AI & Bias: closing the data gap
We like to think of data as being objective, but the answers we get are often shaped by the questions we ask. Beyond that, the data sets themselves could also be limited or biased. As algorithms take over many of our decision-making processes, it matters more than ever which assumptions we build our models on. If data containing inherent bias is used in self-learning systems, the bias will be magnified. Beyond deciding which music you listen to, other impacts could include your next job, your health care eligibility, insurance premiums and credit ratings.
Join our conversation on Tuesday, August 18th at 1pm EST. Register below.
Hosted by our SheSays NY team, panelists include:
Amy Bucher, VP Behavior Change Design @ Mad*Pow (Boston)
Tiffany Johnson, Senior Director, Tech & Data Analytics @ Wunderman (NYC)
Adah Parris, Futurist, Keynote Speaker, Cultural Strategist (London)
Arielle Patrick, Executive VP & Transaction Director @ Edelman (NYC)
Jennifer L. Williams, Principal, The J.L. Solution (NYC)
Moderated by SheSays NY Board Member:
Elizabeth Kiehner, Digital Transformation Officer @ Memorial Sloan Kettering
Photo above by Christina @ wocintechchat.com on Unsplash.