Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence

TL;DR

This paper proposes Intelligent Decision Assistance (IDA), a new AI approach that supports knowledge workers with explainable insights without automating decisions, aiming to reduce automation drawbacks like deskilling.

Contribution

It introduces the concept of IDA, combining DSS and XAI techniques to assist workers without automating decisions, addressing automation bias and deskilling issues.

Findings

Empirical evidence supports IDA's potential to support knowledge workers.

IDAs can enhance decision quality without causing deskilling.

Theoretical hypotheses on IDA's impacts are validated through literature review.

Abstract

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI. To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams -- DSS and automation. IDA supports knowledge workers without influencing them through automated decision-making. Specifically, we propose to use techniques of Explainable AI (XAI) while withholding concrete AI recommendations. To test this conceptualization, we develop hypotheses on the impacts of IDA and provide first evidence for their validity based on empirical studies in the literature.