The past decade has brought a sea change in the availability of data. Instead of a world in which we have small number of carefully curated data sources — instead we have a plethora of datasets, data versions, and data representations . Devices and data acquisition tools make it easy Read more…
Problems involving learning from ranking and choice data arise in several domains, ranging from sports rankings to elections and from recommender systems to marketing. Indeed, algorithms for these problems have been developed in many different fields, ranging from statistics to computer science and from social choice to marketing. Unfortunately, however, Read more…
The overarching goal of the DELPHI project is to simplify the development of correct and reliable software. Existing methodologies require significant human expertise, such as in the design of appropriate abstract domains and environment models, or suffer from significant barriers to adoption, such as limited scalability and flexibility. This project Read more…
Important decisions such as police deployment, loan approvals, hiring, and parole from incarceration are beginning to be made by machine learning algorithms. This has lent urgency to the question of whether these algorithms are fair. A recent well-publicized analysis of recidivism risk predictions studied NorthPointe’s proprietary COMPAS algorithm used in Read more…
The World Well-Being Project (WWBP) is pioneering scientific techniques for measuring psychological well-being and physical health based on the analysis of language in social media. As a collaboration between computer scientists, psychologists, and medical researchers, we are shedding new light on the psychosocial processes that affect health and happiness and Read more…