Understanding Bias in AI Code: 70% of Code Is Made by AI
70% of code is AI generated, and AI agents will be re-trained by their own code resulting bias algorithms. How can we prevent this?
The Coding landscape is changing fast. In the past, people wrote all the code by themselves or with the help of a stack overflow. Now, AI can write most of it.
For the past few years, we’ve seen AI-generated code improve dramatically. Last year alone up to 70% of the code in some projects was AI-generated.
At the same time, the global AI market is predicted to grow at a compound annual growth rate of 37.3% between 2022 and 2030 ( source )
This rapid evolution raises an important question: if AI learns from the code it creates, will it improve over time, or simply replicate its own mistakes and biases?
The AI Code Revolution
AI tools have become a must-have in the coding. They help us test ideas quickly and handle boring tasks (unit testing or centring a div). This has changed how we write code.
92% of developers are now using AI coding ( source ) and over 25% of new Google’s products is AI Generated ( source )
In many projects, it is hard to differentiate if a human or a computer wrote the code. AI code generation gives us the chance to work faster and makes coding easier for everyone. But it also brings risks and new problems.
Bias in AI Code
Every AI tool learns from the data it is given. When AI learns to write code, it learns from both good and bad examples. It can pick up mistakes or biases from that data. Here are some issues:
Feedback Loops: If we use code made by AI to train new AI, any mistakes or biases can be repeated and even get worse. This can make bad patterns become common.
The AI suggests comments in the same tone it learned. Developers often use these suggestions, and the same style spreads over time.
Inherited Blind Spots: Even the best AI is only as good as the data it learns from. If old code has hidden biases and security flaws, or even if it got depreciated or discontinued, these can be built into new software.
An AI vulnerability scanner trained on old code might miss new risks. This gives a false sense of security.
Automation Over Correction: Because AI makes code so fast, we might trust it too much. If people do not check its work carefully, mistakes can continue without correction. Don’t trust new technology blindly.
The AI quickly creates unit tests. But if developers do not review them, the tests may be weak and the code might look safe when it is not.
This problem is not just about technology. It makes us think about who is responsible for the code. If AI mostly learns from its work, we might lose the variety and creativity that humans bring.
How to Stop AI Bias from Growing
How can we stop bias in AI code while still using its power? To do this, we must use several ideas together:
Human-in-the-Loop Review: No matter how smart AI gets, we still need people to check its work.
Diverse Training Data: To reduce bias, we must give AI a wide range of examples. This means using code from different programming languages and up-to-date libraries.
Transparency: When AI makes code, it should explain how it did it and label it as AI-generated. This helps developers find and fix any bias before it spreads.
Regular Updates: The world of coding changes fast. We should update the data we use to train frequently to prevent vulnerabilities in AI-generated software.
Looking to the Future
It may be surprising that 70% of code is made by AI, but this also gives us a chance to improve. We are entering a new time where human creativity and computer speed work together to create great things. However, we must use this power carefully.
We are entering an era where human ingenuity and machine efficiency work hand in hand to create innovative software. It may be surprising that such a large portion of code is now generated by AI, but this trend will only grow and presents both opportunities and challenges.
GitHub CEO has even predicted that AI could soon be writing up to 80% of code will be AI-generated. ( source )
If we check AI code carefully, use many kinds of examples, and work together openly, we can reduce bias. The future of coding will come from both computers and people working together.
Let’s Connect!
💼 LinkedIn: alex-kazos
👨🏻💻 GitHub: alex-kazos