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ART: Resist researcher biases

An introduction to research for University of Redlands students

Learning objective

  • Recognize the impact of confirmation, algorithmic and implicit bias on your research.

Understanding bias

The opposite of being biased is being open to hearing and engaging with the experiences of others, according to lawyer and community organizer Kori Carew. In this 2018 TedXYouth talk, she outlines five principles that we can use to resist bias in everyday life, adding that this takes courage.

What would it look like to engage in research with this mindset?

Now let's look more closely at three kinds of biases that can impact your research.

Confirmation bias

Confirmation bias (also called self-serving bias) describes our tendency to seek out and remember information that supports our existing beliefs. This ultimately closes us off to people who see the world differently. We lose opportunities to understand other points of view through dialogue and to sharpen our own thinking to become more influential.

This video from the University of Texas McCombs School of Business explains how self-serving bias works.

Algorithmic bias

Algorithmic bias explains how social prejudices are woven into the design to the technologies that we use to find information. We can resist algorithmic bias through educating ourselves on how systems like search engines and voice assistants work and critically reflecting on our interaction with these technologies.

Dr. Safiya Noble pioneered research on algorithmic bias in the 2010s and provides a short introduction in this video.

Implicit bias

The National Equity Project defines implicit bias as "the process of associating stereotypes or attitudes toward categories of people without conscious awareness." This explains how researchers can inadvertently perpetuate oppressive views despite their explicit intentions to the contrary. We can resist implicit bias by working to understand and counter our own biases, and also working to uproot oppression within our institutions.

This video from the University of Texas McCombs School of Business and an article from the National Equity Project offer two perspectives on implicit bias.