Modular interface for managing cognitive bias in experts

TL;DR

This paper introduces a modular interface designed to help experts in geoscience reduce cognitive biases during data interpretation, combining existing methods into a flexible system adaptable to various applications.

Contribution

It proposes a novel modular design framework that integrates expert elicitation techniques to mitigate cognitive biases in geoscientific interpretations.

Findings

A set of design principles for bias reduction modules.

A prototype modular bias-reduction system with action modules.

Potential for broad application in geoscience organizations.

Abstract

Expert knowledge is required to interpret data across a range of fields. Experts bridge gaps that often exists in our knowledge about relationships between data and the parameters of interest. This is especially true in geoscientific applications, where knowledge of the Earth is derived from interpretations of observable features and relies on predominantly unproven but widely accepted theories. Thus, experts facilitate solutions to otherwise unsolvable problems. However, experts are inherently subjective, and susceptible to cognitive biases and adverse external effects. This work examines this problem within geoscience. Three compelling examples are provided of the prevalence of cognitive biases from previous work. The problem is then formally defined, and a set of design principles which ensure that any solution is sufficiently flexible to be readily applied to the range of geoscientific problems. No solutions exist that reliably capture and reduce cognitive bias in experts. However, formal expert elicitation methods can be used to assess expert variation, and a variety of approaches exist that may help to illuminate uncertainties, avoid misunderstandings, and reduce herding behaviours or single-expert over-dominance. This work combines existing and future approaches to reduce expert suboptimality through a flexible modular design where each module provides a specific function. The design centres around action modules that force a stop-and-perform step into interpretation tasks. A starter-pack of modules is provided as an example of the conceptual design. This simple bias-reduction system may readily be applied in organisations and during everyday interpretations through to tasks for major commercial ventures.