Professor Yunsieg Kim presented his new paper, “Artificial Sameness,” at the inaugural AI for Law Scholars Conference at Northwestern University Pritzker School of Law. Professor Kim co-authored the paper with Jacob Eisler of Florida State University College of Law.
The paper examines a less-discussed risk of artificial intelligence: its potential to homogenize even as it promises personalization. Kim and Eisler argue that debates over AI have largely focused on bias and disparate treatment while overlooking “substantive convergence beneath surface-level variation.”
They describe this phenomenon as “artificial sameness,” or the systematic convergence of AI-generated outputs, reasoning, and, ultimately, human thought toward common patterns. The paper argues that existing legal tools designed to identify algorithmic bias may fail to recognize this form of homogenization because they focus on unequal treatment rather than the compression of differences.
Profs. Kim and Eisler also explore the broader implications for institutions that depend on diverse perspectives and independent judgment, including the marketplace of ideas, the laboratories of democracy, and the adversary legal system.
