2.3k citations · 2.3k across the 4 of their papers we have counts for
8 papers
Zero-shot causal learning
Hamed Nilforoshan, Michael Moor, Yusuf Roohani +5
Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online mark…
Human mobility networks reveal increased segregation in large cities
Hamed Nilforoshan, Wenli Looi, Emma Pierson +7
A long-standing expectation is that large, dense, and cosmopolitan areas support socioeconomic mixing and exposure between diverse individuals. It has been difficult to assess this…
Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann Gaebler, Ravi Shroff +1
Recent work highlights the role of causality in designing equitable decision-making algorithms. It is not immediately clear, however, how existing causal conceptions of fairness re…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
Hamed Nilforoshan, Neil Shah
Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity. This has spurred research in numerou…
The Measure and Mismeasure of Fairness
Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan +2
The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last decade, several formal, mathematical definitions of fairness have…