Debugging DL

The push for more diverse tech companies is a clear understatement of the hierarchy that riddles our society. I want to cover two areas that I’ve come across that focus on bias within a tech space. My first focus is going over algorithmic biases as well as its effect in the Tech job market. I feel like these two are my main concerns as I searched for a space in the technology ethos.

As we know algorithms are a combination of math and code where they’re made by people collecting data. These personal biases in the real world can be mimicked and sometimes integrated or exaggerated by AI systems due to that connection. This concept is what you would call the algorithmic bias. Many would say that a bias isn’t an inherently terrible thing . It is a method used by our brains to make shortcuts by finding patterns in data around us. An example would be seeing the color blue all of your life and one day you were introduced to the color orange. Your brain would process the color being odd and out of place. This doesn’t become a problem unless we don’t acknowledge exceptions to patterns or unless we start treating certain groups of people unfairly. We do currently have laws that protect us when it comes to discrimination but this gray area should be paid attention to more. Knowing about Algorithmic bias can help steer clear of a future where AI is used in harmful discriminatory ways.

There is at least one type of algorithm biases we should pay attention to;

Data reflecting existing biases

An AI trained on certain news articles or books for the word “babysitter”.It is more likely to refer to a woman while the word “programmer” is likely to refer to a man. A simple search in Google will show you pictures of women with children mostly . The same with men happens with programmers. We know that these job titles are non binary because either men or women can do both of the jobs. When searching “programmers 1960s” it shows more women and this is where algorithms are not that good at recognizing cultural biases as of right now. Presently this can cause hidden bias to be spread to anyone who accesses the internet. If we don’t collect or use training data that categorizes protected classes like race or gender then algorithms couldn’t possibly discriminate.

This also flows over into hiring processes for Africans in the diaspora . This bias in the technology space has led to spaces where there are few black people in Tech spaces and products. Without highly-skilled black employees who can contribute to product development technology manufacturers run the risk of building and selling products that aren’t sensitive to those biases and often perpetuate that bias. Machine learning is an example where it depends on thousands of data points. It often learns from information that can be old and outdated where that favors certain places or colleges, races, and genders. Also job post are written in a certain way to find people who fit a certain dynamics based off of those criteria. If the AI that filters resumes and applications look for only certain colleges ,training courses ,or points in that those data sets , It kicks you out of the running for a position before even being interviewed. If it was more inclusive to recognize words that are associated with African Americans like the different organizations, societies and associations it’ll be a more balanced environment when it comes to diversity.

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Angel Brown

Angel Brown

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