ReEnTrust Student Launches a Study on Fairness Perception of a Job Search Platform

A ReEnTrust student is conducting a research study about how explanations can help improve fairness and transparency in job sourcing platforms from the viewpoint of the applicant. For the past couple of decades, there has been a lot of discussion about trying to find the balance between transparency and accuracy in recommender systems. Due to the significance of e-recruitment systems, some papers suggest we need to sacrifice accuracy to ensure full understanding of how the algorithm produces recommendations. The belief is…

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ReEnTrust launches a new online user study about trust and e-recruitment system

We are pleased to announce a new online study by the ReEnTrust project team, aimed to analyse how an understanding of algorithms can help people feel more confident about using them. The study should take around 60 minutes to be completed. It is aimed to collect qualitative and quantitative data to help us gain a better understanding of how explanation and transparency of online job searching would impact users’ trust. The results of the experiment are aimed to be used in…

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Landscape summary for CDEI: bias in algorithmic decision-making

Publication of the landscape summary on “Bias in Algorithmic Decision-Making”, commissioned by the Centre for Data Ethics and Innovation (CDEI) and produced by our own Michael Rovatsos, with contributions from Ansgar Koene and Brent Mittelstadt. The landscape summary report forms part of the CDEI’s reviews into online targeting and bias in algorithmic decision-making. These landscape summaries have informed the development of CDEI interim reports and will inform their ongoing work on the two reviews. This Landscape Summary draws together the…

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