More than 20 years ago, I embarked on what I believed would be a brief and concentrated academic career. As a young adult studying graphic design at Suffolk University, I myopically focused on developing the skills I needed to secure a job in the commercial arts. My methodology was to hone the skills that would give me tangible, immediate results from my education. That approach seemed validated when I graduated college and joined the advertising industry. Unbeknownst to me then, my academic career had only just started.
Over the next decade, I worked my way up to the title of Creative Director. My success, however, wasn’t attributed to an unmatched creative wit or uncanny ability to channel the social zeitgeist. I was successful because I applied analytics and critical thinking to my decisions. Unable to reconcile why my professional success was incongruent with the skillset I honed in college, I began thinking critically about myself and, in the process, realized that I still had much to learn. I discovered I had a penchant for other topics and began educating myself on statistics, philosophy, and behavioral economics. My approach to education had become invalidated.
I realized that I could go only so far with self-education and needed a proper academic program to help me learn what I didn’t yet know and expose what I didn’t know existed. Data Science, then called “Data Mining,” was a new and exciting field, requiring myriad skills that I knew I would ultimately need to be proficient in using data. At the time, very few online options were available, and the extended studies Data Science program at UC San Diego seemed by far the best. A borderline case, I was admitted into the program. Having not graduated from a STEM program, I probably had to work three times as hard compared to other students to understand and master the topics. But, I was instantly hooked. I learned that Data Science is about finding patterns in data that are so complex they can’t be perceived by the human eye. Seeing those patterns allows you to tell stories that nobody else can. That is, and forever will be, why I love data science.
Data Science is about finding patterns in data so complex they can’t be perceived by the human eye. Seeing those patterns allows you to tell stories nobody else can.
I tried to apply data science concepts to my professional career as fast as I learned them. Which is to say, I knew enough to be dangerous. Over the next decade, I encountered many data-related challenges for many businesses, and I eventually built a foundation in machine learning that I could apply to almost any situation. I guess you could say those experiences were like my personal training dataset. At some point in my ten-year career in data science, I started encountering situations that required ethical judgment calls and moral deliberation to complete the projects—some projects I didn’t complete simply because I could not reconcile the perceived ethical challenges. I found myself trying to navigate an area of data science I didn’t even know existed, and once again, I relied on self-guided education to fill in the blanks.
As AI became more prevalent, so too did the societal and ethical risks I encountered. Knowing that others in industry were likely dealing with similar challenges, I set out to create a standalone course, which I now teach at the Continuing Studies Program at Stanford University. The course, Ethical Data & AI, focuses on teaching students tools and frameworks to make responsible decisions in real-world situations. My goal for the course (and for myself) was to create a curriculum that teaches people how to find a balance between ethics and business progress. It’s what Aristotle referred to as “The Golden Mean,” the desirable middle between two extremes, one of excess and the other of deficiency. I believe what’s needed now are practitioners of AI ethics who not only understand the theoretical aspects of topics such as bias, accountability, and privacy but can also operationalize and enforce those concepts.
Inspired to learn more about the topic, I applied for the Artificial Intelligence Ethics MSc program at the University of Edinburgh, where I currently study. It was there that I found the education for which I was searching. Courses like Democracy, Rights and the Rule of Law in the Data-Driven Society; Algorithmic Bias, Fairness and Justice; and Ethics of Robotics and Autonomous Systems. My motivation, however, isn’t to buy an academic credential or improve the probability of success in my career. It’s the opportunity to go beyond satisfying my appreciation for ethics and declare my devotion to it—deeply, comprehensively, and dispassionately. Where this academic path will lead, I do not fully know. But as Robert Frost once wrote about divergent paths, “I took the one less traveled by, And that has made all the difference.”