The Bias in the Machine: Training Data Biases and Their Impact on AI Code Assistants’ Generated Code

1981 was a banner year for music inspired by computers and futurism. From the dystopian “Moving Pictures” off of Red Barchetta’s album Rush , to the entirety of Kraftwerk’s Computer World , artists were looking ahead with technological precision. As Kraftwerk said, “I program my home computer, beam myself into the future.” Another such album is Ghost in the Machine by The Police, which uses technology’s growing influence as a central theme. They took a more cynical look at the potential downsides of technological advancement. The album still resonates as their view of a world in flux still rings true.
Since 1981 and the home computer revolution, we have seen technology develop at warp speeds, culminating (so far) in the rise of AI code assistants. These tools promise to streamline the coding process, but just like The Police album referenced above, which has a sense of alienation beneath the surface, there is also a hidden factor at play.
Biases in AI
As negative as it can be, biases breed disillusionment and creep into AI assistants’ training data. The Police sing in the song “Invisible Sun” about the positive influence of an unseen force; however, the negative is also true. Biased AI tools can lead to unintended consequences and undermine AI tools. So, let’s discuss how these biases can become the “ghosts in the machine” of AI tools.
“Too much information running through my brain”
Machine learning powers AI code assistants and generative tools, revolutionizing how software is built. Their primary function in coding is to handle repetitive tasks and propose code changes seamlessly. They are considered at the level of a junior developer and need a lot of data to improve. This comes in the form of a huge collection of previous projects for an AI coding assistant. The AI uses it as a version of an instruction manual. That data trains the AI, and by analyzing the code, it can understand patterns and write code more efficiently.
The key here is that the data quality and variety are high. This information is the foundation of AI’s knowledge and must be good enough to ensure quality results.
“They subjugate the meek, But it’s the rhetoric of failure”
The Achilles’ heel of AI code assistants lies in the biases concealed within them. Their foundation is the data on which AI models train. Just as an off-center foundation will form cracks and lead to an unstable structure, the same is true for AI models. Data riddled with hidden biases will compromise the entire AI model. Biases can take several forms:

Social biases – can reflect societal prejudices
Gender biases – might favor one gender over another
Cultural biases – might skew data in favor of specific cultures

The reasons for biases creeping into AI models might not even be nefarious or ill-intended. Sometimes, historical data reflects past inequalities. Other times, data collection methods might introduce a bias. A quick example would be an AI meant to help dispense medical advice. If trained primarily on data written by men, it may fail to capture some nuances of women’s health.
The consequences of biased training data are far-reaching and impact an almost infinite number of scenarios, from loan approvals to job recommendations. Let’s take the career example. A company uses an AI code assistant to help in their hiring process. The model trains on past hiring data. If the data indicates that the most successful hires were men with a specific educational background, it might favor resumes resembling those candidates. This scenario provides a simple and obvious example of how certain candidates could be disqualified based on their gender or education level.
Biased training data can:

Perpetuate existing inequalities: Loan approval systems trained on historical data that favored certain demographics might continue that bias in their automated responses.
Discriminate against certain groups: A clothing site’s AI recommendation system might train on data heavily skewed towards a specific size profile in past purchases. This could make it difficult for individuals outside that demographic to find properly fitting clothes.
Deliver inaccurate results: A weather app trained on data from a specific region might struggle to predict weather patterns in other locations.

“I’m building a machine that’s not for me, there must be a reason that I can’t see”
AI code assistants learn by analyzing training data patterns, sort of like learning a new language. If you learned French by reading Victor Hugo, you might struggle to order in French in a Parisian Cafe. Similarly, biases in training data lead the AI assistant to develop biased patterns in generated code.
This can play out in several ways:

Biased naming conventions: If training data is focused on male pronouns when referring to developers, the system might then be calibrated to generate male-dominated code variables and unintentionally exclude female developers.
Inefficient algorithms: Training data focused on solving problems for specific user demographics might struggle to generate efficient tasks outside that purview. An AI code generator trained to generate website code might not generate the best mobile device code.

These biases seem minor, but the consequences can be dire. Algorithmic discrimination might perpetuate stereotypes and reinforce unfair treatment in automated decision-making. Additionally, biased code can create security risks. AI assistants trained in closed network code might have exploitable weaknesses if ported over to a more open-source environment.
“You will see light in the darkness / You will make some sense of this”
Biases in training data can become the “ghost in the machine” of AI code assistants. However, by implementing basic practices, we can ensure that AI tools serve the greater good:

Build a diverse training set: Like a healthy diet requires various foods, AI code assistants need diverse training data. Teams must actively seek out data from a wide range of sources and demographics. Including code written by programmers of all genders, ethnicities, and backgrounds should be included. The more diverse the training data is, the less likely a bias will creep into the end code.
Human oversight: While capable and powerful AI code assistants should not operate in a vacuum, human oversight is necessary to review generated code for potential biases. It kind of works like a code editor that can also tell what is fair and what is not. Having a human element will identify and address biases before the code is deployed.
Debiasing the algorithm: As AI research evolves, scientists are developing techniques to create debiased algorithms . These algorithms are designed to be more robust and less susceptible to biased training data. They will offer a neutral foundation from which AI code assistants can learn.

With these strategies, we can ensure that AI code assistants become powerful tools for progress and not instruments of bias.
“We are spirits in the material world”
Technology’s influence, explored in albums like The Police’s Ghost in the Machine, is more relevant than ever. Biases in training data hold back AI code assistants from fulfilling their promise to revolutionize software development. This hidden factor is like an “Invisible Sun” influencing unseen forces. The biases can creep into generated code and lead to unintended consequences.
The future thought is not predetermined. Building diverse training sets into AI code assistants, incorporating human oversight, and researching debiased algorithms will help mitigate the biases. Imagining a world where AI code assistants are fortresses of fairness, not instruments of prejudice, requires us to ensure that ethical principles and a commitment to inclusivity guide AI development. There is a vast potential out there, and by addressing the “biases in the machine,” we will ensure they are powerful tools for progress and not perpetuators of bias.