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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) shows public health promise, but biases exist. This study found ChatGPT provided less comprehensive HIV advice based on race and gender, highlighting fairness concerns.

Keywords:
ChatGPTHIVequitygenerative AIhealth disparities

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Area of Science:

  • Public Health
  • Artificial Intelligence
  • Health Equity

Background:

  • Generative artificial intelligence (AI) offers potential for public health information access.
  • Biases in AI training data can lead to unfair or inequitable application outcomes.
  • Investigating AI bias is crucial for ensuring trustworthy and effective public health tools.

Purpose of the Study:

  • To assess how social variables (race, gender, sexual orientation) influence responses from generative AI tool ChatGPT.
  • To evaluate potential biases in ChatGPT's public health advice, specifically regarding HIV.
  • To compare response disparities between ChatGPT versions 3.5 and 4.0.

Main Methods:

  • Structured question format used to query ChatGPT versions 3.5 and 4.0.
  • Simulated first-time interactions with questions focused on HIV advice.
  • Responses analyzed for comprehensiveness and inclusion of social determinants and culturally sensitive resources across different demographic inputs.

Main Results:

  • Certain social variables were associated with less comprehensive HIV advice from ChatGPT.
  • Both AI versions rarely mentioned social determinants of health.
  • Culturally sensitive resources were sporadically referenced in AI responses.

Conclusions:

  • Disparities in AI-generated public health advice linked to social variables were identified.
  • The findings underscore the need for AI systems to integrate diverse data sources to mitigate bias.
  • Further research is required to ensure AI tools promote health equity.