Fairness in AI

Google has great resources for learning more about AI, both for developers and businesspeople: ai.google.

Through this site, I watched a lecture by Margaret Mitchell on fairness in AI. There are many stories about unintended bias in AI tools. A recent article about Amazon’s challenge with this made a lot of noise in the HR community. There are different types of human bias that can manifest in data:

  • Reporting bias: People report what they find interesting or notable so data doesn’t reflect real-world frequencies
  • Selection bias: The training data for machine learning systems is not a random sample of the world but instead things we find interesting
  • Overgeneralization: Conclusion is made based on limited information or information not specific enough
  • Out-group homogeneity bias: We assume people in groups we don’t interact with every day are more similar to each other than those in our in-group
  • Confirmation bias: Tendency to search for, interpret and favor information that confirms our own pre-existing beliefs and hypotheses
  • Automation bias: Preference for suggestions from systems, as if they are somehow more objective than other sources, like humans.

There are methods for designing in fairness in machine learning but this must be intentional – AI is not inherently unbiased.


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Intro to AI via cartoons

waitbutwhy.com always delivers with the thoughtful and funny

Kicking off this month with a primer on AI from waitbutwhy.com. This is from 2015, so there have probably been leaps in AI since this was written, but it is helpful as an anchor.

There are three types of AI:

  • Artificial Narrow Intelligence: We’re here now. AI that can reliably deliver better outcomes than a human brain, such as navigating with a map on your phone and getting customized music recommendations.
  • Artificial General Intelligence: This is AI that can reason and solve problems. We’re not here yet.
  • Artificial Superintelligence: This is intelligence that greatly exceeds human intelligence across all spectrums. This could be closer than we think.

This primer on AI goes into a lot of detail on the exponential increase of computing power over time as well.

When I think about AI’s applications for recruiting and HR, I think of many areas that artificial narrow intelligence is just starting to be applied. Recommendations based on past choices can be generated with existing data. And I also see the potential for recreating bias; this comes up over and over again as a concern with AI for recruiting. I’m curious to read more about how that can be addressed.

Welcome to AI February!

Having touched companies’ recruiting processes in some capacity the entirety of my career, I’ve been fascinated with the ways recruiting has evolved in that time. As a snapshot of this change, ten years ago as a new recruiter, I sometimes called an advertising agency to help post retail openings in the local newspaper. Today in recruiting operations, we use Google Analytics and tracking pixels to see performance of online postings, and programmatic advertising can automate some placement decisions.

One of the most exciting advancements in the field is artificial intelligence. Already there are HR tech startups touting their use of AI to enhance and improve the recruiting experience. The tagline is generally something about removing human bias and increasing efficiency – great arguments to move towards technology!

As a data analyst and a former recruiter, I am both excited and skeptical. Can these tools do what they promise? Do they truly apply AI or is this advanced statistics dressed up? And finally – aren’t people a critical component of the hiring practice?

To dive into some of these questions and familiarize myself with the state of AI, I’ve decided to commit my February to learning more about it. Every day throughout February, I will learn something about artificial intelligence and share it here. I’m focused on the following areas:

  • Defining artificial intelligence. What counts as artificial intelligence? What is the difference between AI and machine learning or are they interchangeable?
  • What’s the state of artificial intelligence in recruiting? Who is already doing this and how successful is it?
  • What are the promises and pitfalls of AI?

I realize that these three areas are rich enough that I could probably devote a month (or more!) to each. My intention here is to more broadly explore, though, so I’ll touch on all of these in one short month.

I’m looking forward to this month of learning and hope you follow along! Feel free to share any interesting tidbits, resources or your own interest with me here or via email or Twitter. Here’s to learning!