Ideas Made to Matter
Exploration-based algorithms boost hiring diversity
A new approach analyzed resumes for a Fortune 500 firm — and bested traditional screening practices.
Faculty
Danielle Li
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About
Danielle Li is the Class of 1922 Career Development Professor and an Associate Professor at the MIT Sloan School of Management. Her research interests are in the economics of innovation, productivity, and organizational economics.
Her work focuses on 1) understanding how organizations weigh expert advice and quantitative data when making investment decisions and 2) assessing the value of such investments on innovation in the life sciences in particular. For example, a recent project studies how machine learning algorithms impact firm HR practices and worker productivity; another examines whether/how public investments in biomedical research shape private sector pharmaceutical development.
Danielle is also a Faculty Research Fellow at the National Bureau of Economic Research. Prior to coming to MIT, she was an assistant professor of entrepreneurship at Harvard Business School.
She holds an AB in mathematics from Harvard College and PhD in economics from MIT.
Honors
Publications
"Internal Deadlines, Drug Approvals, and Safety Problems."
Cohen, Lauren, Umit G. Gurun, and Danielle Li. American Economic Review: Insights. Forthcoming.
"Trust, Collaboration, and Economic Growth."
Kondo, Jiro, Danielle Li, and Dimitris Papanikolaou. Management Science. Forthcoming.
"Insurance Design and Pharmaceutical Innovation."
Li, Danielle, Leila Agha, and Soomi Kim, Working Paper. 2020.
"Hiring as Exploration."
Li, Danielle, Lindsey Raymond, and Peter Bergman, Working Paper. 2020.
"Missing Novelty in Drug Development."
Krieger, Josh, Danielle Li, and Dimitris Papanikolaou, MIT Sloan Working Paper 5622-19. Cambridge, MA: MIT Sloan School of Management, January 2019.
"Public R&D Investments and Private Sector Patenting: Evidence from NIH Funding Rules."
Azoulay, Pierre, Joshua S. Graff Zivin, Danielle Li, and Bhaven N. Sampat. Review of Economic Studies Vol. 86, No. 1 (2019): 117-152. Also NBER Working Paper #20889.
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Media Highlights
Source: Fast Company (Opinion Piece)
Design of hiring algorithms can double diversity in firms
"...an ongoing concern is the potential for automated approaches to codify existing human biases..."
Faculty:
Danielle Li
Source: Axios
When robots are recruiters
Using AI to recruit isn't inherently bad, says Danielle Li...It's about using the right kind of algorithm.
Faculty:
Danielle Li
