Causal Discovery for Observational Image Datasets: A Vision Paper

Open Access
Conference Proceedings
Authors: Atul RawalAdrienne RaglinZiying TangQianlong Wang

Abstract: Artificial intelligence (AI) and machine learning (ML) systems have seen tremendous growth within the last few decades. Even with unprecedented new levels of autonomy for artificial reasoning systems, there are still challenges that remain. Challenges related to causal reasoning act as a roadblock for AI/ML systems to achieve human-like intelligence. For these systems to achieve human-like intelligence they must be able to gather causal information from given information. While causality for machine learning has made progress within the past years, there is still a lack of ability for AI/ML systems to generate causal relations from image datasets. To this end, this paper proposes a novel new perspective on discovering causal relations with image data by utilizing existing tools and methodologies.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), Causal Learning, Image Data-sets, Big-Data

DOI: 10.54941/ahfe1004476

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