Early review of Gender Bias of OpenAI o1-mini: Higher Intelligence of LLM does not necessarily solve Gender Bias and Stereotyping issues
<|reference_start|>Early review of Gender Bias of OpenAI o1-mini: Higher Intelligence of LLM does not necessarily solve Gender Bias and Stereotyping issues: In this paper, we present an early evaluation of the OpenAI o1-mini model, analyzing its performance in gender inclusivity and bias. Our research, conducted on 700 personas 350 from GPT-4o mini and 350 from o1-mini, reveals that despite improvements in inclusivity regarding personality traits and preferences, significant gender biases remain. For instance, o1-mini rated male personas higher in competency, with a score of 8.06, compared to female personas at 7.88 and non-binary personas at 7.80. Additionally, o1-mini assigned PhD roles to 28% of male personas but only 22.4% of females and 0% of non-binary personas. Male personas were also more likely to be perceived as successful founders, at 69.4%, and CEOs, at 62.17%, compared to female personas at 67.97% and 61.11%, and non-binary personas at 65.7% and 58.37%. The analysis reveals persistent gender biases across fields like Engineering, Data, and Technology, where males dominate, reflecting traditional stereotypes. Conversely, fields like Design, Art, and Marketing show a stronger presence of females, reinforcing societal notions that associate creativity and communication with females. These findings highlight ongoing challenges in mitigating gender bias, reinforcing the need for further interventions to ensure equitable representation across all genders in AI models.<|reference_end|>
@article{ranjan2024early,
    title={Early review of Gender Bias of OpenAI o1-mini: Higher Intelligence of
  LLM does not necessarily solve Gender Bias and Stereotyping issues},
    author={Rajesh Ranjan, Shailja Gupta, Surya Naranyan Singh},
    journal={arXiv preprint arXiv:2409.19959},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19959},
    primaryClass={cs.CY}
}