reflexivity
What It Means
Reflexivity means stepping back to examine your own assumptions, biases, and motivations that influence how you approach research or analysis. It's the practice of questioning why you're studying something a certain way, what preconceptions you're bringing to the work, and how your background shapes the conclusions you draw.
Why Chief AI Officers Care
AI systems inherit the biases and assumptions of their creators, making reflexivity critical for identifying blind spots in AI development and deployment. Without reflexive practices, teams may unknowingly build discriminatory algorithms, miss important use cases, or create solutions that don't work for diverse user populations. This directly impacts regulatory compliance, brand reputation, and product effectiveness.
Real-World Example
A hiring AI team realizes their algorithm favors candidates from certain universities because the development team all attended elite schools and unconsciously weighted those credentials more heavily in training data. Through reflexive analysis, they identify this bias, examine why it happened, and redesign their approach to focus on actual job performance predictors rather than prestige markers.
Common Confusion
People often confuse reflexivity with simple bias checking or diversity training. Reflexivity goes deeper than identifying obvious prejudices - it requires examining the fundamental assumptions about what problems are worth solving and how solutions should be designed.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, reflexivity requires clinicians and researchers to critically examine how their cultural background, trai...
Finance: In finance, reflexivity refers to how market participants' perceptions and behaviors influence asset prices, which in tu...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"A form of critical thinking that prompts us to consider the ‘whys’ and ‘hows’ of research, critically questioning the utility, ethics, and value of what, whom, and how we study"Source: Jamieson_Govaart_Pownall
"in qualitative research, the self-referential quality of a study in which the researcher reflects on the assumptions behind the study and especially the influence of his or her own motives, history, and biases on its conduct."Source: APA_reflexivity
Related Terms
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