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How AI Can Effectively Solve Inherent Human Biasby@sunnysaurabh
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How AI Can Effectively Solve Inherent Human Bias

by Interviewer.AIJanuary 30th, 2022
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Sunny Saurabh, Co-founder, and CEO of Interviewer.com, talks about biases in artificial intelligence. He says the Halo Effect is a cognitive phenomenon when our primary judgment of an individual is based on our preferences, intolerances, and social perceptions. This effect directly influences our decisions regarding many areas of life, both social and workplace. It can be solved using the state-of-the-art technology of our creation – artificial intelligence. It is our responsibility to ensure judgment-free, accurate, and thorough data collection.

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It is the complexity of our minds that has helped us build the world we live in today. But that sophistication comes with a cost: On one hand, it can develop and execute grandiose ideas, pushing our reality beyond its limits. On the other hand, it can be easily contaminated with cognitive imperfections. Those appear to compensate for high-speed processing and the overwhelming amount of stimuli our brains process every day.

In fact, our unconscious brain processes and sifts through vast amounts of information, looking for patterns processing 11 million bits of information every second. But our conscious minds can only handle 220 000 times less than that. 

Young generations, in particular, are being thrown into a dynamic world of careers, always racing, relentlessly pursuing prestige, and being overstimulated by constantly developing technological innovations. According to Forbes, 59% of Millennials and 58% of Gen Zs suffer from chronic burnout.

With such growing complexity of our reality, cognitive biases multiply over time. Let’s discover those effectively solved using the state-of-the-art technology of our creation – artificial intelligence. 

Halo Effect

The Halo Effect is a cognitive phenomenon that refers to situations when our primary judgment of an individual is based on our preferences, intolerances, and social perceptions. It can be exemplified by people simply assuming that a good-looking person in a photo is also generally good and trustworthy in person. This effect directly influences our decisions regarding many areas of life, both social and workplace. 

This human bias manifests most when observing the difference between the “talker” and “doer” during job interviews. The first one is a person with developed communication skills and makes a great first impression. Even though they may charm the hiring specialist in an interview, they might not excel in the position. On the other hand, the latter is a person who could perform awfully on the call, and the interviewer may find him very hard to comprehend but does a great job in the role.

As simple as it gets – the way you design input will determine what outcomes you can expect. Hence, it is our responsibility to ensure judgment-free, accurate, and thorough data collection and proper model training followed by frequent AI projects validation to use the technology to its full potential.

Similar-to-Me Effect

The beliefs that we hold about ourselves serve the essential purpose of shaping our self-esteem, self-confidence, and self-image. The perception of ourselves reflects on our life satisfaction and the results we achieve. Then, it is no surprise that when we perceive wearing glasses as a feature showing intelligence because we wear them ourselves, we tend to assume they represent the same value when we see someone wearing glasses. 

This applies to political preferences, life partner choices, and hiring processes – frankly, most areas of our lives. Research led by prof. Sears shows that those interviewers who identified an interviewee as similar to themselves believed that the interviewee was more suitable for the position and said they were more likely to hire them. 

No machine is similar to a human being. Therefore, there’s no room for any similar-to-me effect. AI algorithm’s transparency, fed with well-defined and diverse data inputs, solves this cognitive bias once and for all, ensuring fairness and equality among all applicants.

Stereotyping 

Stereotyping implies that a person holds a set of features and characteristics that we presume are shared by all members of any particularly defined group. Those are merely far-fetched and inaccurate assumptions, influencing negatively how we perceive certain people. Moreover, it has an exceptionally harmful influence on our daily lives, and especially in any workplace, creating a hostile environment. Acting on stereotypes can result in increased conflicts and a tense atmosphere that brings low morale, overall reduced productivity, and employee retention.

Every person is unique, representing different life experiences, various traits, and vast skill-sets. In social psychology, stereotyping is relentlessly recurring that can’t be entirely weeded out from humans’ cognitive structures. But there’s a light at the end of the tunnel, foreshadowing the upcoming change – artificial intelligence. 

AI-driven engines, when programmed accordingly, won’t end up shifting towards cognitive biases such as ethnic, gender, or physical features that humans’ brains conveniently fall for. Over time and with enough datasets to analyze, machine learning will rule unbiased customization, providing efficient results according to the initial algorithm’s input. 

Adapting To What The Future Holds

What AI technology can take away is the repetitive, tedious, and time-consuming tasks across various industries and job positions. This applies especially to hiring processes, allowing to transfer resume pre-screening and candidates stack ranking responsibilities from hiring managers to user-friendly AI-driven platforms. Moreover, it can free up time for more strategic assignments that are more intellectually demanding and complex in ways that machines haven’t yet learned how to solve – human interactions. 

With skills and staff shortages, AI-driven hiring platforms can be used to measure and better allocate employee talent across the entire organization. Recognizing bottlenecks and providing insights into potential educational opportunities for employees can help level up the skills of current employees to save both time and money. 

Companies are moving towards increased automation and lifelong learning. Roles are evolving, industries are limitless, and people are more conscious of their time and how they are spending it. Organizations ask for traits like adaptability, which means employees are most likely to switch roles as the company evolves over time. 

These days, flexibility, diversity, and acceptance are critical to an optimized workplace. The world faces a shift from a profit-oriented to an employee-oriented approach,  building internal resistance to market fluctuations. Caring about employees’ satisfaction connects directly to the organizational atmosphere, building loyalty and deep engagement to the company’s mission and development. 

Imagine if we were to consciously process every information we’re exposed to, every little detail and nuance. We would likely collapse, burning our synapses inside out. That’s why our clever and inestimable brain uses cognitive biases to handle often overwhelming environments as efficiently as possible. 

But in some situations, that's not ideal, which is precisely where AI steps in. Hence, instead of continuously falling for humans’ cognitive imperfections, embrace the prominent bias-free future of AI and ensure the ease of development for your business strategies.