How Lack of Diversity in Technology Leads to Coding and Algorithm Biases

Artificial intelligence (AI) is an essential part of our modern world, shaping everything from healthcare to transportation. As such, it has become increasingly important to recognize the flaws behind the code. AI technology is plagued with flawed algorithms which, if left uncorrected, can negatively affect the lives of people everywhere.

Before eliminating algorithm bias, it is important to understand the root cause of the problem: lack of diversity within technical teams who create them. The US Equal Employment Opportunity Commission (EEOC) estimates that more than 83% of technology executives and managers are white and just over 20% are women. In fact, white and male employees occupy the majority of all technical positions.

Having a nearly homogenous workforce is not only unethical or detrimental to a fair company culture, its effects can also seep into code. Without racial or gender diversity in the technology, the algorithms are susceptible to programmed and implicit biases in the coding. This can lead to unconscious biases in algorithms, meaning programmers don’t even realize they’re there.

The idealized world of algorithmic technology

There is an irony in the fact that algorithms are often biased towards the perspective of the majority white male. Algorithmic technology is often touted as a solution to bias issues in several fields, for example, AI was designed to help recruitment professionals screen candidates without the influence of human biases. In an ideal world, AI technology would prevent the attribution of racial and gender stereotypes to job applicants, such as the attribution of hostility to black women.

Likewise, AI has the potential to help healthcare professionals interpret medical data more accurately. This powerful technology can identify patterns, such as trends in a patient’s medical history, faster and more efficiently than humans. This allows for better diagnoses and treatment plans for patients.

Although algorithmic technology has a lot of potential, it often perpetuates biases. When an undiverse group of professionals provide training data for AI, they will likely adopt the subconscious biases of the group as they learn.

Algorithms gone wrong

AI hasn’t gone mainstream for a long time, but there are already plenty of examples of algorithms gone wrong. Tay, a Twitter-based chatbot released by Microsoft in 2016, offered a first look at the influence of biased training data. As Twitter users began sending misogynistic and racist tweets to Tay, the Twitter bot learned to post inflammatory messages in less than 24 hours.

In another example, when YouTube released a kid-friendly algorithm that blocked adult content, it also blocked content from LGBTQIA+ creators. If left unaddressed, this issue could have deeply affected their performance on the platform, unfairly punishing marginalized YouTube creators. Given that 92% of software developers are heterosexualit’s clear that underserved populations can easily be hurt or overlooked by the majority of coders.

Recent AI issues come as no surprise

More recently, Google’s AI has generated significant controversy. The language model LaMDA learned about racist and sexist stereotypes — not the first time Google’s technology has perpetuated harmful biases. These issues likely persist because Google’s engineering team fails to diversify.

Google Diversity Report shows that in 2022, 74.1% of its global technology employees are male and 44.4% are white. Google also sees the lowest retention rates among female, black and Latino employees. Although they have made strides to improve the hiring and retention process, the lack of diversity is still evident and may take some time to correct.

Reform is essential for effective AI

Although the algorithms are advanced enough to work, they are still not as efficient as they should be. Making AI useful – without any of the harmful biases it has been prone to in recent years – depends on the efforts of tech industry leaders to diversify their workplaces. This is especially the case within the development teams which are heavily dominated by white men.

Diversification can take time because the lack of diversity often stems from a deep-seated corporate culture and past discriminatory practices. However, one action that business leaders can take is to implement internal governance policies, especially for larger companies. Since AI collects and analyzes large amounts of data, these policies can help organizations oversee the use of this technology and prevent data misuse. The the future of enterprise AI can even afford to use AI to prevent these same transgressions.

Stepping into a brighter future for algorithmic technology

As we enter the future of algorithmic technology and its inevitable wider application, it is essential to address the biases of this intelligent technology as a central issue. Since AI is trained and coded by predominantly white male engineers, the technology generally perpetuates stereotypes held by the majority in the tech industry.

Left unchecked, the lack of diversity among developers can lead to negative experiences for historically underserved populations. This is all the more the case as AI is implemented in more and more parts of our daily lives, such as our careers, our homes and our community services, including the police force and the medical field. . However, with some reform, technology leaders can minimize the damage while producing incredible benefits for our world.

Image: ThisisEngineering RAEng

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