“Language Models are Unsupervised Multitask Learners”

This research paper by Radford et al. (2019) introduces GPT–2, a large language model developed by OpenAI. It discusses the model‘s impressive capabilities and highlights its potential for generating human-like text. However, it also acknowledges the presence of biases in the training data and the potential for the model to amplify or propagate those biases. The paper emphasizes the need for ongoing research to address bias mitigation in language models. 

Website: https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

“Mitigating Unwanted Biases in Text Classification”

Bolukbasi et al. (2016) present a research paper that explores the issue of unwanted biases in text classification models. The authors discuss how these biases can arise from the training data and affect downstream applications. They propose a method to measure and reduce biases in text classifiers, highlighting the importance of fairness and inclusivity in machine learning systems.

Link:  Debiasing Embeddings for Reduced Gender Bias in Text Classification – ACL Anthology

“Examining Gender Bias in Languages with Grammatical Gender“ 

This paper by Zhao et al. (2018) focuses on gender bias in language models that are trained on languages with grammatical gender, such as Spanish or German. It investigates how these biases can manifest in the generated text and proposes methods for reducing gender bias in language models. The study highlights the importance of addressing biases in language models to ensure fair and inclusive AI systems.

Website:  https://aclanthology.org/D19-1531.pdf

“Attenuating Bias in Word Embeddings”

This research paper by Bolukbasi et al. (2016) explores the issue of bias in word embeddings, which are widely used in natural language processing tasks. The authors present a method to attenuate bias in word embeddings by debiasing them using an adversarial training framework. The paper discusses the implications of biased word embeddings and provides insights into potential solutions to mitigate bias in language models.

Website: https://arxiv.org/abs/1607.06520

The Risk of Racial Bias in Hate Speech Detection“

 This paper by Sap et al. (2019) investigates the presence of racial bias in hate speech detection models. It explores how biases in training data can lead to the misclassification of hate speech, particularly when it involves racial or ethnic slurs. The authors discuss the challenges of bias mitigation in hate speech detection and propose methods to improve fairness and reduce racial bias in these models.

Website: https://arxiv.org/abs/1903.04561

AI-Detectors Biased Against Non-Native English Writers

Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse. The

[2304.02819] GPT detectors are biased against non-native English writers (arxiv.org)