Value-based Fast and Slow AI Nudging

TL;DR

This paper introduces a value-based AI-human framework that employs different nudging modalities to influence human decision-making by targeting fast, slow, or meta-cognitive thinking, based on scenario-specific values.

Contribution

It proposes a novel framework that dynamically selects nudging strategies based on multiple, context-dependent values to enhance decision quality and human-AI collaboration.

Findings

Framework supports multiple simultaneous values with adjustable priorities

Different nudging modalities effectively target fast, slow, or meta-cognitive thinking

Potential to improve decision outcomes and human learning

Abstract

Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AI-human collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment.