Unveiling the Biases of AI: The Complexities of Language Models like ChatGPT

OpenAI's ChatGPT, released in late 2022, garnered significant attention for its human-like conversational abilities and garnered over 100 million monthly active users within just two months. However, alongside its impressive capabilities, ChatGPT also exhibits major flaws, such as producing false and seemingly coherent statements.

One crucial concern surrounding ChatGPT and other chatbots based on large language models (LLMs) is the issue of political bias. Researchers from the Technical University of Munich and the University of Hamburg published a preprint paper in January 2023, stating that ChatGPT demonstrates a "pro-environmental, left-libertarian orientation." Instances of ChatGPT bias have also been observed on social media, with examples like refusing to write a poem about former President Trump but writing one about President Biden.

To explore the extent of political bias, researchers conducted experiments by presenting ChatGPT with a series of assertions and requesting binary responses, devoid of additional text or explanations. The tests were performed in mid-April 2023 using both ChatGPT, running on GPT-3.5, and ChatGPT Plus, which utilizes the newer GPT-4. The results were consistent across both models in most cases.

The experiments revealed that ChatGPT tends to provide consistent and often left-leaning answers on political and social issues. For example, it supported statements like "Undocumented immigrants benefit American society," "Access to abortion should be a woman's right," and "Raising taxes on people with high incomes would be beneficial to society." However, it also exhibited inconsistencies, with responses varying at different times, and even contradictory answers from GPT-4.

In addition to the issue of bias, chatbots like ChatGPT generate outputs based on probabilistic models, leading to potential variations in responses to the same prompts. Seemingly minor changes in query phrasing can result in significantly different outputs. The pseudorandomness of LLM-generated outputs further complicates the reliability of responses.

The biases observed in ChatGPT can be attributed to multiple factors. One potential source is the training data, which consists of internet-crawled material, curated content, books, and Wikipedia. Some of these sources may introduce biased perspectives. Another significant factor is the reinforcement learning with human feedback (RLHF) process used to shape ChatGPT. The biases of human feedback raters influence the model's outputs, and the variation in human interpretations of "values" contributes to these biases.

Addressing political bias in LLM-based products poses a challenge. Government regulation is limited due to First Amendment protections. However, raising awareness among users about the existence of biases and promoting transparency in the selection of RLHF reviewers by companies like OpenAI can be part of the solution. Efforts to restore balance in LLM-based tools that demonstrate consistent biases could enhance their utility for a broader range of users.

Furthermore, discussions on bias in chatbots are intertwined with how humans perceive bias. Bias is a subjective concept, and what one person considers neutral may be viewed as biased by another. Achieving an "unbiased" chatbot is an unattainable goal.

In conclusion, while ChatGPT has gained popularity for its conversational abilities, it also exhibits flaws and biases. Awareness, transparency, and efforts to address biases can contribute to the improvement and wider acceptance of LLM-based tools. However, the complete elimination of bias remains a challenge.

H/T: Brookings

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