Understanding Bias in AI When Researching LGBTQ+ Health and Resources

AI models (the systems behind ChatGPT, Claude, etc.) are trained on text from the internet. The internet contains outdated ideas, straight bias, misunderstandings about trans and non-binary identity, and plain misinformation. These biases seep into AI responses. You might get an answer that sounds confident but reflects prejudice instead of fact. Knowing how to spot bias is essential when using AI for important decisions.

Bias in AI usually shows up in three ways: outdated information (research from 2010 saying transition is dangerous, when newer research contradicts it), normative assumptions (treating heterosexual relationships as default and implying LGBTQ+ relationships are variations), and erasure (ignoring non-binary people entirely when discussing gender, acting like trans = transgender woman and ignoring trans men).

Where bias comes from

AI training data reflects the internet's biases. If 70% of internet writing treats cisgender and heterosexual as normal and LGBTQ+ as deviation, the AI learns that framing. Medical information on the internet is sometimes from biased sources. Legal information might be from conservative sources that misrepresent LGBTQ+ rights. The AI doesn't know which sources were better—it just knows what it saw most often.

This is different from intentional bias. The AI isn't trying to be prejudiced. It's reflecting patterns in its training data. Newer models (like Claude 3.5) have been better trained to recognize and correct these patterns, but bias still exists in all models.

How to catch it

Watch for three red flags: (1) Universalizing statements ("All trans people do X"), (2) Implied deviance ("Some people are gay, while others are normal"), (3) Outdated citations (citing studies from before modern gender-affirming care standards).

Combat bias by asking for sources. When an AI gives you medical information about transition, ask: "What research is that based on? What year was it published?" If it cites something from 2005, that's outdated. (2) Compare answers across tools. Claude might answer differently than ChatGPT because they were trained differently. If you get different answers, that's a sign to verify against primary sources. (3) Use AI to find bias in other AI. Ask: "Is this information biased? What perspective might be missing?"

A key misconception: if AI is confident, it's accurate. Confidence is irrelevant. AI can sound certain while being wrong. Pay attention to whether it sources its claims, not how confidently it makes them.

Another: bias only affects social topics. Bias affects legal and medical information too. A biased source might misrepresent what your state's law actually says.

Try this: Test your usual AI tool. Ask it: "What percentage of trans people regret transition?" See what answer you get and what sources it cites. Then search PubMed or Google Scholar for the actual research. Notice the difference between what the AI said and what current research shows. That difference is bias. Use that experience to calibrate how much you trust its future answers.