JDS is committed to the promotion of diversity and inclusion in all aspects of our professional activities. We celebrate the diversity in our community and welcome everyone regardless of age, gender identity, race, ethnicity, socioeconomic background, country of origin, religion, sexual orientation, physical ability, education, and work experience. We also welcome people and opinions of all political persuasions, as long as they abide by the ACM policy against harassment.
As we pursue more initiatives, we may have some missteps. We value your feedback and ideas to help us all build a healthier and more welcoming community.
Diversity is achieved when the individuals around the table are drawn from a variety of backgrounds and experience, leading to a breadth of viewpoints, reasoning, and approaches (also referred to as "the who"). Inclusion is achieved when the environment is characterized by behaviors that welcome and embrace diversity ("the how"). Both are important in our writing and other forms of communication such as posters and talks.
Be mindful of not using language or examples that further the marginalization, stereotyping, or erasure of any group of people, especially historically marginalized and/or under-represented groups (URGs) in computing. Of course, exclusionary or indifferent treatment can arise unintentionally. Be vigilant and actively guard against such issues in your writing. Reviewers will also be empowered to monitor and demand changes if such issues arise in your submissions. Here are some examples of such issues for your benefit:
Going further, please also consider actively raising the representation of URGs in your writing. Diversity of representation helps create an environment and community culture that could ultimately make our field more welcoming and attractive to people from URGs. This is a small but crucial step you can take towards celebrating and improving our community’s diversity.
Finally, if your work involves data-driven techniques that make decisions about people, please consider explicitly discussing whether it may lead to disparate impact on different groups, especially URGs. Consider discussing the ethical and societal implications. For example, see this article discussing the potential for disparate impact of facial recognition in healthcare and strategies to avoid or reduce harm. This SIGMOD Blog article also gives a comprehensive overview of various dimensions and approaches for responsible application of data management ideas. We hope our community can help permeate this culture of responsibility and awareness about potentially harmful unintended negative consequences of our work within the larger computing landscape.
The content here builds on the instructions created by Avrilia Floratou and Arun Kumar for ACM SIGMOD 2021.