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Organizational knowledge bases are moving from passive archives to activeentities in the flow of people's work. We are seeing machine learning used toenable systems that both collect and surface information as people are working,making it possible to bring out connections between people and content thatwere previously much less visible in order to automatically identify andhighlight experts on a given topic. When these knowledge bases begin toactively bring attention to people and the content they work on, especially asthat work is still ongoing, we run into important challenges at theintersection of work and the social. While such systems have the potential tomake certain parts of people's work more productive or enjoyable, they may alsointroduce new workloads, for instance by putting people in the role of expertsfor others to reach out to. And these knowledge bases can also have profoundsocial consequences by changing what parts of work are visible and, therefore,acknowledged. We pose a number of open questions that warrant attention andengagement across industry and academia. Addressing these questions is anessential step in ensuring that the future of work becomes a good future forthose doing the work. With this position paper, we wish to enter into thecross-disciplinary discussion we believe is required to tackle the challenge ofdeveloping recommender systems that respect social values.

Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems