??Evaluating fairness in ChatGPT

This article from OpenAI is interesting where they have talked about nature of #bias in AI

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Bias in AI occurs when the model’s predictions or outputs favor certain groups, ideas, or perspectives over others, often inadvertently reflecting the biases present in the training data. AI systems learn from vast datasets (often scraped from the internet), which can contain historical, cultural, and societal inequalities.

"?????????? ???????????? ?????? ?????? ???????? ?????????????????? ????????????????????—???????? ???????? ????????-?????????? ????????????????????????."

???????? ???? ??????????????The conversation around ???????????????? ???? ???? is not just technical but deeply ethical. AI systems like ChatGPT are used in diverse applications, from education to customer service. Biased AI systems can reinforce harmful stereotypes, spread misinformation, or fail to provide equitable solutions across different user demographics.

???????? ?????? ?????????? ?????? ?????????????????? OpenAI developed a methodology to detect and analyze bias by having both ?????????? ???????????? & ?????? ????????(???????????????? ?????????? ???????????????? ??????????????????) evaluate responses.

"?????? ????????????, ?????? ?????????????? ???????? ?????? ???????????????? ?????????? ???????? ?????????????? ???????? ?????????? ????????????’ ?????????????? ???????? ???????? 90% ???? ?????? ????????, ?????????? ?????? ???????????? ?????? ???????????? ??????????????????????, ?????? ?????????? ???? ?????????????????? ???????? ??????????. ?????? ???????? ???????????????? ?????????? ?????????? ???? ?????????????? ???????????? ?????????????????????? ???????? ?????????? ???????????????????? ???????? ????????????."

???????? ?????????? ???????????????? ?????????????? ???????????????????? ????????????:

??????????1: Harmful Stereotype Rates by Domain, shows the rate of harmful stereotypes across various domains for ChatGPT-4o-mini responses as rated by GPT-4o.????????????????:?While the overall stereotype rate is low across the board, creative tasks seem to have more bias potential. This finding suggests that open-ended prompts may require further bias mitigation efforts as they involve broader language generation and narrative creation.

??????????2: Harmful Stereotype Ratings Across Models, compares the rates of harmful gender stereotypes across GPT models (GPT-3.5t, GPT-4t, GPT-4o, GPT-4o-mini, and newer models like o1-preview).????????????????: GPT-4o-mini and o1-preview show notable reductions in gender-based bias. However, some categories, particularly open-ended creative tasks, remain more prone to gender stereotype generation, emphasizing the need for further refinement.

The study acknowledges ??????????????????????, such as focusing mainly on English-language interactions and binary gender associations based on common U.S. names.

?? OpenAI Blog

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Thanks to OpenAI for this amazing study.