System Analysis for Responsible Design of Modern AI/ML Systems

TL;DR

This paper advocates for applying traditional system analysis methodologies to AI/ML system design to promote responsible practices and provides a review of how these methods can support ethical and effective ML development.

Contribution

It introduces the integration of system analysis techniques into ML system design as a novel approach for fostering responsibility in AI/ML practices.

Findings

System analysis offers a formal framework for responsible ML design.

Connecting system components enhances ethical considerations in AI development.

Review of system analysis components supports responsible AI practices.

Abstract

The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and systems. Such perspective can provide a formal way for evaluating and enabling responsible ML practices. In this paper, we review components of the System Analysis methodology and highlight how they connect and enable responsible practices of ML design.