Visiting Scholar to Present “Data Science for Social Justice”
Name:
Visiting Scholar to Present “Data Science for Social Justice”
Date:
September 25, 2023
Time:
7:30 PM - 8:30 PM EDT
Website:
SLU Event Page
Event Description:
Visiting Scholar to Present “Data Science for Social Justice”
St. Lawrence University's chapter of the Phi Beta Kappa honorary society will sponsor a presentation by Visiting Scholar Talitha Washington, president of the Association for Women in Mathematics and Director of the National Data Science Alliance. Her presentation, “Data Science for Social Justice,” will be held on Monday, September. 25 at 7:30pm in the Gulick Auditorium. The event is free and open to the public.
Washington, who holds a Ph.D. in mathematics with a specialization in applied mathematics, has designed her presentation for a general audience and will illustrate how data science impacts every facet of society: from marketing to finance to facial recognition to medical care. What happens if data science develops technology that amplifies societal biases and blatant racism? Washington will share her vision of how everyone can contribute to bringing true validity to data science.
St. Lawrence University's chapter of the Phi Beta Kappa honorary society will sponsor a presentation by Visiting Scholar Talitha Washington, president of the Association for Women in Mathematics and Director of the National Data Science Alliance. Her presentation, “Data Science for Social Justice,” will be held on Monday, September. 25 at 7:30pm in the Gulick Auditorium. The event is free and open to the public.
Washington, who holds a Ph.D. in mathematics with a specialization in applied mathematics, has designed her presentation for a general audience and will illustrate how data science impacts every facet of society: from marketing to finance to facial recognition to medical care. What happens if data science develops technology that amplifies societal biases and blatant racism? Washington will share her vision of how everyone can contribute to bringing true validity to data science.
