Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally?

Exploiting Multiple Existing Models and Learning Algorithms

This paper presents MMM and MMC, two methods for combining knowledge from a variety of prediction models. Some of these models may have been created by hand while others may be the result of empirical learning over an available set of data. The approach consists of learning a set of \Referees", one for each prediction model, that characterize the situations in which each of the models is able t...

Machine Learning Algorithms for In-Database Analytics

Our project focused on extending the functionality of MADlib. MADlib is an open source machine learning and statistics library which works with Postgres or Greenplum to provide in-database analytics. Although some machine learning algorithms have been implemented in MADlib, there is room for additional contributions. We have implemented two different machine learning algorithms, symbolic regres...

Who do I serve?

COMMENTARY This talk is inspired by the story of Matthew about whom you will hear in a few moments. This ceremony acknowledges a transition in your medical education: A rite of passage from the study of basic medical science in classrooms to new encounters with patients at bedsides and in clinics. On choosing and being chosen You are an exceptional group of women and men. In the style of praise...

How Long Do They Serve?

Occupy Algorithms: Will Algorithms Serve the 99%?

This short note is a response to Frank Pasquale’s article “The Emperor’s New Codes: Reputation and Search Algorithms in the Finance Sector” published as part of the conference Governing Algorithms1, New York, 2013. Pasquale describes how algorithms in the finance industry have failed to deliver on the promise of greater transparency, accountability, fairness, and efficiency. We discuss some of ...