ARTIFICIAL INTELLIGENCE

Forget Machine Learning:
Humans Still Have A Lot to Learn About Themselves, Part II

By February 12, 2019 No Comments
Forget Machine Learning: Humans Still A Lot to Learn About Themselves, II

Just like the new technologies and applications humans have created, humans themselves are far from perfect. Humans (often unwittingly) pass on their imperfections and biases to the algorithms that power Artificial Intelligence (AI) applications. This is known as AI bias, or machine learning bias. It occurs when an algorithm arrives at conclusions and/or generates results that have been influenced (prejudiced) by biased assumptions during the machine learning process. Many of these biases go undiscovered until the results they produce are witnessed and/or documented by their designers—or by the general public.

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Cognitive Biases: All Humans Have them, and they’re Influencing Machine Learning

Most people believe they make rational decisions, thinking through situations to make the best decision or reach a sound conclusion. And over the course of human evolution, cognitive biases have helped humans make quick decisions that have helped to keep the species safe—and alive. This split-second thinking helps in a number of ways, but biases also distort how humans think, and can lead to poorly-made decisions, too; biases are not so great for assessing situations or considering evidence. And it’s those same biases that become part of some AI algorithms, leading to flawed machine learning.

How to Prevent Machine Learning Bias

For machine learning to be a truly objective process, the data used to write the algorithms should not contain any biases; it should be influenced by random and/or complete data due to its pattern recognition capabilities. Ensuring biases do not influence machine learning is essential for a number of reasons—especially as machine learning becomes more ubiquitous in not only the business space, but in everyday life. These biases could mean the difference between a college student’s application being rejecting or accepted, correct or harmful data generated to medical professionals that drives care decisions, and misidentifying an innocent person who resembles a wanted criminal.

Not only is identifying one’s biases a challenge, but finding those biases before they influence algorithms is another. One way to guard against machine learning bias is to be on the lookout for it when choosing training data. Algorithms are exposed to innumerable pieces of data (or, huge data sets) and learn to identify (among other traits) similarities and differences in those data, ultimately making predictions based on data consumed.

These massive data sets contain variables that may or may not be important for arriving at an unbiased conclusion. When a business needs to assess dozens (or hundreds) of applicants’ resumes, for example, removing irrelevant variables such as age or gender helps ensure that the algorithm will consider all resumes based on relevant factors, like experience, skills, and any position-specific qualifications. However, factors that are included should not lead to either the algorithm or hiring manager learning the applicants’ age or gender.

But algorithms influencing job prospects isn’t merely hypothetical. Researchers at Carnegie Mellon University found that significantly more men than women were targeted and shown online advertisements guaranteeing assistance for finding jobs that paid more $200,000. In their experiment, 1000 simulated users (split between males and females) visited the top 100 employment websites. The result? Men’s profiles were more strongly associated with career coaching for positions receiving that level of income than women’s; male users were exposed to roughly 1800 ads, while female users saw around 300. Finding flaws like these are crucial for preventing and eliminating machine learning bias altogether, but as humans are most often blind to their biases, the path to more-objective machine learning may be one that winds rather than progresses linearly.

Machine Learning Brings Lots of Uncertainty.
Work with an Experienced Partner that Can Help You Navigate that Uncertainty.

Artificial Intelligence and machine learning receive a lot of media attention. But for as bright as that spotlight is, humans still don’t quite get AI. And little of that media attention highlights how far these buzzwords must advance before they are even capable of performing most human tasks—and “taking” jobs. AI and machine learning will undoubtedly become increasingly interwoven with humans’ day-to-day lives, but it’s still unclear as to what degree, and when.

No matter where your global expansion leads, it will likely become more reliant on new AI applications. But the uncertainty surrounding AI does not have to negatively influence your expansion plans. Velocity Global has helped hundreds of organizations reach their international growth goals in times of political and economic uncertainty, and we can help your business do the same. Our International PEO (Professional Employer Organization) solution can have you operating in virtually any country in as few as 48 hours—without setting up an entity.

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