e trade-o between relevance and fairness in personalized recommendations has been explored in recent works, with the goal of minimizing learned discrimination towards certain demographics while still producing relevant results.We present a fairness-aware variation of the Maximal Marginal Relevance (MMR) re-ranking method which uses representations of demographic groups computed using a labeled dataset. is method is intended to incorporate fairness with respect to these demographic groups.We perform an experiment on a stock photo dataset and examine the trade-o between relevance and fairness against a well known baseline, MMR, by using human judgment to examine the results of the re-ranking when using di erent fractions of a labeled dataset, and by performing a quantitative analysis on the ranked results of a set of query images. We show that our proposed method can incorporate fairness in the ranked results while obtaining higher precision than the baseline, while our case study shows that even a limited amount of labeled data can be used to compute the representations to obtain fairness. is method can be used as a post-processing step for recommender systems and search.

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The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.