Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. We argue that a new specialization should be formed within machine learning that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural machine learning.
Short Bio: Timnit Gebru co-leads the Ethical Artificial Intelligence research team at Google, working to reduce the potential negative impacts of AI. Timnit earned her doctorate under the supervision of Fei-Fei Li at Stanford University in 2017 and did a postdoc at Microsoft Research NYC in the FATE team. She is also the cofounder of Black in AI, a place for sharing ideas, fostering collaborations and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence.
NVIDIA
Programming Systems of Data
Machine learning and data analysis thrive on mass quantities of data. At the same time, the cost of data distribution and movement is among the most critical factors determining the performance of applications at scale. Consequently, scalable high-performance machine learning and data analysis requires software environments that support the careful management of data. Whereas modern cloud systems provide data stores and services that help support efficient delivery of data to applications, the tools at hand for developers to efficiently manage distributed data within a running application are considerably more limited. It is particularly challenging to deliver high-performance execution across distributed nodes while maintaining software modularity and composability. In this talk, I will focus on developments in the design of scalable programming systems that help address these challenges by providing data-centric interfaces that provide a convenient notation to the developer and dynamic information to the runtime system tasked with scheduling the application at peak efficiency.
Short Bio: Michael Garland is the Senior Director of Programming Systems and Applications
research at NVIDIA. He completed his Ph.D. at Carnegie Mellon University, and
was previously on the faculty of the Department of Computer Science of the
University of Illinois at Urbana-Champaign. He joined NVIDIA in 2006 as one
of the first members of NVIDIA Research, and has been working to develop
effective parallel programming systems ever since. His research goal is to
develop tools and techniques that will equip programmers to realize the full
potential of modern, massively parallel, computing systems.