Bias in Data and Algorithmic Systems: Problems, Solutions and Stakeholders by Dr Jahna Otterbacher
Start time: 14:00 / End time: 14:45 / Date: 28 Oct 2020 / Venue: Online
Open to: Students in host dept/school/institute/centre / Staff in host dept/school/institute/centre / Students from same Faculty as host dept/school/institute/centre / Staff from same Faculty as host dept/school/institute/centre / Students within this Faculty / Staff within this Faculty / Any UOL students / Any UOL staff / Students from other HEIs / Staff from other HEIs/research institutions / Any potential undergraduate students / Any potential postgraduate students / Any potential international students / University of Liverpool Alumni / Business/industry / General Public
Contact: For more information contact Andrea Bainbridge at Andrea.Bainbridge@liverpool.ac.uk
About the event
WiT Lecture Series
Online Webinar: “Bias in Data and Algorithmic Systems: Problems, Solutions and Stakeholders”
This is a live online webinar hosted by University of Liverpool, School of Electrical Engineering, Electronics and Computer Science, as part of the WiT Lecture series. The talk will be followed by a Q/A session.
Guest Speaker: Dr Jahna Otterbacher
From the faculty of the Open University of Cyprus (OUC), School of Pure and Applied Sciences.
Mitigating bias in algorithmic processes and systems is a critical issue drawing increasing attention across research communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders – not only developers, but also end-users and third parties – there is a need to understand the landscape of the sources of bias, as well as the solutions being proposed to address them. In this talk, I present insights from a recent survey of 250+ articles across four domains (machine learning, information retrieval, HCI, and RecSys), providing a “fish-eye view” of the field. In the second part of the talk, I will discuss examples of our previous work on auditing proprietary computer vision systems for social biases, positioning this work vis-à-vis the aforementioned framework as well as the emerging science of machine behavior.
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