Human beings are inherently biased. We judge people based on cultural norms, our lived experiences, and our personal beliefs. While this isn’t a problem in many situations and can actually be helpful in everyday life, it can also create inequality and disparities in areas like healthcare, personal finance, and hiring.
It’s impossible to completely remove bias from human evaluations, although it is possible to reduce any biases by becoming conscious of them and actively working to eliminate them. Another way we can remove bias is by putting in safeguards like blind evaluations or turning to automation.
Computers and algorithms are viewed as completely neutral in evaluations. And while it might be true that computers do not have their own opinions, it’s also true that humans are the ones programming them. Unfortunately, that’s when we start to see issues like algorithmic biases. But why does this matter and what tools can we use to find and eliminate these biases?
Why Do Algorithm Biases Matter?
Some tech innovators claim that algorithms aren’t biased. But the issue is not whether algorithms themselves are biased—it’s about the people and systems informing the algorithm. An algorithm created by someone with certain biases and privileges will often reflect those biases and may even teach itself to be racist and sexist over time with machine learning.
Algorithm biases matter because they can perpetuate inequalities and affect people’s lives. They can require different standards for different people and may even affect a person’s ability to get a job or create health disparities with severe consequences.
If you’re not part of a group that might be targeted, then you probably haven’t given algorithm biases much thought. But if you are, they could be contributing additional obstacles in your life. As a society, we need to ensure equality for all—which is why it’s important to address this issue now.
TCAV is Google’s Primary Tool for Detecting Bias
So what’s the answer toward biases in algorithms? More technology, of course! Although there are many tools and programs that are being developed to find biases in algorithms, Google’s primary tool at the moment is a research project known as Testing With Concept Activation (TCAV). TCAV’s system is designed to scan algorithmic models for common biases, such as race, gender, and location. The company hopes to use the system in medical applications eventually.
Python Can Also Help Detect Potential Bias
Python is one of the most widely-used and popular programming languages in the United States. It is easy to learn, simple, yet powerful, versatile, and well-supported. Although it is used for many different applications, including photo-based social services like Instagram and Pinterest, Python might also be able to find biases in algorithms.
A good example of this was the use of Python deep learning libraries to find bias within written media outlets. Victor Saenger, who ran the experiment, explains the entire process, which provided good accuracy scores and showcases how this powerful language can be used in bias detection. While advanced Python skills are needed for these detection methods, it’s a great tool that doesn’t require the resources of a giant company like Google.
Looking to Utilize AI? Pymetrics’ Audit-AI is Worth Checking Out
The best thing about AI is that it doesn’t require much human intervention once it’s set up. The worst thing about AI is that it might teach itself something undesirable (like biases against certain traits or demographics) and disadvantage various populations in the course of its calculations. That’s where Pymetrics’ Audit-AI comes in.
The Audit-AI tool is designed for machine learning tools to detect bias and head off common pitfalls of algorithms that evolve with the input of more and more data. The tool is intended to help people using AI to create fairer systems and to prevent discrimination.
Preventing Bias in Advanced Computing
We’ve known for some time that algorithm bias is real and can affect people’s lives in profoundly negative ways. Fortunately, some of the brightest minds in tech are working on ways to offset these issues and provide easy-to-use tools for anyone who might need them.
It’s important to remember that whenever you’re working with lots of data, be careful about who and what gets excluded—or included in your calculations. Those calculations might have ripple effects you never would have expected.