Operationalizing Data Ethics: Considerations for the Artificial Intelligence and Machine Learning (AI/ML) Development Lifecycle
Digital government is built on big data that is constantly being created, aggregated, manipulated, and shared. The nature of this data usage—in addition to a rapidly evolving technology landscape—increasingly introduces risks that are not accounted for in traditional data governance frameworks. A data ethics perspective is critical for adapting risk mitigation strategies in the AI/ML development lifecycle to address risks such as reinforcing human biases, using data and analytic outcomes unethically, prolonging discrimination, and failing to make algorithmic decision-making transparent and comprehensible to humans.
LMI invites you to attend its half-day workshop, “Operationalizing Data Ethics: Considerations for the AI/ML Development Lifecycle,” which will focus on aspects of data ethics that are pervasive in AI/ML. In this context, we will share how guiding data ethics principles may be operationalized, measured, monitored, enforced, and institutionalized. The workshop will connect policy makers, practitioners, and decision makers with AI/ML experts from academia, industry, and the startup technology sector.
The Center for Data Ethics and Justice at the University of Virginia School of Data Science will explain how academia is preparing our future data scientists and leaders for the intersection of data science and ethical, social, and political issues. Attendees will also hear from a panel of experts that will discuss how data ethics may be a persistent design consideration across all use cases, how AI/ML explainability facilitates data ethics, and how technology and data governance may help enforce data ethics policies.
Space is limited, register before January 7 to attend.
Welcome: Workshop Framing & Objectives
Linda Bixby, PhD Director, Academic Programs, LMI Research Institute
Brant Horio Director, Data Science, LMI
Overview: University of Virginia (UVA) School of Data Science