A Recap of the 2024 Data Science Leadership Summit

Leaders Taking Responsibility for the Future of Data Science in Academia

3 June 2024

BY VERONICA WOODLIEF

 

ADSA’s Data Science Leadership Summit convenes the leadership community around common challenges and opportunities to share ideas and best practices for taking collective responsibility in preparing next-generation data scientists to contribute to the best interests of society.

The Summit is one of ADSA’s two flagship annual events (the other is the ADSA Annual Meeting, which will be at the University of Michigan on Oct. 29-31, 2024). This year marked the 5th annual Leadership Summit and was hosted by the Georgia Tech Institute for Data Engineering and Science (IDEaS) on May 6 - 8 in Atlanta, GA.

The theme of this year’s Summit was Data Science in the Age of AI, with sessions highlighting the challenges and opportunities presented by the rapid rise of AI related to data science research, education, and ethics, as well as program administration and operationsHow can educators effectively teach and foster AI literacy among students? What strategies should universities adopt to integrate AI into their operations and administration? How can AI be leveraged to enhance research processes and promote interdisciplinary collaboration? Most importantly, how can these initiatives be undertaken with an eye on the ethical implications of AI technologies, such as bias, privacy, accountability, and the potential for misuse? These questions were examined in panel discussions throughout the 3-day program.

The AI for Research panel, featuring Andrew Connolly (University of Washington, Bryan Carter (University of Arizona), Jing Liu (University of Michigan, Colin Twomey (University of Pennsylvania), and Ellen Zegura (CISE NSF), and chaired by Arthur “Barney” MacCabe (University of Arizona) and Bhuvnesh “Bhuv” Jain (University of Pennsylvania), explored how AI is advancing research across various fields, including STEM, medicine, and the humanities, by enhancing the research process and facilitating interdisciplinary collaboration. They also discussed the development of national and local infrastructures to support AI in research, such as the National AI Research Resource (NAIRR) and university-level initiatives. Following the panel discussion, attendees went into breakout tables to discuss how AI is transforming research at their institutions. Many of the discussions mentioned an increase in hiring faculty for new or expanding AI initiatives, but distinguishing between specialized AI work and applied AI work embedded in other disciplines can be challenging when it comes to hiring decisions. There were also commonalities in a collaborative and interdisciplinary focus amongst researchers from different departments (including the arts, humanities, and social sciences departments at many institutions), and some concern that the hype around AI is overshadowing the importance of data science (several participants joked that all data science centers should re-brand themselves as AI centers).

The AI and Data Science Education panel, featuring Rohan Alexander (University of Toronto, Varada Kolhatkar (University of British Columbia), and Brian Wright (University of Virginia), and chaired by Megan Mittelstadt (University of Georgia) and Eric Van Dusen (University of California, Berkeley, discussed AI’s crucial role in teaching data science and enhancing AI literacy among students, with practical examples of project-based learning using cloud computing and Large Language Models. The discussion covered incorporating foundational AI knowledge, programming, data analysis, ethics, interdisciplinary understanding, and continuous learning. Following the panel, participants engaged in structured breakout conversations to share best practices and brainstorm strategies for integrating AI into data science curricula.

 

The AI for Program Operations panel, featuring Ron Hutchins (Golden State Net and University of Virginia) and Stella Wenxing Liu (Arizona State University) and chaired by Arlyn Burgess (University of Virginia), began with a discussion on universities' and administrations' priorities regarding AI in operations, highlighting task forces, common questions, and traditional data science approaches for AI integration. It explored the culture of higher education and effective collaboration between academic and administrative units. The session concluded with a presentation of Arizona State University’s AI Acceleration Evaluation Framework, followed by an interactive workshop where participants developed similar processes for their own initiatives and institutions.

Not surprisingly, the AI in Ethics panel, chaired by Celeste Lee (Spelman College) and Robert Rosenberger (Georgia Institute of Technology) was a favorite among participants. The panel, which included Justin Biddle (Georgia Institute of Technology, Yanni Loukissas (Georgia Institute of Technology), Damian Patrick Williams (University of North Carolina, Charlotte), and Shay Welch (Spelman College), examined how Generative AI has rapidly advanced with developments in large language models, chatbots, and multimodal technologies, thus highlighting the need for academic institutions to prioritize ethical considerations and value alignment. They discussed the relationship between AI, ethics, and design, addressing issues like bias, privacy, accountability, and value installation. There was a breakout activity planned for the second half of the session, but the conversation was so rich that the entire time was used for the panel! One participant reported that the Ethics panel “introduced new perspectives that I found inspiring. It re-energized me.” Another applauded the “perspectives of the speakers from viewpoints related to, but beyond, data science-specific disciplines.”

