Difference between revisions of "Understanding Unanticipated, Social Consequences of Popular Algorithms"
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'''Description:''' Data-driven approaches have become common in technology and social computing to analyze and infer social behavior of people in large scale networks. However, data-driven algorithms are not impartial much as the popular narrative might imply. In this project, we will examine the unanticipated effects of popular data-driven algorithms on publicly available social and networked data. By doing this, we will learn how the biases and assumptions of data scientists, algorithm designers and data-driven decision makers affect the overall behavior of different algorithms leading to unforeseen, future consequences that affect the lives and behavior of the people being analyzed and examined. We will make inferences about society and technology at large from our results and prepare a poster submission for an appropriate conference (CSCW’18 or GROUP’18). The student will receive full credit and first authorship on this poster upon successful completion of the project. | '''Description:''' Data-driven approaches have become common in technology and social computing to analyze and infer social behavior of people in large scale networks. However, data-driven algorithms are not impartial much as the popular narrative might imply. In this project, we will examine the unanticipated effects of popular data-driven algorithms on publicly available social and networked data. By doing this, we will learn how the biases and assumptions of data scientists, algorithm designers and data-driven decision makers affect the overall behavior of different algorithms leading to unforeseen, future consequences that affect the lives and behavior of the people being analyzed and examined. We will make inferences about society and technology at large from our results and prepare a poster submission for an appropriate conference (CSCW’18 or GROUP’18). The student will receive full credit and first authorship on this poster upon successful completion of the project. | ||
Revision as of 02:02, 20 January 2017
Mentor: Dr. Shion Guha
Title: Understanding Unanticipated, Social Consequences of Popular Algorithms
Description: Data-driven approaches have become common in technology and social computing to analyze and infer social behavior of people in large scale networks. However, data-driven algorithms are not impartial much as the popular narrative might imply. In this project, we will examine the unanticipated effects of popular data-driven algorithms on publicly available social and networked data. By doing this, we will learn how the biases and assumptions of data scientists, algorithm designers and data-driven decision makers affect the overall behavior of different algorithms leading to unforeseen, future consequences that affect the lives and behavior of the people being analyzed and examined. We will make inferences about society and technology at large from our results and prepare a poster submission for an appropriate conference (CSCW’18 or GROUP’18). The student will receive full credit and first authorship on this poster upon successful completion of the project.
