Conflict and Cooperation: AI Research and Development in terms of the Economy of Conventions

TL;DR

This paper explores how the development of AI systems reflects societal moral conventions using the Economy of Conventions theory, employing active learning and classification to analyze AI research data.

Contribution

It introduces an iterative active learning tool to label AI research data according to moral conventions and demonstrates its feasibility with preliminary classification results.

Findings

Preliminary classifier trained on AI research conventions.

Comparison with classifiers trained on software conventions.

Feasibility of using active learning for convention labeling.

Abstract

Artificial Intelligence (AI) and its relation with societies is increasingly becoming an interesting object of study from the perspective of sociology and other disciplines. Theories such as the Economy of Conventions (EC) are usually applied in the context of interpersonal relations but there is still a clear lack of studies around how this and other theories can shed light on interactions between human an autonomous systems. This work is focused into studying a preliminary step that is a key enabler for the subsequent interaction between machines and humans: how the processes of researching, designing and developing AI related systems reflect different moral registers, represented by conventions within the EC. Having a better understanding of those conventions guiding the advances in AI is considered as the first and required advance to understand the conventions afterwards reflected by those autonomous systems in the interactions with societies. For this purpose, we develop an iterative tool based on active learning to label a data set from the field of AI and Machine Learning (ML) research and present preliminary results of a supervised classifier trained on these conventions. To further demonstrate the feasibility of the approach, the results are contrasted with a classifier trained on software conventions.