It’s an increasingly common experience among data scientists - you’re minding your own business, getting your job done, and your boss’s boss walks in to your office or drops in to your Slack DMs. “Do we have AI in the product? What’s your plan for making our product AI powered?” (That’s if you don’t just get ambushed with this question in a big meeting in front of a bunch of executives.

Do we still care about how our machine learning does what it does? Today I want to get a bit philosophical and talk about how explainability and risk intersect in machine learning. What do we mean by Explainability? In short, explainability in machine learning is the idea that you could explain to a human user (not necessarily a technically savvy one) how a model is making its decisions. A decision tree is an example of an easily explainable (sometimes called “white box”) model, where you can point to “The model divides the data between houses whose acreage is more than one or less than or equal to one” and so on.

The digital world can’t exist without the natural resources to run it. What are the costs of the tech we’re using to build and run AI? There’s a core concept in machine learning that I often tell laypeople about to help clarify the philosophy behind what I do. That concept is the idea that the world changes around every machine learning model, often because of the model, so the world the model is trying to emulate and predict is always in the past, never the present or the future.

Musings on whether the “AI Revolution” is more like the printing press or crypto. (Spoiler: it’s neither.) I am not nearly the first person to sit down and really think about what the advent of AI means for our world, but it’s a question that I still find being asked and talked about. However, I think most of these conversations seem to miss key factors. Before I begin, let me give you three anecdotes that illustrate different aspects of this issue that have shaped my thinking lately.

Courts are preparing to decide whether generative AI violates copyright—let’s talk about what that really means Copyright law in America is a complicated thing. Those of us who are not lawyers understandably find it difficult to suss out what it really means, and what it does and doesn’t protect. Data scientists don’t spend a lot of time thinking about copyright, unless we’re choosing a license for our open source projects.

Thinking about the intersection of people and technology in the creative process in the AI era Understanding art is challenging for lots of people, and it can often seem inaccessible. However, I have long been a lover of art (to the point where I almost majored in Art History in college) and eagerly seek out art to better understand human conditions past and present. As a result, bringing people to art and art to people is important to me.

What would you say it is you do here? Now that many of us are returning to the office and getting back into the swing after a winter break, I have been thinking a bit about the relationship between machine learning functions and the rest of the business. I have been getting settled in my new role at DataGrail since November, and it has reminded me how much it matters for machine learning roles to know what the business is actually doing and what they need.

On the use of machine learning in healthcare and the United Healthcare AI scandal Some of you may know that I am a sociologist by training — to be exact, I studied medical sociology in graduate school. This means I focused on how people and groups interact with illness, medicine, healthcare institutions, and concepts and ideas around health.* I taught undergraduates going into healthcare fields about these issues while I was an adjunct professor, and I think it’s really important for people who become our healthcare providers to have insight into the ways our social, economic, and racial statuses interact with our health.

On deepfakes, authenticity, and the President’s Executive Order on AI One of the many interesting ethical issues that comes with the advances of generative AI is detection of the product of models . It’s a practical issue as well, for those of us who consume media. Is this thing I am reading or looking at the product of a person’s thoughtful work, or just words or images probabilistically generated to appeal to me?

Much of the division between technology and human activity is artificial — how do people make our work possible? We don’t talk enough about how much manual, human work we rely upon to make the exciting advances in ML possible. The truth is, the division between technology and human activity is artificial. All the inputs that make models are the result of human effort, and all the outputs in one way or another exist to have an impact on people.