Built on the success of the past five years, KDD DSHealth 2023 will further catalyze the development of links between academic and industrial data science groups. The workshop aims to stimulate discussion on strategic areas for development and to facilitate future cross-disciplinary collaborations. In accordance with the multi-year goal to continue fostering this community via timely topics, this year the workshop will focus on the applications and new development of generative models in healthcare, including the new development and application of LLMs. The workshop invites full papers, as well as work-in-progress on the application of data science in healthcare. The workshop will feature two invited talks from eminent speakers, spanning academia, industry, clinical researchers, and governmental regulatory bodies. In addition, we will invite community members to submit their research works and bring them for discussion. The summary gives a brief description of the half-day workshop to be held on August 7th, 2023.

KDD DSHealth 2022, aims to build on the success of the past four years to further catalyze the development of links between academic and commercial data science groups and the rapidly developing translational medicine informatics community. The workshop will stimulate discussion as to strategic areas for development and will lead to future cross-disciplinary collaborations. In accordance with the multi-year goal to continue fostering this community as a series of KDD workshops via timely topics, this year the workshop will focus on the transparency and human-centered AI in healthcare. The workshop invites full papers, as well as work-in-progress on the application of data science in healthcare. The workshop will feature four invited talks from eminent speakers, spanning academia, industry, clinical researchers, and governmental regulatory bodies. In addition, selected papers will be invited to publish in a special issue of Journal of Healthcare Informatics Research. The summary gives a brief description of the full-day workshop to be held on August 14th, 2022.

Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the development of quantitative metrics to measure algorithm disparities across different protected groups, and approaches for adjusting the algorithm output to reduce such disparities. In this paper, we propose to study the problem of identification of the source of model disparities. Unlike existing interpretation methods which typically learn feature importance, we consider the causal relationships among feature variables and propose a novel framework to decompose the disparity into the sum of contributions from fairness-aware causal paths, which are paths linking the sensitive attribute and the final predictions, on the graph. We also consider the scenario when the directions on certain edges within those paths cannot be determined. Our framework is also model agnostic and applicable to a variety of quantitative disparity measures. Empirical evaluations on both synthetic and real-world data sets are provided to show that our method can provide precise and comprehensive explanations to the model disparities.

KDD Health Day/DSHealth 2021, aims to build on the success of the past 3 years to further catalyze the development of links between academic and commercial data science groups and the rapidly developing translational medicine informatics community. The workshop will stimulate discussion as to strategic areas for development and will lead to future cross-disciplinary collaborations. In accordance with the multi-year goal to continue fostering this community as a series of KDD workshops via timely topics, this year the workshop will focus on the state of explainability and trustworthiness in healthcare. The workshop invites full papers, as well as work-in-progress on the application of data science in healthcare. The workshop will feature 8 invited talks from eminent speakers across academia, industry, clinical researchers, and governmental regulatory bodies. In addition, selected papers will be invited to publish in a special issue of Artificial Intelligence in Medicine journal. The summary gives a brief description of the full-day workshop to be held on August, 2021 virtually.

While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender.There is also a growing need to lexically represent this variety of gender identities.In our previous work, we developed a set of tools and approaches for analyzing Twitter data as a basis for generating hypotheses on language used to identify gender and discuss genderrelated issues across geographic regions and population groups in the U.S.A.In this paper we analyze the coverage and relative frequency of the word forms in our Twitter analysis with respect to the National Transgender Discrimination Survey data set, one of the most comprehensive data sets on transgender, gender non-conforming, and gender variant people in the U.S.A.We then analyze the coverage of WordNet, a widely used lexical database, with respect to these identities and discuss some key considerations and next steps for adding gender identity words and their meanings to WordNet.