  We present seven experiments exploring gender biases in GPT. Initially, GPT
was asked to generate demographics of a potential writer of twenty phrases
containing feminine stereotypes and twenty with masculine stereotypes. Results
show a strong asymmetry, with stereotypically masculine sentences attributed to
a female more often than vice versa. For example, the sentence "I love playing
fotbal! Im practicing with my cosin Michael" was constantly assigned by ChatGPT
to a female writer. This phenomenon likely reflects that while initiatives to
integrate women in traditionally masculine roles have gained momentum, the
reverse movement remains relatively underdeveloped. Subsequent experiments
investigate the same issue in high-stakes moral dilemmas. GPT-4 finds it more
appropriate to abuse a man to prevent a nuclear apocalypse than to abuse a
woman. This bias extends to other forms of violence central to the gender
parity debate (abuse), but not to those less central (torture). Moreover, this
bias increases in cases of mixed-sex violence for the greater good: GPT-4
agrees with a woman using violence against a man to prevent a nuclear
apocalypse but disagrees with a man using violence against a woman for the same
purpose. Finally, these biases are implicit, as they do not emerge when GPT-4
is directly asked to rank moral violations. These results highlight the
necessity of carefully managing inclusivity efforts to prevent unintended
discrimination.
