Subject archive for "model-context," page 3
On Ingesting Kate Crawford’s “The Trouble with Bias”
Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED and local affiliates. Members of Domino were in the live audience for the City Arts talk. This Domino Data Science Field Note provides insights excerpted from Crawford’s City Arts talk and from her NIPS keynote for additional breadth, depth and context for our data science blog readers. This blog post covers Crawford’s research that includes bias as a socio-technical challenge, implications when systems are trained on and ingest biased data, model interpretability, and recommendations for addressing bias.
By Domino11 min read
The Machine Learning Reproducibility Crisis
Are We Back in the Dark Ages? Without Source Control?
By Pete Warden9 min read
Managing Data Science as a Capability
Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts. The learnings, anecdotes, and best practices shared in the workshop were based upon years of candid discussions with customers about managing and accelerating data science work. The workshop also featured reusable templates that included a pre-flight data science project checklist as well as a planning template for hiring and onboarding data scientists. We are sharing the breakout materials based on attendee feedback. If you missed Strata and are interested in joining similar discussions, then consider attending Rev.
By Domino5 min read
0.05 is an Arbitrary Cut Off: "Turning Fails into Wins”
Grace Tang, Data Scientist at Uber, presented insights, common pitfalls, and “best practices to ensure all experiments are useful” in her Strata Singapore session, “Turning Fails into Wins”. Tang holds a Ph.D. in Neuroscience from Stanford University.
By Domino5 min read
Data Science Use Cases
In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next.
By Donald Miner19 min read
Racial Bias in Policing: An Analysis of Illinois Traffic Stop Data
Mollie Pettit, Data Scientist and D3.js Data Visualization Instructor with Metis, walks data scientists through analysis of Illinois police traffic stop data, presenting a story narrative of Chicago in 2016. Pettit also discusses how, and shows why, data scientists need to be thoughtful and aware of assumptions when analyzing data and presenting a story narrative.
By Domino14 min read
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