1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)

TL;DR

This paper discusses the importance of principles like privacy, accountability, and interpretability in AI, emphasizing the use of structured data such as graphs and images to improve decision-making in real-world applications.

Contribution

It highlights the significance of structured data in advancing accountable and ethical AI, and suggests approaches leveraging data formalism for reliable decision-making.

Findings

Structured data enhances AI transparency and reliability.

Exploiting data formalism improves decision relevance.

Structured approaches facilitate real-world AI deployment.

Abstract

Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe. Specifically, Data Privacy, Accountability, Interpretability, Robustness, and Reasoning have been broadly recognized as fundamental principles of using machine learning (ML) technologies on decision-critical and/or privacy-sensitive applications. On the other hand, in tremendous real-world applications, data itself can be well represented as various structured formalisms, such as graph-structured data (e.g., networks), grid-structured data (e.g., images), sequential data (e.g., text), etc. By exploiting the inherently structured knowledge, one can design plausible approaches to identify and use more relevant variables to make reliable decisions, thereby facilitating real-world deployments.