Eric Sydell - Decoding Talent: How AI and Big Data Can Solve Your Companys People Puzzle

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ACKNOWLEDGMENTS

This book has been a team effort from the beginning. At Modern Hire, we thank Jen Wason for her phenomenal editing skills (and patience), Tiana Rownd for keeping the wheels on the bus, and Erin Iafelice for her sage counsel. We also thank Jay Miller for getting the project off the ground and guiding it in the early stages, Peter Baskin and Kim Macdonald for their steerage and advice, and Jennifer Hurd Studebaker for her thorough research support.

Of course, we could not have written Decoding Talent without substantial input from our clients and professional colleagues. We thank all those who sacrificed free time between their pandemic video meetings to chat with us about the future of talent management and hiring.

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INTRODUCTION

Subsumed as they are by the hustle and bustle of our daily lives, all the seismic technological changes afoot can be hard to see. But since the advent of the artificial intelligence (AI) technique called deep learning near the start of this millennium, machines have been developing truly stunning abilities to make sense of our world. Deep learning is essentially a data analysis technique, a paradigm-shifting statistical tool allowing us to process and make sense of all kinds of data faster and more thoroughly. Some of the amazing (and entertaining) things deep learning has produced include:

Cars that can nearly drive themselves

Virtual assistants that understand increasingly more of what we ask them

Software that can read medical scans as well as or more accurately than trained physicians

A stunningly de-aged Robert De Niro in the movie The Irishman

Illicit websites that can instantly write a term paper for you

Multiple vaccines for COVID-19 in less than a year, when the previous world record was four years for the mumps vaccine

But while this technology is amazing and sometimes frightening, tremendous structural barriers come with trying to roll it out quickly and widely. These include the following:

Big data is required but often hard to obtain. Tremendous amounts of data are recorded and stored, but the data is not always high quality.

Laws, regulations, and guidelines are woefully slow to evolve. It takes time for lawmakers to reach consensus on how to handle new technologies, but AI is advancing at a rate far beyond our ability to civilize it.

Access to data is often restricted, intentionally or unintentionally. Many times, organizations house data for safekeeping without any reasonable way to access and analyze it. They store the data, but we cant see the data. Its sort of like keeping your fine jewelry in a safe deposit boxa good way to keep it safe but not very convenient to access.

Concerns related to privacy and bias, while addressable, lead to confusion and loss of momentum. It is a massive under-statement to say that AI is powerful, and with that power comes great potential for benefit and harm.

Do these challenges mean we should oppose AI and work to limit its adoption? We dont think so, because it would cede technological leadership to those who do not have such scruples (and there are many). Instead, we elect to take a strong stance on AI: It should be harnessed for the benefit of humanity. This principle means individuals should benefit, not just corporate entities. If AI is used to continually make organizations more efficient, it will eventually do so at the expense of the people who make up organizations. It will ultimately dehumanize us.

AI does not cause biasit reveals it. Its the humans using AI incorrectly who cause or exacerbate existing biases. In many ways we are entering a golden age of understanding and identifying bias. Big data and powerful algorithms are uncovering it in places we never knew it existed. As difficult as it might be to face, AI being able to locate more bias in all the different places it lurks is a positive developmentbecause it also comes with the knowledge to see bias and address it.

Many AI vendors talk about trusted AI and bias-free AI, but these phrases involve some wishful thinking. Any tool that is in any way opaque in how it operateswhich a lot of AI technology iscan never be fully trusted or left to its own devices. Nor can we ever assume it is bias-free, even if an analysis at one point showed it was. As more data arrives and algorithms evolve, bias will be reintroduced. The solution is to continually monitor the structure and functioning of AI and, when bias is found, to fix it.

Even so, the biggest barrier to the realization of the potential of deep learning is us. We humans struggle to envision and comprehend possible futures, especially ones involving complex advanced technologies. We must see concrete examples and results for those futures to feel real and immediate to us. And we are fearfulfearful of the unknown, fearful of change. Of course, this natural intransigence allows us the time to civilize and tame new technologies, thus limiting the potential for negative and often unforeseen downsides. But it does so only as a