Résultats de recherche pour « Interpretability »

In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understanding their decisions is essential, not only for ethical and regulatory compliance, but also for fostering trust in AI-driven outcomes. This paper introduces the novel concept of a Computer Vision Interpretability Index (CVII). The CVII framework is designed to emulate human cognitive processes, specifically in tasks related to vision. It addresses the intricate challenge of quantifying interpretability, a task that is inherently subjective and varies across domains. The CVII is rigorously evaluated using a range of computer vision models applied to the COCO (Common Objects in Context) dataset, a widely recognized benchmark in the field. The findings established a robust correlation between image interpretability, model selection, and CVII scores. This research makes a substantial contribution to enhancing interpretability for human comprehension, as well as within intelligent sensor applications. By promoting transparency and reliability in AI-driven decision-making, the CVII framework empowers its stakeholders to effectively harness the full potential of AI technologies.

Advancement in any field requires approaches for measurement. Failure to build such approaches inhibits improvementswithin the field. In the context of interpretability in Artificial Intelligence (AI), a lack of widely adopted evaluationand measurement approaches prevents its advance. While some approaches in literature propose ways to measure interpretability,no consensus exists on objective measurement of interpretability. To advance the state-of-the-art, a clear understandingof these approaches is essential. This paper conducts a systematic review of existing approaches that propose to measure or quantify interpretability and its aspects. The resulting analysis of this review identifies important aspects to consider when measuring interpretability. We found that no approaches directly propose to measure interpretability but instead quantify aspects associated with interpretability. We identify four of these aspects in result of this review.