As machine learning and artificial intelligence take a more center stage in tech, the tech world is awakening to the realization that there is a hitherto unrecognized bias in tech.
Machine learning is the aspect of artificial intelligence that focuses on building systems that learn or improve performance based on the data they consume.
Artificial intelligence, meanwhile, is the development of computer systems able to perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Machine learning and artificial intelligence require a lot of data to learn from. So what you are practically doing is teaching the computer about a particular subject by exposing it to many examples.
These examples come in the form of data. First, you input a lot of data into the system, the computer processes it using an algorithm you created or inputted, and the system begins to look for patterns.

All of this is so the computer can recognize, estimate, and predict by getting familiar with many samples, the A.I can easily recognize another copy or variation of the same item. After consuming a lot of datasets, it can make summations and deductions, and other reasonable estimations. Most importantly, with an extensive knowledge base, a well-taught computer can make predictions with a high degree of accuracy.

This new industry is data reliant. The more diverse, more extensive, and more qualitative the data available, the better the outcome.
This is where there is a bit of a snag. Most of the data currently available to the data science world is generated from nations that are majorly Caucasian in population. The result of this is a dataset that is monolithic.

In some cases, minorities are underrepresented. In other cases, they are absent, and in some cases, they are estimated in. A data scientist assumes they know what a minority group looks like, prefers, or has gone through, so they input what they think is right.

This has resulted in A.I powered cameras that cannot recognize anyone who isn’t white. We have algorithms that don’t know what to do with details of minorities. Law enforcement is forced to ignore their own systems because it racially profiles. After all, the dataset it was built on is heavily skewed against ethnic minorities.

All of these are issues that arise because humans design systems, and humans work within the knowledge they are familiar with. If the data science and artificial intelligence industry continue to be white-heavy, the algorithms and applications produced in the industry would also be implicitly biased against minorities.

A great starting point for getting this bias out of tech is by encouraging underrepresented groups to come into the industry. When people are present and able to speak for themselves, they can correct assumptions and misrepresentations that negatively affect them.

For many of these people, the cost of getting this education is a barrier to getting into the industry. A few who have the education find themselves distant from where the industry is located. For a start-up that recognizes this problem and is interested in hiring minorities to help correct it, the cost of relocating the talent can be a dissuading factor.

Zart Talent is bridging the gap between both parties. By helping tech companies get the best available experts from minority communities remotely, we help bring diversity and inclusion to these companies.
The result is a tech product applicable anywhere in the world—nuanced, representative, and accepting of differences and functions without bias.

Visit ZartTalent.org to find out how you can join us in building a bias-free and more inclusive tech world.