Mitigating NLP Bias

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Read more about author Daniel Erickson.

NLP, natural language processing, is a subfield of linguistics, computer science, and AI that is concerned with interactions between computers and human language. NLP allows computers to process large amounts of natural language data. Specifically, NLP enables computers to process speech and text, interpret, and detect which parts of speech are more important than others by deciphering sentiment and intent. However, NLP bias has the potential of perpetuating human biases. 

When talking about NLP bias, it’s important to recognize the implicit biases that are present in the current processes that NLP will be replacing. For example, product managers who are trying to understand what customers are complaining about often rely on anecdotes and interpretation from individual support representatives to evaluate and summarize the situation, and as a result, what actions should be taken. Because these conclusions are made based on the version of events the representative interpreted, these conclusions include their own built-in biases. To put it frankly, all NLP models have inherent biases, but the leading models all have teams and processes in place to help mitigate the worst of it. 


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OpenAI is especially careful here, and while GPT-3, an NLP model that produces human-like text from simple text-based prompts, still displays some bias in generic use cases, when fine-tuned for specific use cases, those biases will have the potential to be dramatically reduced. GPT-3 is more than what meets the eye, besides monitoring for human biases, GPT-3 is able to transform data into storytelling. For example, if you own a company, and have metrics measuring sales, reviews, returns, approval ratings, etc., you are responsible for reviewing and deciphering complicated data to formulate your conclusive takeaways. In using GPT-3, your data is converted into storytelling you can understand and take action from.

While we should always be striving to reduce bias in these systems, especially in powerful large language models like GPT-3, we should never lose sight of the real-world improvements that these systems can provide today and how they ultimately allow for more user-friendly and inclusive access. Realistically, with the number of people using the internet throughout the day, it is virtually impossible to audit all of the biased word embeddings that exist. We see this affect our democracy, communication, and greater community every day. All of this emphasizes the urgent need for an AI monitoring mechanism in today’s world. Not only do AI technologies have the ability to prevent acting on human biases, but they also limit the aftereffects of NLP bias.

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