Black in AI
The session will introduce the AISTATS community to the Black in AI organization, including a discussion of ways to get involved, avenues for receiving and providing mentorship, and suggestions for getting started in research or applied artificial intelligence. This will be followed by a mentoring session, discussing pathways for graduate school, and tips for careers in both academia and industry.
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"A.I. is like nuclear energy -- both promising and dangerous" -- Bill Gates, 2019.
Data Science is a pillar of A.I. and has driven most of recent cutting-edge discoveries in biomedical research. In practice, Data Science has a life cycle (DSLC) that includes problem formulation, data collection, data cleaning, modeling, result interpretation and the drawing of conclusions. Human judgement calls :wq:ware ubiquitous at every step of this process, e.g., in choosing data cleaning methods, predictive algorithms and data perturbations. Such judgment calls are often responsible for the "dangers" of A.I. To maximally mitigate these dangers, we developed a framework based on three core principles: Predictability, Computability and Stability (PCS). Through a workflow and documentation (in R Markdown or Jupyter Notebook) that allows one to manage the whole DSLC, the PCS framework unifies, streamlines and expands on the best practices of machine learning and statistics – bringing us a step forward towards veridical Data Science. We will illustrate the PCS framework in the modeling stage through the development of DeepTune images for characterization of neurons in the difficult V4 area of primary visual cortex.
Caucus for Women in Statistics
Round Table Discussion: Journeys in AI, ML and Stats: the female perspective
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