LATIDIA · Investigación

Sigue al rebaño: un enfoque basado en datos para un recurso procesable

arXiv:2610.36580v1 Anuncio Tipo: nuevo Resumen: A medida que los sistemas de aprendizaje automático toman decisiones cada vez más importantes sobre la vida de las personas, es fundamental brindar a los usuarios recursos prácticos sobre cómo pueden lograrlo.

La noticia

arXiv:2610.36580v1 Announce Type: new Abstract: As machine learning systems make increasingly greater decisions about people's lives, it is critical to provide users with actionable recourse about how they can achieve their desired outcomes. This problem is currently tackled by relying on expensive human supervision which involves hand-designing a cost function before searching for low-cost actions that achieve the desired outcome. In this paper, we propose \textit{``Data-Driven Action Recourse"} (DDAR), the first approach for providing action recourse without requiring costly supervision from humans about action costs. We build upon the notion that highly likely trajectories in the historical user data should be associated with lower costs. With this, DDAR first builds