Diversity & Inclusion



Starting in 2021, the Data Management community begins an integrated effort to promote diversity and inclusion in all aspects of our professional activities. EDBT/ICDT 2021 participates in this effort (alongside SIGMOD, VLDB, SoCC, and ICDE) which celebrates the diversity in our community and welcomes everyone regardless of age, sex, gender identity, race, ethnicity, socioeconomic background, country of origin, religion, sexual orientation, physical ability, education, work experience, etc. It also welcomes people and opinions of all political persuasions, as long as they abide by the ACM policy against hate speech and harassment . Specific information can be found in the following, dedicated web site:  https://dbdni.github.io 

D&I Keynote:

Analysing crowds mobility from decolonisation and inclusive perspectives


We live in the era of massive exodus and delicate personal security issues. Millions of migrant children are lost in Europe, and adults migrating from the LATAM region to North America are missing. Women disappear without a trace as they flee from war, hunger, disease, and bad local economic and political situations. This exodus creates humanitarian problems that countries have to deal with, and inclusive decolonised mobility analytics can bring hope.

Indeed, the “digital age” has brought great innovation, opportunity and connectivity and opens the hope of using data to address important questions about “subaltern” people mobility. However, too often, data is collected, analysed and interpreted in a way that perpetuates the narrative of poverty and need, painting a portrait of disparity and deficit. It is essential to ensure that both data and processing strategies (algorithms) and digital technology do not reproduce colonial paradigms of oppression, domination and harm. To avoid this narrative, it is essential to have a reckoning over lingering colonial history and practices, which are evident in the global imbalance between the Global North and the Global South, a continuation of the extractive and colonial relationship.

This talk will be weaving a trait with two strands: 1) crowds behaviour analytics techniques that combine data management and visualisation to control and model the crowds as complex systems; 2) a decolonisation perspective to perform data analysis more inclusively, allowing displaced communities to speak out.

Genoveva Vargas-Solar (http://www.vargas-solar.com) is a principal scientist of the French Council of Scientific Research (CNRS) and a member of the DataBase group of the Laboratory on Informatics on Image and Information Systems (LIRIS). She is a regular member of the Mexican Academia of Computing. She obtained her Habilitation à Diriger des Recherches (HDR – tenure) from University of Grenoble. She obtained her first PhD degree in Computer Science at University Joseph Fourier and her second PhD degree in Literature at University Stendhal. She obtained her first master’s degree in computer science at University Joseph Fourier and her second master’s degree in Compared Literature at University Stendhal. She did her undergraduate studies in Computer Systems Engineering at Universidad de las Américas in Puebla. She contributes to the construction of service-based database and ata science management systems. She proposes query evaluation methodologies, algorithms, and tools for composing, deploying, and executing data science functions on just in time architectures (disaggregated data centres). Her research interests in Literature concern middle age Literature, myths’ critics and myths’ analysis applied to different myths of origins. She promotes gender equality and diversity and inclusion (D&I) actions. She is a member of the gender equality committee at LIRIS and she represents EDBT in the inter-conference group D&I databases. She leads the SINFONIA and JOWDISAI projects on women’s work in AI and DS. She actively promotes scientific cooperation in Computer Science between Latin America and Europe, particularly between France and Mexico.

D&I Workshop@ MDM 2022

Title: Demystifying AI: Comics as a medium of scientific dissemination

Session description:

Humor is a contextual element of human communication, representing a critical difference between people and machines. Coupled with visual imagery, it presents a formidable weapon with which to cut through technical jargon and to make the discourse around critical technologies more accessible to the general public. At NYU’s center for responsible AI we run two comic book series: The Data, Responsibly comics series, aimed at budding data scientists and practitioners; and the “We are AI” comic series targeted at the general public. The primary goal of this session is to share insights from our ongoing work in using comic books as a medium of scientific dissemination and to equip participants – through hands-on activities – with the skills to build upon and continue such creative explorations, themselves. Participants will collectively “crowdsource” a comic book about Responsible AI, centered around their personal lived experience. A key novelty of this session will be the use of original artwork to provoke participants to think critically about what democratic, accessible and accountable algorithmic systems look like. We will use panels from our comics to demonstrate to participants how to break down complicated topics into simple, relatable and humorous cartoons. The best part? No art skills needed! At the event, we will engage participants in a creative exploration into what Responsible AI looks like, discussing ideas and viewpoints around the themes of algorithmic justice, fairness, rights and liberties, grounded in their lived experiences. We will emphasize underrepresented viewpoints and center the narrative around marginalized communities.


The comic book format invites the readers to be part of a solution because they can “see” the systems, values, and impacts clearly and beautifully rendered. It also reveals the limitations of the scientific method in addressing bias in algorithms. This activity will advance creative techniques for improving public engagement in AI, inviting participants to contribute a creative response to the multi-faceted approach of the ‘Data, Responsibly’ comics. Specifically:

o review the illustrative metaphor of ‘data as a mirror reflection of the world’ — a reflection that both distorts, and is distorted by, the world,

o anchor narratives in personal, lived experiences (For example: people with disabilities),

o emphasize accessibility as the norm,

o challenge the extant, binary framing of techno-optimism vs. techno-skepticism, and

o apply the “onion structure” to a narrative, such that both a casual reader and an expert is able to see themselves in the text.


Name: Falaah Arif Khan

Title: PhD student, Center for Data Science, New York University

Pronouns: she\her

Email: fa2161@nyu.edu

Website: https://falaaharifkhan.github.io/research/

Bio: Falaah is a first year Data Science PhD student at NYU, working with Prof Julia Stoyanovich on the ‘fairness’ and ‘robustness’ of algorithmic systems. An engineer by training and an artist by nature, Falaah creates scientific comic books to bridge together scholarship from different disciplines, and to disseminate the nuances of her research in a way that is more accessible to the general public — She runs the ‘Data, Responsibly’ and ‘We are AI’ comic series with Prof Julia Stoyanovich at NYU’s Center for Responsible AI, and the ‘Superheroes of Deep Learning’ comic series with Prof Zack Lipton (CMU). Falaah holds an undergraduate degree in Electronics and Communication Engineering (with a minor in Mathematics) from Shiv Nadar University, India, and has industry experience in building machine learning models for access management and security at Dell EMC.