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How to Identify and Mitigate Gender Bias in AI

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Joshua Feast

Despite our best efforts to build neutral machines, artificial intelligence (AI) is ultimately a mirror of our human selves. Since AI is made by humans, it has the potential to inherit the human biases we have and apply those tendencies to its models and systems. This is why it’s no surprise that AI systems are susceptible to gender bias, given it’s a learned trait from its human creators.

Naturally, this is cause for concern if it’s not properly addressed. As gender bias pervades many AI systems, this can result in a range of negative consequences, from reinforcing societal stereotypes to affecting people’s lives unintentionally, such as influencing college admissions, job hirings, and loan application approvals.

One common example of this bias exists in natural language processing (NLP), which is the branch of AI focused on how computers can process language like humans do. NLP and the more advanced large language models (LLMs) are a fundamental component of many of today’s most popular AI platforms, from voice assistants like Amazon’s Alexa, to flourishing generative AI applications like OpenAI’s ChatGPT.

However, instances of gender bias in NLP are not isolated incidents but rather recurrent patterns. Meaning, women can be met with incorrect results and high error rates when interacting with AI. This can be attributed to the underrepresentation of women in the technology field, combined with the under-sampling of women in the data that shapes AI.

To foster equitable technology, it’s imperative that researchers and machine learning teams across the industry collectively strive to rectify this imbalance. Fortunately, promising efforts are well underway to understand this issue and address it head-on.

Understanding Root Causes of Bias in AI

Just as bias in human interactions is impossible to completely eliminate, it is difficult to stop in machine interactions, too. Still, AI companies can work to better understand where these biases come from to reduce the harmful consequences. In machine learning, bias implies a higher error rate for specific demographic categories. Since there’s no single root cause of such bias, researchers must consider multiple variables when developing and training machine learning models, including:

  1. Incomplete data sets are predisposed to bias: AI is only as good as the data it’s trained on. Bias occurs when demographic categories are underrepresented or absent from the training data. Models trained on such data may struggle when applied to new data containing those missing categories. For instance, if female speakers constitute just 10% of the training data, then errors may spike when the trained model is applied to females.
  2. Human bias seeping into training models: Commercial AI systems predominantly use supervised machine learning, where humans label the training data to instruct the model’s behavior. However, human bias, whether conscious or unconscious, can inadvertently infiltrate the labels, subsequently encoding misclassification and bias towards a particular label into the machine learning model.
  3. Features and modeling techniques: The measurements used as inputs for machine learning models, as well as the training methods themselves, can also introduce bias. For example, female speakers have historically had poorer performance with field speech synthesis—a.k.a. text-to-speech technology (e.g., the late Stephen Hawking’s computer-generated voice)—as well as automatic speech recognition—a.k.a. speech-to-text technologies (e.g., closed captioning), due to underrepresentation. As a result of how the speech was being analyzed and modeled, taller speakers with longer vocal cords and lower-pitched voices, characteristics typically associated with men, were often more accurate.

What Steps Can Mitigate Gender Bias in AI?

Addressing AI bias is a nuanced endeavor. There is no “right” way to eliminate bias, because even defining what fair or unbiased looks like can vary from company to company, and even department to department. For executives looking to harness the potential of AI while remaining vigilant about bias, these steps should be taken within machine learning teams:

  1. Ensure diversity in training samples: Include a balanced representation of both female and male samples in the training data, as well as diversity of accents, race, geographic backgrounds, etc., for a fully diverse set.
  2. Diversify the human labelers: Collaborate with labelers from various backgrounds, cultures and races to minimize bias in labeling.
  3. Measure accuracy levels separately for different demographic categories: This helps to identify instances where one category faces unfavorable treatment, so it can be addressed accordingly.
  4. Correct for unfairness: Gather more training data from sensitive groups and apply modern machine learning debiasing techniques that not only penalize primary variable errors but also address fairness concerns.Of course, it would be irresponsible to discuss bias in AI without also addressing the need for technology roles and companies to prioritize eliminating gender bias. As of 2023, only 11% of the engineering workforce is female, and females leave the tech industry at a 45% higher rate than men. It’s imperative to champion female engineers in the industry and ensure there’s representation on a team, so that there is diverse representation of those who are designing new AI models, applications and systems. To successfully banish gender bias, we need to go a step beyond gathering data about women and include women in understanding how the data represents them.

    Acknowledging the existence of bias in AI is a significant first step as it’s necessary for understanding the scope of the problem and laying the groundwork for further action. While recognizing the initial efforts we’ve made to tackle this issue above, it’s clear that a concerted and collaborative next-step is necessary to ensure AI technology is both effective and equitable for all members of society. The potential benefits of AI can indeed outweigh the risks if we remain committed to diminishing bias and fostering fairness in AI systems. It’s a shared responsibility that requires continued dedication and innovation from the entire AI community.

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