The topic of this blog suggests this is a herculean challenge that some companies are either unwilling to tackle, or simply don’t know how to hire a workforce that represents an ever-changing society. Some experts have suggested that such deep-seated unconscious biases, in a setting, where some like to hire and work with people who look and talk like them and who, have similar backgrounds and experiences is pervasive throughout the tech world. However, even with the best of intentions, hiring biases—raises red flags for qualified potential candidates who would rather work for companies who are diverse (in both words and actions) rather, than ones who may hire them as the token employee for diversity within their ranks.

Shama Hyder’s article titled 5 Steps to Help Tech Companies Reduce Bias in AI   is a good guide for any company who is unsure of how to tackle hiring biases.

“1. Make tech education accessible: Artificial intelligence systems are biased, and the technology usually follows the viewpoints of its creators. While society has changed considerably in the last half-century, corporations still have underlying biases (whether they realize them or not). It’s essential that we take active steps to reverse our biases so that we can prevent further biases from developing in artificial intelligence, and the best way to do this is to make the tech industry more accessible to a wider range of people.

Initiatives like Girls Who Code, AI4ALL and other educational programs make it possible for children to develop an interest in technology. To reduce bias and make the tech industry more diverse, leaders must invest in the education of young people so that they can develop an interest in the field and build the skills necessary to pursue a career. Tech companies should invest in a range of students early on, knowing that investments in education yield long-term results.

2. Hire and promote with diversity in mind: Despite numerous call-outs of major industry leaders, the tech world still lacks diversity. The Harvard Business Review reported that leading companies like Google have only crawled ahead toward more diversity among staff members. Even nearly seven years after tech companies started reporting diversity efforts, most leading tech organizations are failing — with minorities only making up single-digit percentages of the overall workforce.

3. Evaluate Data Sets: Bias is already in your data sets, and you shouldn’t ignore it. To counter biases, every AI technology developer should devote time to evaluating the data sets with which the system was created. This evaluation should take place at every stage of development, from the initial design to the final proofs. 

The best way to evaluate AI for biases is to ask specific questions. The FTC provides guidelines to determine if artificial intelligence is on the right trajectory, and to clarify what is allowed (or prohibited) by law. Developers must question themselves and the technology they are creating. It is imperative that developers understand their own biases — especially the unconscious ones — and can evaluate their work for the same. Working to eliminate biases is not a linear process, as it will take multiple back-and-forth steps.

4. Regularly re-evaluate systems to detect bias: Rigorous evaluations can’t stop at data sets. Technology is growing and changing at such a rapid pace, and strategies, systems and even outcomes should be re-evaluated each step of the way. In order to reverse the biases already in artificial intelligence and prevent further biases from developing, companies must check their work over and over again. 

5. Adjust and repeat the process: Technology has never developed linearly. The same applies to artificial intelligence: Data, processes, systems and even the bots themselves must be adjusted over time. The best avenue forward is to take a preventative approach. That means that these five steps to reduce bias in AI should be adjusted and repeated multiple times on any given system”.

Bottomline: Confirmation bias is the human tendency to process information by looking for — or interpreting information consistent with our own beliefs. Today, we know that confirmation bias affects technology development and when we allow our biases to distort what we think we know, it alienates qualified candidates, who could help make our companies grow.

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