Debiasing – Integration and Implementation Insights

A framework for navigating the impact of using artificial intelligence on collaborative research communication

March 24, 2026March 3, 2026

By Faye Miller.

faye-miller_2025 — Faye Miller (biography)

How can research teams recognise when their use of artificial intelligence is affecting their ability to integrate different knowledge and perspectives? How can they navigate the impact of artificial intelligence on their collaborative processes?

When research teams use artificial intelligence in collaborative work, new complexities emerge, especially subtle shifts in communication patterns that can fundamentally alter how teams integrate different perspectives and knowledge forms. Consider an environmental team relying on artificial intelligence summaries across hydrology, ecology, and policy. They might miss crucial disciplinary nuances, or follow its “evidence-based” recommendations that may clash with community priorities.

You are biased!

September 11, 2024September 18, 2018

By Matthew Welsh

Complex, real-world problems require cooperation or agreement amongst people of diverse backgrounds and, often, opinions. Our ability to trust in the goodwill of other stakeholders, however, is being eroded by constant accusations of ‘bias’. These are made by commentators about scientists, politicians about media outlets and people of differing political viewpoints about one another. Against this cacophony of accusation, it is worthwhile stepping back and asking “what do we mean when we say ‘bias’ and what does it say about us and about others?”.

Harnessing analogies for creativity and problem solving

November 2, 2023September 13, 2016

By Christian Schunn

What is an analogy? How can analogies be used to work productively across disciplines in teams?

We know from the pioneering work of Kevin Dunbar (1995), in studying molecular biology labs, that analogies were a key factor in why multidisciplinary labs were much more successful than labs composed of many researchers from the same backgrounds. What is it about analogies that assists multi- and interdisciplinary work?

The advice that follows comes from a decade of research involving intensive analyses of hundreds of hours of interdisciplinary science and engineering teams, following the minute-by-minute processes of the teams, and using advanced statistical techniques to look for robust patterns in behavior over time and across teams. In general, our research has shown that creative teams generate twice as many ideas per unit time when they use analogies than when they do not.

So, what is an analogy?

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