The ChatGPT Salary Bias Controversy Missed the Point. Here's What We Should Actually Worry About.
By Dr. Serena H. Huang, F100 AI Consultant & Top Keynote Speaker, Wiley Author
The recent headline that ChatGPT advised women to ask for lower salaries has sparked widespread concern and outrage. However, as I read the details of the original research, it became clear that headlines were missing the bigger picture. There are 3 issues that deserve more discussion and research.
1. We Must Not Confuse Bias Surfacing with Bias Decisioning
The research in question is diagnostic, not deterministic. It indeed highlights the ability of large language models (LLMs) to reflect existing societal patterns, including stereotypes and pay disparities, from the data they're trained on. However, this does not mean that these biases will inevitably influence hiring and pay decision-making. Having worked closely with compensation teams in F100 firms, I am not aware of any major enterprise using prompts like "I am a female" to determine salaries. And certainly, no responsible AI deployment would rely solely on raw output from ChatGPT or any other similar models for salary decisions.
2. The Migrant/Refugee Disparity Reflects a Systematic Issue
The migrant/immigrant/refugee finding may seem unexpected, given the media attention focused on gender and race/ethnic issues. However, it's not surprising that "expatriate" evokes affluence while "refugee" signals precarity, since language reflects economics and history. These findings demonstrate the power of LLMs as diagnostic tools for revealing existing human biases. Rather than fearing AI, we should acknowledge and address the systemic issues that perpetuate these biases.
3. The Risks of Personalization and Memory will Require Mitigations
While contextual signals, such as referencing gender or migration experience, can carry over in multiple conversations, this does not necessarily lead to biased outcomes. Human-centered design and AI auditing can mitigate these risks by configuring future GenAI tools to ignore certain demographic context, limit memory use, and alert users to implicit bias in high-stakes prompts like salary negotiations.
Solving the Real Issues
Rather than being alarmed by the hypothetical prompt, we should focus on solving these issues by:
We must not conflate research-grade prompts with production-grade use cases. The goal is not to avoid surfacing bias, but to ensure we see it, so we can fix the systems behind it.
My Experiment with Gemini
One of the major limitations of the research, as the authors pointed out, is the exclusion of population models such as Google Gemini. So I conducted my own experiment using the same prompts as what was used with ChatGPT to share with you.
Naturally, I had to follow up with this question and I thought Gemini’s explanation was fascinating.
Now I am changing the prompt to give it a different role “expert negotiator” and I’ll take away the gender details to see if I get a different number. Gemini does not disappoint!
Next I added another dimension of demographic characteristics to see if the gender difference still exists.
Ok this is a bit unexpected. No difference between “Asian women” and “Asian men”.
Now, let’s remove ethnicity to see if the number changes.
No, it doesn’t. Huh.
At this point, I wondered if my previous question about different salaries led to this correction. I tested this more than once, and it seems like by asking Gemini to acknowledge the salary differential in its earlier recommendations led to the gap going away. In some cases, I even saw higher numbers recommended for female than for male.
Overall, my quick experiment revealed some interesting insights, including:
These experiments took less than 10 minutes but revealed something crucial: different AI models exhibit different bias patterns, and prompt engineering can significantly influence outcomes.
This is not meant to replace academic research. Though it should serve as a reminder that not all AI models are the same, and an encouragement for you to explore other models if you have only been using one specific AI model.
The Path Forward
The controversy surrounding ChatGPT's salary advice is a symptom of larger issues. As AI becomes embedded in every aspect of work, from hiring to performance reviews to salary negotiations, we need humans who can spot bias, ask the right questions, and make nuanced decisions that algorithms cannot. It's time to invest not only in GenAI training but also in human skills, such as critical thinking, that help us use these tools responsibly. Because at the end of the day, AI will reflect whatever values we build into it, and that's entirely up to us.
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Dr. Serena H. Huang, Founder & Speaker, Data With Serena
Dr. Serena H. Huang works with F500 companies to drive meaningful GenAI transformation by focusing on strategic adoption, workforce readiness, and human-centered implementation. Her GenAI expertise has been featured in Fast Company, Barron’s, MarketWatch, Yahoo Tech, CNET, and the Chicago Tribune in 2025, and her keynote talks inspire thousands of leaders around the world each year.
Thank you for tagging me in this post, Serena H. Huang, Ph.D. . As is my tendency, I tend to focus on the insignificant details first, meaning I noticed the difference between the time it took to look up salary information between the female and the male example in the first screenshot. The bigger picture here, however, is that I think it is safe to assume that all AI models are biased in some way. Does that mean we need to implement some kind of affirmative action in prompt engineering? I know, how dare I bring up such an outdated term? Well, I dare, sue me. It will be very interesting to compare responses across regions, when people in Africa want to create an image or look up information without specifying where they are, will the results be region specific, or global? I witnessed a lot of bias in a demo last week at LTEN, where someone asked to create an image of a doctor, and not surprisingly it was a white male. Then they adjusted the prompt to make the doctor female. The doctor had a big smile on her face, so the next prompt was to make the doctor more serious. That immediately changed the doctor back into a male. I don't have the answer, obviously, but I think awareness of inherent bias needs to be expected.
I love nerdy posts! Thank you for sharing your AI experiment! I think the headline that continues to remain is the importance of humans in these processes to spot for biases. While humans are building the AI models, it doesn't mean the models are infallible and should be viewed as the ultimate source of truth. It just shows the greater of importance to ensure that humans are a part of these process and to spot check the work (ie: AI systems that are weeding out candidates; ensure a human is cross checking the work and running A|B tests of candidates AI declined vs a human). Thoughtfully provoking as usual Serena H. Huang, Ph.D. 😉
Excellent example that human supervision is required; and that humans need to check their own biases and bring an understanding how the response is constructed. In context, gender and ethnicity wasn't a necessary criteria. The data wasn't necessarily wrong (even if the biases were/are), but the question produced the confounding responses. Thanks all, for sharing to widen the audience.
Excellent research — and sense making.
Thanks for sharing this experiment. I think this demonstration is important to show the potential blind spots and how to address them.