Overview
This year’s Annual Meeting theme is: Data Science and AI – Keeping Humans in the Loop
Data has become the essential building block for research and insight in nearly all fields. The next wave of emerging technologies, including artificial intelligence (AI), is fueled by the unprecedented amount and variety of available data. Data and data-enabled technologies promise to reshape research and discovery, as well as how we live and how society functions. ADSA’24, hosted by the Michigan Institute for Data Science, will draw our focus to humanity in data and AI – humans as data producers and data engineers, humans as AI designers and developers, humans represented in data, and humans as data and AI users and as the beneficiaries or victims of data and AI. We will explore the central role of humans in the data and AI revolution: to maximize its benefits for research and innovation; to ensure that the use of data and technologies and the insights they generate are aligned with our values and priorities; and to ensure that the future workforce are prepared to continue discovery and innovation.
Keynote Speakers
Janet Haven
Executive Director of Data & Society
Maggie Levenstein
Director of ICPSR
University of Michigan
Session Topics
- K-12, Community College, & Beyond: Developing Inclusive Ramps and Pathways to Data Science
- Critical Making as Pedagogical Approach to the Data Science Classroom
- Connecting the Dots: AI Education and Training with AI and Data Science Jobs
- Enhancing Learning through Data Science Competitions: Insights from a Hackathon
- Best Practices for Teaching Ethics in Data Science
- Impacts of Data Science for Social Good Training Programs on Student Experiences and Workforce Demands
- Analyzing Microtargeting on Social Media
- Labor in Data Science and AI
- Privacy-by-Design: Interactive Record Linkage Using a Hybrid Human-Computer
- Art, artists and the concepts of intelligence, artificiality and realness.
- Stories in Concert: take a qualitative journey of information gathering and meaning making (interactive session!)
- Expanding Beyond Natural Language Processing: Harnessing Large-Scale Models for Real-Time Series Data Prediction in Advancing Wireless Communications and Networks towards the Next Generation
- Data-driven Approaches as a Revolution for Space Weather Forecasting
- Generative AI and the future of scientific code
- Healthcare AI Infrastructure and Governance, considerations from the Clinical Intelligence Committee
Workshops
- Exploring and Accessing Data Through the Inter-University Consortium for Political and Social Research (ICPSR) – Lynette Hoelter (University of Michigan), Amy Pienta (University of Michigan)
- DEDICATE: Creating Accessible POGIL-based Data Science Training for Social Impact Education – Ravanasamudram Uma (North Carolina Central University), Earvin Balderama (California State University, Fresno), Marc Boumedine (University of the Virgin Islands)
- Engaging Minds with Project-Based Learning in Data Science – Jeanne McClure (North Carolina State University), Kelsey Dufresne (North Carolina State University)
- Workshop on Data for Good for Education – Karl Schmitt (Trinity Christian College), Dharma Dailey (University of Washington), Allissa Dillman (Montgomery College), Katherine Walden (University of Notre Dame), Samuel Fanijo (Iowa State University), JaMor Hairston (Emory University), Laura Harris (Michigan State University), Rebecca Rapp (Washington & Jefferson College)
- Analytical Storytelling (aka, How to Present to a Non-Expert Audience) – Douglas Hague (University of North Carolina at Charlotte)
- Case Study Materials for Teaching Ethics in Data Science (MTEDS-case studies) – Lenore Cowen (Tufts University), Thomas Arnold (Tufts University), Muhammad Umair (Tufts University)
- Unlocking Census Data Through data.census.gov – Tyson Weister (U.S. Census Bureau)
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