I think all of the sessions were excellent. The panels were fantastic. It was also great to hear the AI Ethics perspectives of the speakers from viewpoints related to, but beyond, data science-specific disciplines. Such an impressive gathering. Honored to have attended.

In line with the theme of ethics, keynote speaker David Leslie, Director of Ethics and Responsible Innovation Research at The Alan Turing Institute and Professor of Ethics, Technology, and Society at Queen Mary University of London, delivered a presentation titled “Research Ethics in the Generative AI Era”. He advocated for a “tool-based understanding of AI systems as computational instruments embedded in warm-blooded interactive processes” and emphasized the importance of the principles of research ethics—honesty, rigor, open and transparent communication, respect for all research participants, and responsibility—to incorporate AI into contemporary science appropriately.

I really loved the ethics session... it presented new perspectives that I found inspiring. It re-energized me.

The final session of the program was an interactive workshop presented by Sondra LoRe and Greg LoRe (SPEAR- STEM Program Evaluation, Assessment, & Research) and chaired by Michael Colaresi (University of Pittsburgh) and Rachel “Ray” Levy (North Carolina State University). Participants engaged with hands-on materials for program evaluation mapping, incorporating case studies from an existing grant-funded program into group discussions to facilitate connections to their own programming opportunities. They left equipped with materials for mapping their programs, sample metrics for measurement, and methods for communicating return on investment in inclusive and accessible ways. 

The Summit also featured a fireside chat with the city of Atlanta’s first-ever Senior Technology Advisor, Donnie Beamer, and Director of the Atlanta University Center (AUC) Data Science Initiative, Talitha Washington,  who discussed their work building partnerships across the tech industry, academia, and city government in Atlanta.

 

The “unconference” lightning talks emerged as another highlight. Speakers were allowed 3 minutes to pitch an opportunity for collaboration, a thought exercise on a hot topic in data science, or another discussion-provoking topic. These unconference sessions received unanimous acclaim from participants, as they facilitated collaboration and more informal interactions. One attendee remarked, “The networking was fantastic and was really helped by the "unconference" talks. I met people with whom I will pursue collaborations, and I re-engaged with folks I had met before.”

The networking was fantastic and was really helped by the "unconference" talks. I met people with whom I will pursue collaborations, and I re-engaged with folks I had met before.

Attendees were surveyed about their experience at the Leadership Summit shortly after the event.

Here are some of the positive outcomes reported:

  • 83% of participants surveyed learned about Data Science programs at other institutions
  • 75% had a productive interaction with someone they just met at the Summit; 64% had a productive interaction with colleagues they already knew
  • 72% said the Leadership Summit made them feel part of a greater data science community
  • 58% were inspired to work with people they haven't worked with before
  • 47% were inspired to push for change at their university, college, or organization
  • 42% discovered approaches to data science programs that might work at their school
  • 39% learned about approaches to incorporating AI tools or curricula in their programs
  • 11% were inspired to start a new program (keep us posted!)

 

The 2024 Data Science Leadership Summit was a resounding success, bringing together academic leadership to strategize on the continued development and evolution of data science and the integration of AI across campus. With a strong focus on responsibility and ethics, participants charted paths forward for data science in the age of AI.

The success of the Summit was due in large part to our active and collaborative community (you!), but we especially want to thank the 2024 Program Committee for their hard work and support of the meeting.

2024 Leadership Summit Program Committee

  • Rachel Levy (co-Chair), North Carolina State University
  • David Uminsky (co-Chair), University of Chicago
  • Srinivas Aluru (general Chair), Georgia Institute of Technology
  • Mario Banuelos, Fresno State University
  • Arlyn Burgess, University of Virginia
  • Helen Burn, Highline College
  • Celeste Lee, Spelman College
  • Arthur Maccabe, University of Arizona
  • Melissa Ngamini, ADP and ATLytiCS
  • B. Aditya Prakash, Georgia Institute of Technology
  • Azer Bestavros (2023 Chair), Boston University

Get Involved

  • Tag us in your pictures and use the hashtag #DataScienceLeadership2024 if you attended the 2024 Leadership Summit!
  • Sponsor next year's Summit! Learn more about event sponsorship and contact us to get started!
  • Stay connected with the ADSA community. Join our Slack for ongoing discussions and collaborations.

Thank you to our generous sponsors!

 

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