Project Description

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RESEARCH AREAS:

  • Critical Data Studies

  • Fairness, Accountability, Transparency, & Ethics (FATE) of AI

  • Science & Technology Studies

CONTACT:

PINAR BARLAS

Doctoral Student; 
Faculty of Media & Information Studies, Western University

Pınar (they/she) is a Library and Information Science Ph.D. candidate in the Faculty for Information and Media Sciences at Western University. Pınar’s doctoral work investigates how human values become embedded in machine learning applications by studying how practitioners construct and clean “ground truth” data. Pınar approaches the exclusion and elimination of data points via “data cleaning” as discard practices, which are techniques that establish and maintain power systems. Pınar’s overall research goal is to identify points of interruption and intervention so that data cleaning in practice can be improved, reducing harms to marginalized communities. 

Previously, Pınar was a Research Intern at Microsoft (FATE Montreal) and a Research Associate at the CYENS Centre of Excellence (Fairness and Ethics in AI-Human Interaction Multidisciplinary Research Group). Pınar has a BA in Cultural Studies & Communication, an MA in Interaction Design, and brief experience in the industry as a User Experience and Service Designer. 

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Barlas, P. (2025). Data Cleaning, Discard Studies, and Discretionary Power. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES). [doi] 

Barlas, P., Krahn, M., Kleanthous, S., Kyriakou, K., & Otterbacher, J. (2022). Shifting Our Awareness, Taking Back Tags: Temporal Changes in Computer Vision Services’ Social Behaviors. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM). [doi]  

Barlas, P., Kyriakou, K., Kleanthous, S., & Otterbacher, J. (2021). Person, Human, Neither: The Dehumanization Potential of Automated Image Tagging. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES). [doi] 

Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., & Otterbacher, J. (2020). To “See” is to Stereotype: Image Tagging Algorithms, Gender Recognition, and the Accuracy–Fairness Trade-off. Proceedings of the ACM on Human-Computer Interaction (V4, CSCW3, #232). [doi]