Don’t try to fit a mold. This will be challenging sometimes, but ultimately rewarding.”
Emilio Lehoucq (he/him)
PhD Candidate in the Department of Sociology and Master’s Candidate in the Department of Statistics
Emilio Lehoucq is a PhD candidate in the Department of Sociology in the Weinberg College of Arts and Sciences. He also is pursuing a master’s degree in the Department of Statistics. Emilio conducts research on our data-driven society.
How would you describe your research and/or work to a non-academic audience?
I seek to understand the social dynamics that sustain data-driven problem-solving. Three questions that guide my work are: What enabled the rise of machine learning? What factors account for beliefs about algorithmic fairness? What is driving the increasing demand for privacy? I bring together sociological and statistical approaches to answer these questions.
What have been some of the most memorable twists and turns of your career?
Starting my career in law, I didn’t expect to end up working with data. However, what excited me was understanding the social life of the law. Legal scholars’ responses were mostly theoretical, and I wanted empirical answers. This drove me to Northwestern, where I encountered machine learning. I fell in love with machine learning’s potential to solve important social problems and pivoted my career.
Tell us what inspired your research and/or work.
A passion for data-driven research drove me from law school in Bogota, Colombia, to a PhD in sociology and a master’s degree in statistics at Northwestern. As I was acquiring skills in quantitative and qualitative methods, the growing use of data to solve social problems fascinated me. Ultimately, I decided to contribute to this trend through my work.
What is the biggest potential impact or implication of your work?
Since around 2016, the social perception of technology has been changing, partly due to famous scandals such as Cambridge Analytica. The development of technologies like machine learning is increasingly dependent on the demand for algorithmic fairness and privacy. By studying the social roots of this demand, I can potentially help shape the future of data-driven problem-solving.
What books are on your bedside table?
These days, I am reading a lot about the histories of technology companies. Currently, the book on my bedside table is We Are the Nerds: The Birth and Tumultuous Life of Reddit, the Internet’s Culture Laboratory by Christine Lagorio-Chafkin. Some of my other recent reads are the histories of IBM and Coinbase.
What inspires you?
My inspiration comes from seeing how data-based technologies can help solve some of the world’s most important and pressing problems. I am concerned by the unethical use of such technologies but believe in a future of responsible data-driven problem-solving. This long-term vision is what motivates me.
What advice would you give your younger self or someone considering a similar path?
Don’t try to fit a mold. This will be challenging sometimes, but ultimately rewarding. Having a broad range helps you solve problems from different perspectives, avoid biases, and be more creative.
Tell us about a current achievement or something you're working on that excites you.
Recently, the Journal of Data Science published one of my articles on Americans’ perceptions about the fairness of predictive automation. I’m excited to build on this research through a collaborative survey experiment on beliefs about the fairness of pretrial risk assessment tools. Wrapping up my dissertation on the socio-economic dynamics behind the rise of machine learning and publishing some of that research is also a lot of fun these days.
Published: January 10, 2023
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