Generative AI tools reflect the biases present in their training data, which may originate from various sources including: data inputters, anyone providing data or content (personal bias), the origin of the data (machine bias), and the exclusion of underrepresented or marginalized communities (selection bias). Moreover, users may inadvertently reinforce their existing beliefs by rephrasing prompts until they receive the answer they most desire (confirmation bias). Generative AI tools amplify and reinforce these biases, and it is crucial to remain critical of outputs/responses.

Otherwise, a growing sense of comfort, acceptance, and trust in what these tools provide can transform into depending on the machine to provide answers, realizations, and/or decisions (automation bias). Refer to academic resources to verify information and analyze the company’s data collection policies or procedures.

Digital Humanities Librarian Mary Ton provides an example of ChatGPT 3.5 bias in her Make my emails sound more masculine experiment conducted on June 1, 2023.

• National Education Association white paper (February 2024), "Does AI Have a Bias Problem?"
• Washington Post article (November 2023), "AI image generators like Stable Diffusion and DALL-E amplify bias in gender, race and beyond"
• Scientific American article (October 2023), “Humans Absorb Bias from AI—And Keep It after They Stop Using the Algorithm”
• New York Times article (July 2023), “Black Artists Say A.I. Shows Bias, With Algorithms Erasing Their History”
• Bloomberg article (June 2023), “Humans Are Biased. Generative AI Is Worse.”
• Washington Post article (April 2023), “Inside the secret list of websites that make AI like ChatGPT sound smart”
• Joy Buolamwini’s Ted Talk (November 2016)  “How I’m Fighting Bias in Algorithms”
• Joy Buolamwini’s The Gender Shade project