Feminism and HCI: New Perspectives

Interacting with Computers, 10/2011, Volume 23, Issue 5, p.iii-xi, 2011
Feminism and HCI: New Perspectives
Shaowen Bardzell, Elizabeth Churchill

As a word and as a set of theories and practices, feminism is a poorly understood concept. However, feminist perspectives have a lot in common with user- and value-centered design processes such as those espoused within the field of Human Computer Interaction.

Examples include consideration of alternative viewpoints, considerations of agency (who get to say/do what and under what circumstances) and the development of reflective and reflexive methods for understanding how, when, where and why people do what they do.

In the ''Feminism and HCI: New Perspectives'' special issue, we have invited researchers and practitioners to reflect on the ways in which feminist thinking, theory, and practice can and does have an impact on the field of Human Computer Interaction.

This introductory editorial offers more background to our view that there is great value to understanding the actual and potential impact of feminist thinking on HCI, followed by a precis of each paper. We close with some observations regarding common themes, points of contention and possibilities for future work.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.