Facets of algorithmic literacy: Information, experience, and individual factors predict attitudes toward algorithmic systems

Abstract

Algorithmic decision-making systems are ubiquitous in digital media, but the public has been largely unable to negotiate the role of algorithms in society. Building from the concept of attitude-behavior consistency for political behavior, we develop a framework for fostering algorithmic literacy to develop well-informed attitudes toward algorithms. As algorithms are increasingly relevant to broad societal effects, an integrative approach is needed for a full account of how the public makes sense of algorithms and their role in society. We designed and tested a novel intervention that combines algorithmic literacy with personalized user experiences to see how each component influenced attitudes toward algorithms. We found these methods jointly informed attitudes, but the intervention’s efficacy was dependent on participants’ individual differences in technology use.

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