRG) están ganando popularidad, pero la demanda de una mayor transparencia en los algoritmos y los procesos de toma de decisiones repercute negativamente en las oportunidades de empleo, el comercio electrónico y la exposición a las noticias. Garantizar un trato justo y comprensible es crucial para establecer la transparencia y generar confianza en sistemas basados en inteligencia artificial como SRG. Sin embargo, aunque los sistemas tradicionales de recomendación individual se han esforzado por mejorar estos aspectos, aún no se han explorado en los SRG. El equipo del proyecto FIDELITY, financiado por las acciones Marie Skłodowska-Curie, se dedica a desarrollar nuevos algoritmos y herramientas para mejorar la explicación, la equidad y la sinergia dentro de los SRG. El equipo del proyecto incorpora técnicas de explicación «post hoc» para acortar la brecha entre explicación y equidad en recomendación individual y SRG.
Objetivo
Today, most social media networks use automated tools to recommend content or products and to rank, curate and moderate posts. Recommender systems (RSs), and in particular Group recommender systems (GRSs), -a specific kind of RSs used to recommend items to a group of users-, are likely to become more ubiquitous, with expected market forecast to reach USD 16.13 billion by 2026.
These automated content governance tools are receiving emerging interest as both algorithms and decision-making processes behind the platforms are not sufficiently transparent, with a negative impact on domains such as fair job opportunities, fair e-commerce or news exposure.
Two of the key requirements that have to be fulfilled to build and keep users’ trust in AI systems while guaranteeing transparency are Fairness and Explainability. But, aside from some previous attempts to enhance both aspects in traditional-individual RS, they have hardly been explored in GRSs.
FIDELITY addresses this challenge by developing novel algorithms and computational tools in GRS to boost explanation, fairness, and synergy between them through a disruptive multidisciplinary research approach that: 1) extensively brings SHAP and LIME, as state-of-the-art post-hoc explanation approaches in AI, into RS and GRS contexts, 2) bridges explanation and fairness in RS and GRS, introducing an explanation paradigm shift moving from “why are the recommendations generated?” to “how fair are the generated recommendations?” and, 3) transversally evaluates the new methods through real-world GRSs and user studies. The ultimate goal is to guarantee greater user trust, and independence of RS output from any of the sociodemographic characteristics of users. The training programme, designed with the aim to fill the existing gaps between computing science, social research and business development reality, will provide the candidate with a multidisciplinary background that will boost his innovation potential and career prospects.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinador
23071 Jaen
España