ACM SIGMOD City, Country, Year
sigmod pods logo

SIGMOD 2021: Panel Discussions

Automation of Data Prep, ML, and Data Science: New Cure or Snake Oil?


Arun Kumar (University of California, San Diego)


As machine learning (ML), artificial intelligence (AI), and Data Science grow in practical importance, a large part of the ML/AI software industry claims to have built tools and platforms to automate the entire workflow of ML. That includes vexing problems of data preparation (prep), studied intensively by the database (DB) community for decades, with basically no resolution so far. Such claims by the ML/AI industry face a stunning lack of scientific scrutiny from the DB and ML research worlds, largely due to the lack of meaningful, large, and objective benchmarks. As such tools rapidly gain adoption among enterprises and other customers, this panel will debate whether the new ML/AI industry is basically selling "snake oil" to such users, how to evolve away from the status quo by instituting meaningful new benchmarks, creating new partnerships between industry and academia for this, and other pressing questions in this important arena. We aim to spur vigorous conversations that will hopefully lead to genuine new cures for an age-old affliction in Data Science.

Comfired Panelists

Data Management to Social Science and Back in the Future of Work


Sihem Amer-Yahia (CNRS, Univ of Grenoble Alpes)

Senjuti Basu Roy (New Jersey Institute of Technology)


How will we work, live, and thrive in the post-pandemic future? The rapid mushrooming of online job markets has been transforming the definition of work and workplaces. After the pandemic, as we "cope with the new normal", the future world of work may change forever and become predominantly virtual. This makes an unprecedented pool of talent available at our beck and calls to work on "gigs" that disband when the job is over; this also is the time of destabilization and changing nature of job security. As scientists, we have a big responsibility and a tremendous opportunity in shaping the Future of Work (FoW) post pandemic, by designing effective platforms that support productive employment, mitigate social costs, and provide an effective and safe learning environment.

A research agenda for FoW must mobilize the participation of various scientific, regulatory and miscellaneous stakeholders [10]. We will ask the questions: what is the role of Data Management (DM) in shaping research on FoW? Is now a ripe time to get Economics, Labor Theory, Psychology of Work and AI to help put DM research and technology at the center of research on FoW? Are we at all interested? The panelists will debate two complementary views: A pessimistic view on whether FoW will tend to see humans as machines, robots, or low-level agents and use them in the service of broader AI goals vs. a more optimistic view, where AI and Social Science will help DM to develop technologies that empower humans for future workforce and workplaces.

Comfired Panelists

Follow our progress: FacebookTwitter