Artificial intelligence is an increasingly prominent part of our lives, in areas you may not even think about. Chances are you've had a travel problem in the last year or two, caused by the many disruptions the COVID pandemic has wrought on the industry. When you messaged your airline's Facebook page, did you encounter a bot? I bet your school-age children ask your smart speaker at home 1,000,000 questions per day, or ask your respective brand's speaker to play 46,789 songs per day. I bet many of you reading this have applied for a job during the pandemic, when the job market has very much favored job seekers.

AI and machine learning are useful tools in helping organizations implement more efficient, unbiased and effective hiring processes. AI can free human recruiters (who often spend 40 percent of their time sorting resumes) to do more high value tasks, like building relationships with candidates, and streamline and automate interview scheduling, candidate screening, and measure specific recruitment KPIs. Critically, AI algorithms used in the hiring process must be trained on diverse historical data representative of real-world populations to ensure that bias is not being perpetuated. Incorporating input and perspectives from various teams and individuals within the company, such as recruiters, data scientists, subject matter experts, and managers, is key to developing algorithms that aren't governed by partiality. AI models should also undergo rigorous testing prior to production, and be continuously evaluated and retrained over time.

AI can help eliminate bias. "We don't see things as they are, we see them as we are." So wrote Anais Nin, rather succinctly describing the unfortunate melange of biases that accompany these otherwise perfectly well-functioning human brains of ours. In a business context, affinity bias, confirmation bias, attribution bias, and the halo effect, some of the better known of these errors of reasoning, really just scratch the surface. In aggregate, they leave a trail of offenses and errors in their wake.

AI can help eliminate bias. "We don't see things as they are, we see them as we are." So wrote Anais Nin, rather succinctly describing the unfortunate melange of biases that accompany these otherwise perfectly well-functioning human brains of ours. In a business context, affinity bias, confirmation bias, attribution bias, and the halo effect, some of the better known of these errors of reasoning, really just scratch the surface. In aggregate, they leave a trail of offenses and errors in their wake.

Can businesses trust decisions that artificial intelligence and machine learning are churning out in increasingly larger numbers? Those decisions need more checks and balances -- IT leaders and professionals have to ensure that AI is as fair, unbiased, and as accurate as possible. This means more training and greater investments in data platforms. A new survey of IT executives conducted by ZDNet found that companies need more data engineers, data scientists, and developers to deliver on these goals. The survey confirmed that AI and ML initiatives are front and center at most enterprises.

Co-founder & CTO at Orient Software, a software outsourcing company that employs top software engineering talent from Vietnam. Artificial intelligence (AI) is changing the world. It's transforming the way we work and live. It's doing tasks that humans used to do. Instead, this is a story about how AI can help make businesses and their employees more innovative, creative and efficient. AI is already enhancing our lives in many ways.

Children inevitably adapt to the culture in which they were raised. Parents or guardians shape the lens through which they view the world, largely through the examples they set. Many parents experience humored horror when a child picks up on an inappropriate word, likely from an overheard adult conversation, and begins to employ that expression in their everyday speech. It does not matter whether the parent is intentionally or unintentionally crafting the lens for the child -- they will still pick up on the parents' viewpoints and habits. We are witnessing this same progression in the tech world.

Angela Benton, founder and CEO of data-science company Streamlytics, talks about using ethically sourced data to train AI. TODAY'S MUST READS: New Research: Status Reports Can Make Teams Less Effective

Bias is a major topic of concern in mainstream society, which has embraced the concept that certain characteristics -- race, gender, age, or zip code, for example -- should not matter when making decisions about things such as credit or insurance. But while an absence of bias makes sense on a human level, in the world of machine learning, it's a bit different. In machine learning theory, if you can mathematically prove you don't have any bias and if you find the optimal model, the value of the model actually diminishes because you will not be able to make generalizations. What this tells us is that, as unfortunate as it may sound, without any bias built into the model, you cannot learn. Modern businesses want to use machine learning and data mining to make decisions based on what their data tells them, but the very nature of that inquiry is discriminatory.

When searching for talent, sometimes the best person for the job is a machine. Robots make sense for repetitive and dangerous tasks, but they also work well as a check against bias. Artificial intelligence already outperforms judges in choices about setting bail because humans on the bench tend to overthink the defendants' demeanor, a poor predictor of flight risk. Likewise, hiring algorithms do better than recruiters at screening resumes because humans in HR show too much favoritism for traditional applicants. Unfortunately, smart technology also has blind spots.