More Than Just Algorithms(queue.acm.org)

queue.acm.org

https://queue.acm.org/detail.cfm?id=3587481

7 comments

Having read the whole thing, it comes across as pretty high on the bland-corporate-speak spectrum, e.g.

> "For me, the first big revelation of data science was that data can be a key asset that offers real value. But, the second revelation was that data can be a liability if you're not a good shepherd for it."

Translation: "Targeted advertising has been highly profitable, but it's a problem when your target's credit card numbers, address, SSN, get are exfiltrated to a dark web broker."

> "End-to-end encryption would reduce risks to privacy and keep providers from seeing private messages, but it would also limit platforms' abilities both to respond to law enforcement requests and to perform content moderation."

Translation: "Governments want more power to snoop on citizen's hearts and minds, identify potential subversives at an early age, and control the spread of information, and if we (Big Tech) don't cooperate we won't get government contracts and might be targeted ourselves."

See: https://en.wikipedia.org/wiki/Dragonfly_(search_engine)

troelsSteegin3 anni fa

This is an interview with the authors of "Data Science in Context", https://datascienceincontext.com/, about their intentions behind the book, which I'll describe as a framework for applied data science. The book looks like a textbook for people outside CS, as written by luminaries in CS - Norvig, Wing...

garganzol3 anni fa

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Willuminaughty3 anni fa

This article is a deep dive into the complexities and nuances of data science, and I found it incredibly enlightening. The discussion with Alfred Spector, Peter Norvig, Chris Wiggins, Jeannette Wing, Ben Fried, and Michael Tingley provides a comprehensive view of data science's impact on various sectors and its potential for future growth. The concept of the analysis rubric, which includes elements like tractable data, technical approach, dependability, understandability, clear objectives, tolerance of failures, and ethical, legal, societal considerations, is a brilliant framework for understanding the multifaceted nature of data science. It's fascinating to see how data science isn't just about algorithms and models, but also about understanding societal norms, ethical implications, and the ever-changing world we live in. This article is a must-read for anyone interested in the broader implications and potential of data science.