Natural Language Processing has come a long way in recent years through advances in machine learning. This has enabled systems to analyze and generate human language at a massive scale through tools like translation services, virtual assistants, and more. At the same time, as the capabilities of these systems have increased, so too have concerns about unfair treatment and unintended harm. It is important that we constructively address issues of bias to ensure NLP progresses in an ethical, socially responsible manner.

Several studies have highlighted biases in current models related to aspects of human identity like gender, race, and accent or culture. For example, some language models display stereotypical biases when generating text corresponding to different genders or ethnicities. Automatic speech recognition systems also tend to perform better on male and standard accent voices compared to others. These pipeline issues can negatively impact groups and perpetuate realworld harms if left unaddressed.

By gathering datasets and creating benchmarks that are more diverse and representative, incorporating fairness and accountability techniques, and engaging in respectful, multistakeholder processes, we can develop technologies that serve all groups equitably. With openness, diligence and collaboration between researchers, companies and communities, together we can create more inclusive language tools. We want to HEAR YOUR VOICE!

Read more on current research here:

Bias In Large Language Models

Bias in Automatic Sound Recognition Models

Language Models are Unsupervised Multitask Learners

This research paper by Radford et al. (2019) introduces GPT2, 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


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.


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.


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.


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 (

Racial Disparities in Sound Recognition Models

A study conducted by researchers at Stanford University found an error rate of 35% percent for African American Male speakers v 17% for white counterparts. While even 17% is high, 35% is more than double.


Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 

This study from MIT explored bias in commercial image and audio analysis services. When examining voice recognition technology, they found substantially lower classification accuracy for females compared to males, especially for accent and nonnative speakers. The study sheds light on important issues of algorithmic fairness and recommends further progress towards more inclusive artificial intelligence.

Link:  buolamwini18a.pdf (

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Researchers at Anthropic examined the potential for bias in large language models and found that while very effective at tasks like summarization, they can also reflect, reinforce and even generate opinions that align with historical prejudices. When analyzing automatic sound recognition specifically, the researchers express concerns about perpetuating realworld inequities if underlying data or training procedures are not thoroughly examined and improved. Overall, a thoughtful read on ensuring AI systems are developed and used responsibly.

Link:  On the dangers of stochastic parrots | The Alan Turing Institute

Challenges in Collected Data Can Lead to Biased Predictions on Minority Groups

This paper from IBM Research discusses how imbalances or deficiencies in collected training data can negatively impact prediction performance for minority demographic groups. When data mainly represents a majority experience, models may struggle with varied pronunciations, accents or other characteristics of underrepresented populations. The researchers encourage the collection of more diverse, representative datasets to facilitate the development of more universally applicable machine learning models.

Link:  IBM researchers investigate ways to help reduce bias in healthcare AI | IBM Research Blog

Error Rates in Automatic Speech Recognition for English Language Learners

 This paper looked at how well existing automatic speech recognition (ASR) systems perform when used by English language learners from various first language backgrounds. They tested several popular ASR models and found higher error rates for nonnative speakers compared to native English speakers, especially for those just beginning to learn English. The researchers hope their findings can help improve ASR accessibility.

Link:  Exploiting automatic speech recognition errors to enhance partial and synchronized caption for facilitating second language listening – ScienceDirect

Accent and Speaker Variability Effects on ASR Performance 

This study examined how different accents and levels of English proficiency impact the accuracy of automatic speech recognition or ASR. They tested ASR systems on speech samples from over 100 speakers with diverse first language backgrounds. The results showed that error rates tended to be higher for stronger nonnative accents and less proficient English skills. The researchers suggest this area needs more work to make ASR work better for all.


CrossLingual Transfer Learning for LowResource Speech Recognition 

Here, the researchers experimented with usingtransfer learning techniques to improve ASR models for underrepresented languages by leveraging data from to models trained only on limited local data. Their method shows promise as a way to develop more inclusive automatic speech technologies.

Link:(6) 2022-01-13 – Automatic Speech Recognition for Low Resource Languages – Satwinder Singh – YouTube