Call for Diversity and Inclusion Track

Diversity & Inclusion (D&I) Track

The D&I track is dedicated to promote diversity and inclusion (D&I) in research in Data Sciences and Databases, and also the use of data to study questions around diversity and inclusion in social or institutional settings. The goal of the D&I track is to enable greater participation of a wider audience than is typical, as well as to promote research that enables us to better understand what the current barriers are to achieving equitable representation of different demographics and what can be done to remove such barriers.

Introduced in 2022, this track has received highly encouraging responses, which has led to its scope being continuously evolved. This year also we intend to add some new activities. Like earlier years, our focus is on achieving not only greater gender diversity, but also diversity along other salient dimensions such as caste, geography, language, institutional affiliation, and more.

The track will feature eminent keynote speakers, a live panel, and talks on experiences of people in data science from diverse backgrounds, and D&I opportunities enabled by ACM/ACM India. This year we will also host a poster session on “Data Science for Inclusivity”, which will feature ongoing work done by researchers in this area. Below is a “Call for abstract” for the session. We encourage researchers from all disciplines who work in relevant problems to submit abstracts of their work for this track. Additionally, we will be hosting mentorship and networking luncheon opportunities for students, faculty members, and researchers from traditionally under-represented groups to provide them an opportunity to interact with leading scientists from both academia and industry. Usually there are a few scholarships that are offered under the track, in order to enable greater participation in the conference of attendees from traditionally underrepresented segments. We aim to continue this practice; for more details about the scholarships, watch this space.

Data Science for Inclusivity - Call for Extended Abstracts

As data-driven decision making influences almost all spheres of our lives, it is imperative to focus on relevant data science problems and methods that address diversity and inclusion issues. It is a participatory workshop where the intent is to increase awareness about the role of data science to ensure a better future.

We invite extended-abstract submissions of up to 4 pages, including references, on any aspect of the use of data science and data-driven research to understand or address diversity and inclusion issues in any social or institutional context. Some indicative, but not exhaustive, topics are mentioned below :

  • Methods for detecting and/or preventing hate speech directed towards underprivileged groups on social media platforms
  • Tools or techniques for increasing the linguistic accessibility of websites, apps, or other IT services
  • Use of behavioral or neural data to study cognitive diversity or neurodiversity across different groups of people
  • Methods for detecting bias in data and/or bias mitigation policies
  • Data-driven policy planning to ensure greater inclusion

Selected papers will be invited for a lightning and poster presentation as part of a pre-conference workshop. The session will also host talks by a few eminent senior researchers of India, working in the area. The session will give an opportunity to the participants to interact with the senior researchers and get feedback / guidance / new ideas.

There will be an award for the Best Poster.

While the accepted abstracts will not be included in the conference proceedings, these will be made available online and linked to the conference website.

Submission Instructions

Please see this page for common submission instructions.

Important dates

All deadlines are Anywhere on Earth (AoE, UTC-1200)

September 30, 2023: Submission of abstract
October 30, 2023: Notification of selection

Diversity & Inclusion Track Chairs

Lipika Dey, TCS Research <[email protected]>
Sumeet Agarwal, IIT Delhi <[email protected]>

For more details, please reach out to the track chairs at [email protected]