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

arXiv: https://arxiv.org/abs/2209.03819 · HTML: https://mlsanity.com/paper/2209.03819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Evaluating Stochastic Rankings with Expected Exposure](https://mlsanity.com/paper/2004.13157/) (2020)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned](https://mlsanity.com/paper/1809.06481/) (2018)
- [Exposing Query Identification for Search Transparency](https://mlsanity.com/paper/2110.07701/) (2021)

## Abstract (arXiv)

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