Jens-Erik Mai
Department of Communication,
University of Copenhagen, Denmark
Information in the AI-gorithmic world: Privacy, Identity, Chatbots
On-Site Keynote
On-Site in Bloomington, Indiana, USA: Thursday, 20 March 2025: 11:00am - 12:30pm
Life in the AI-gorithmic age is increasingly shaped by algorithmic processes, generative artificial intelligence, machine learning, data analytics, and the vast collection of personal information by big tech. In this talk, I will explore how adopting an information perspective can illuminate key challenges and deepen our understanding of this era. I will focus on three central issues: privacy as the control of personal information, identity as the construction of an informational self, and chatbots as tools for facilitating the exchange of meaningful information. I will argue that information lies at the heart of
analyzing these technologies, along with their political and ethical implications, and that a robust theory of information is essential for addressing the core challenges of the AI-gorithmic age.
Tshiamo Motshegwa
African Open Science Platform (AOSP)
Digital Transformation Division
National Research Foundation
South Africa
African Open Science Platform – Implementing The African Open Science Vision
On-Site Keynote
On-Site in Bloomington, Indiana, USA: Friday, 21 March 2025: 11:00am - 12:30pm
This talk discusses the development of the African Open Science (AOSP) in advancement of open science in the African continent including through leveraging
open science, data utility, artificial intelligence opportunities in support of the platform’s science priorities.
Beth A. Plale
Luddy School of Informatics, Computing, and Engineering
Indiana University Bloomington
Being Responsible about AI: View Under the Hood
On-Site Keynote
On-Site in Bloomington, Indiana, USA: Tuesday, 18 March 2025 4:00pm - 5:30pm
Most researcher/scholars engaging with AI in their research aim to judiciously adhere to principles of responsible use such as the Association for Computing Machinery Global Technology Policy committee Statement on Principles for Responsible Algorithmic Systems (Nov 2022) and more recently Principles for the Development, Deployment, and Use of Generative AI Technologies (Jul 2023). While the principles serve as helpful guides, research exists in a highly dynamic environment with a host of complicating factors that can challenge a researcher/scholar’s sense of responsibility. This talk gives a view on the challenges and opportunities of AI research from the perspective of the broader context of compute resource shortages, data constraints, and global forces.
Specifically, the drive for results can mean optimizing for opportunism when it comes to acquiring necessary compute resources. The primary computational unit on which AI research is carried out (the “GPU”) is in short supply due to confluent factors: the explosion of AI research, the significant computational requirements especially of generative AI and the highly concentrated need in deep pocket, large technology companies. Excessive need for compute resources stresses energy budgets and taxes the environment. Strong demand from technology companies for a product under virtual monopolistic control puts academia on its back foot. Federally funded computational infrastructure is attempting to address the need through national resources but that requires data to move.
Too, data is increasingly being held close, and this impacts AI models which are inextricably tied to the datasets that it uses for training. Dataset use in science used to be relatively easy – those who needed the datasets were frequently within the field or a closely related field so who used a dataset and how it was used was relatively easy to work out. But as research institutions are becoming more aware of data sensitivities, restrictions on the use of datasets are having to be worked out more explicitly. Data are at the risk of being held too close and not contributing to science and scholarship where it could. The research enclave is an opportunity.
Finally, science and technology are increasingly becoming a national currency of power (Gil 2025). AI research must balance both openness to facilitate science and public good, with security concerns to protect national competitive interests. This is complicated by generative AI that is susceptible to new host of attack vectors and data leaks.