  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.
