Article
Regulating Machine Learning The Challenge of Heterogeneity
TechReg Chronicle
(2023)
Abstract
Machine-learning algorithms increasingly drive technological advances that deliver valuable improvements for society
and the economy. But these algorithms also raise important concerns. The way machine-learning algorithms work au-
tonomously to find patterns in large datasets has given rise to fears of a world that will ultimately cede critical aspects
of human control to the dictates of artificial intelligence. These fears seem only exacerbated by the intrinsic opac-
ity surrounding how machine-learning algorithms achieve their results. To a greater degree than with other statistical
tools, the outcomes generated by machine learning cannot be easily interpreted and explained, which can make it hard
for the public to trust the fairness of products or processes powered by these algorithms.
For these reasons, the autonomous and opaque qualities of machine-learning algorithms make these digital tools
both distinctive and a matter of public concern. But when it comes toregulatingmachine learning, a different quality
of these algorithms matters most of all: their heterogeneity. The Merriam-Webster Dictionary defines “heterogeneity” as
“the quality or state of consisting of dissimilar or diverse elements.” Machine learning algorithms’ heterogeneity will
make all the difference in deciding when to regulate them, who should regulate them, and how to design regulations
imposed on their development and use.
Keywords
- Artificial intelligence,
- machine learning,
- data science,
- analytics,
- big data,
- algorithms,
- rules and standards,
- government regulation,
- regulatory instruments,
- administrative agencies,
- agency capacity,
- public administration,
- expertise,
- heterogeneity
Disciplines
Publication Date
February 1, 2023
Citation Information
Cary Coglianese. "Regulating Machine Learning The Challenge of Heterogeneity" TechReg Chronicle (2023) Available at: http://works.bepress.com/cary-coglianese/192/