Establishing trust in automated reasoning

TL;DR

This paper explores how to establish trust in automated reasoning systems by examining their reviewability and proposing measures to enhance their trustworthiness through technical and social means.

Contribution

It identifies key characteristics affecting reviewability and discusses strategies to improve trust in automated reasoning systems, integrating technical and social approaches.

Findings

Reviewability is crucial for trust in automated reasoning.

Characteristics influencing reviewability are identified.

Proposed measures aim to increase system trustworthiness.

Abstract

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by philosophers of science but has so far received little attention by practitioners. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures.