KDD 2021 EDI Statement
KDD 2021 EDI Statement
Diversity and Inclusion in KDD
The KDD community 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 . Please follow this webpage for updates on the steps we are taking to enhance the inclusivity and diversity of the KDD community. 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.
If you have any comments, questions, suggestions, or complaints, please email the 2021 ACM SIGKDD Equity, Inclusion, and Diversity Chairs: Lee Mong Li () and Johannes Gehrke ().
INCLUSION AND DIVERSITY IN WRITING
As a large scientific and technical community that has a direct impact on many people from different backgrounds around the world, Diversity and Inclusion are crucial for the data management community. ACM explains these goals as follows. 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 ariseunintentionally. 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:
Examples of exclusionary and other non-inclusive writing to consider avoiding:
- Implicit assumption: An example of dataset characteristics: “Every person has a mother and a father.” This example is exclusionary and potentially hurtful to people from single-parent households and people with same-sex parents.
- Oppressive terminology: Using the term “Master-Slave” to describe a distributed data system architecture. This can be hurtful to people whose families have suffered the inhumanity of enslavement. A good source of alternative terms to oppressive language often used in computer science can be found in .
- Marginalization of URGs: An example of attribute domains: “The Gender attribute is either Male or Female.” This example is exclusionary and potentially hurtful to people who are intersex, transgender, third gender, two-spirit, agender, or have other non-binary gender identities.
- Lack of accessibility: Using color alone to convey information in a plot when good alternative data visualization schemes exist. This can be exclusionary to people who are color-blind. Please consider using patterns, symbols and textures to emphasize and contrast visual elements in graphs and figures, rather than using colors alone. Use a color-blind friendly palette that is designed with accessibility for visually impaired people. Avoid bad color combinations such as green/red or blue/purple.
- Stereotyping: Reinforcing gender stereotypes in names or examples of roles, e.g., using only feminine names or presentations for personal secretary or assistant roles.
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.
Examples of infusing diversity into writing to consider adopting:
- Embracing different cultures: Names of people are a visible way to enhance diversity of representation in writing. Instead of reusing overused names in computing such as Alice and Bob, consider using names from a variety of languages, cultures, and nationalities, e.g., Alvarez and Bano. Avail of the many online resources on this front for ideas, e.g., on names across different cultures.
- Embracing differences in figures: Depictions of people or people-like icons in illustrations are also a good avenue to enhance diversity of representation. Consider depicting people of different gender presentations, skin colors, ability status, and other visible attributes of people.
- Embracing gender diversity in pronouns: Consider using a variety of gender pronouns across your named examples consciously, including “he/him/his,” “she/her/hers,” and “they/them/theirs”. Likewise, consider using gender-neutral nouns when referring to generic roles, e.g., “chairperson” or just “chair” instead of “chairman,” and gender-neutral pronouns for such roles.
Finally, if your work involves 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 discussing the potential for disparate impact of facial recognition in healthcare and strategies to avoid or reduce harm. 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.
Acknowledgments and Further Reading:
- ACM SIGMOD 2021 Diversity and Inclusion in Database Venues.
- ACM Diversity and Inclusion:
- ACM SIGMOD Blog article on “Data, Responsibly”:
- AMA Journal of Ethics article on “What are Important Ethical Implications of Using Facial Recognition Technology in Health Care?”: https://journalofethics.ama-assn.org/article/what-are-important-ethical-implications-using-facial-recognition-technology-health-care/2019-02
- Article on “Inclusive CS Examples”:
- Article on “Terminology, Power and Oppressive Language”:
- Helpful materials from UCSD CSE Diversity, Equity, and Inclusion Committee:
- NeurIPS CFP on Broader Impact Statement:
- Wikipedia listing of names across cultures: