Socio-Technical Futures Lab – Recommendation Systems and Data

Summary

Emerging technologies such as machine vision, AR/VR, AI and recommendation systems raise profound questions about the relationship between technology and society, and how these technologies are becoming differentially integrated into everyday life in Australia and elsewhere. Yet the invention, design, implementation, and use of technology proceeds without such knowledge. 

The Socio-Tech Futures Lab (STuF Lab) has been established in the Department of Media and Communication to examine the ways in which social, cultural, and political dynamics influence the integration of technologies into everyday life, and the implications of such forces in shaping and designing our futures. See - STuF Lab

Led by Professor Heather Horst, Professor Gerard Goggin and Dr Marcus Carter, the STuF Lab is seeking PhD students interested in bringing humanities and social science research to the table with other disciplines, community, industry and policy actors in the study of emerging digital technologies. 

The exemplary PhD candidate is not expected to have pre-existing practical or high-level technical literacy about these emerging technologies. They will be supervised by a multidisciplinary team of senior researchers, incorporating the knowledge, industry experience and technical expertise available at The University of Sydney in their chosen research area.
 
We are currently recruiting PhD students interested in conducting projects on AI, Machine Vision, AR/VR, and on Recommendation Systems.


Supervisor(s)

Professor Heather Horst, Professor Gerard Goggin, Dr Marcus Carter

Research Location

Department of Media and Communications, School of Letters, Art and Media (SLAM)

Program Type

PHD

Synopsis

Recommendation systems seek to filter and parse the overwhelming amount of digital data available to users online. They are how websites decide what YouTube video to recommend you watch next, to how they choose which potential romantic partner you’re shown on Tinder. These systems use algorithms drawing on past user-behaviour to support users, but in turn shape user behaviour, creating unknown feedback loops and otherwise unlikely social and political contexts (Ananny, 2015; Mittelstadt, 2016; Paraschakis, 2017; Noble, 2018). Increasingly, recommendation systems are drawing on wider and wider sets of data to inform their decision making, in ways that are less and less knowable and visible to users (Diresta, 2018). This project will explore the social and political impacts and implications of recommendation systems, such as in news sharing or on YouTube, and/or our day-to-day relationships with data, and work towards understanding how policy and design can ensure they are more ethical and responsible. 

The specific nature of this project will be developed in consultation with the prospective PhD student and identified supervisor(s). On these topics, you will be working alongside other researchers and post-doctoral researchers interested in these fields of enquiries and advancing knowledge in your own right. Prospective candidates are not required to have advanced technical literacy in their chosen topic area, although – depending upon the project - an openness to learning these is always welcome. 

Additional Information

  • This is not a funded position, although opportunities for research assistance work and travel funding may be available through association with the STuF Lab. The applicant is responsible for obtaining a stipend.  
  • Initial Inquiries should be sent by email to Professor Heather Horst and should be include a copy of the applicant’s CV and a 500 – 1,000 word project proposal on one of the suggested topics. 
  • Depending on the research project proposal, we will connect applicants to potential supervisors from the Department of Media and Communications and across the University of Sydney.
References: Paraschakis, D. (2017) 'Towards an ethical recommendation framework'. In Research Challenges in Information Science (RCIS), 2017 11th International Conference on (pp. 211-220). IEEE. Noble, S.U. (2018) 'Algorithms of oppression: How search engines reinforce racism'  New. York: NYU Press. ISBN 9781479837243

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Keywords

Recommendation systems, algorithms, social and political impacts, AI, artificial intelligence, emerging digital technologies, technology and society, Socio-Tech Futures Lab, STuF Lab, Department of Media and Communication, future

Opportunity ID

The opportunity ID for this research opportunity is: 2626

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