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Ethics: A Data Scientist's Perspective
- 1. Ethics: A Data Scientist’s Perspective Mike Zawitkowski Founder, Principal Consultant Acorn Analytics Inc. http://AcornAnalytics.io “10 common ethical errors in data science”
- 2. Views are mine alone. What works for me may not work for you.
- 3. Base Assumptions • ethics = knowing right from wrong • Ethics and morals used interchangeably • Line between ethical and legal is murky • External ethics informs internal ethics
- 5. Key Takeaway • Internal/external misalignment = trouble • Trouble = maybe a little guilt, or a lot of jail • Internal trumps External: start from within
- 6. What we’ll do for the next hour.. Now Talk 7:40 Q&A 7:55 Parting Words 7:58 Back to you, Ovid.
- 8. ’Uncle’ Bob Martin - The Future of Programming https://youtu.be/ecIWPzGEbFc
- 12. Domain Expertise Software Engineering Math & Statistics Volkswagon was a data science scandal
- 24. Practice Best Practices • Transparency • Reproducability • Code Standards (Google’s R Styleguide, PEP8) • TDD, Agile, Scrum • Solicit, Accept, Provide Honest Feedback • Lifelong Learning • Scientific Method
- 26. Cookie Cutter Data Science https://drivendata.github.io/cookiecutter-data-science/
- 28. Fixing Internal/External Mismatch Low Mid High High Change careers Change neighborhoods Fix the external Mid Change employers Reinforce internal Compromise personal ethics Low Make new friends Do nothing Total Potential Cost* to the Individual Short Term Effort * Cost may amount to a little guilt, or a lot of jail time
- 29. Fixing Internal/External Mismatch Low Mid High High Change careers Change neighborhoods Fix the external Mid Change employers Reinforce internal Compromise personal ethics Low Make new friends Do nothing Total Potential Cost* to the Individual Short Term Effort * Cost may amount to a little guilt, or a lot of jail time Always start here
- 30. Fork Your Own Code of Ethics • Search for "ethics" + [keywords] • “data science code of ethics”
- 31. Good starting points • https://unstats.un.org/unsd/dnss/docViewer.as px?docID=93#start • http://www.datascienceassn.org/code-of- conduct.html • http://www.acm.org/about/se-code
- 32. Find Your Tribe
- 33. Checklists, Mental Models 1. Are the statistics solid? 2. Who wins? Who loses? 3. Are the changes in power structures helping? 4. How can we mitigate harms? — Abe Gong, CDO Aspire Health https://www.oreilly.com/ideas/metrics-are-great-but-even-a-data-driven-business-needs-to- consider-side-effects
- 34. “Do nothing that you would not be happy to have an unfriendly but intelligent reporter write about on the front page of a newspaper." —Warren Buffett
- 35. K-GULF • Kindness • Generosity • Understanding • Love • Forgiveness
- 36. Q&A Mike Zawitkowski Acorn Analytics Inc. http://AcornAnalytics.io medium.com/acorn-analytics-blog linkedin.com/in/mikezawitkowski twitter.com/AcornAnalytics
- 39. “Your reputation is what you're perceived to be; your character is what you really are. I think that character is much more important than what you are perceived to be. You'd hope they'd both be good, but they won't necessarily be the same.” — Coach John Wooden
Editor's Notes
- By tribe I mean your local community, your family, your friends, your neighborhood, your co-workers, your clubs. You know the names of most of these people. Society: I mean the greater communities you’re affiliated with. Your country you live. The industry where you work. The faceless groups you have a sense of belonging to. Humanity is the big picture. What the majority of human beings can mostly agree on in terms of right and wrong.
- “The detailed requirements for each test gave Volkswagen the advance knowledge it needed to teach its cars when to behave more cleanly. By measuring how long the engine was running, the vehicle’s speeds, and even seemingly esoteric factors, such as the position of the steering wheel and barometric pressure, Volkswagen vehicles could understand they were being tested and so adjusted their engine performance to pass the tests, according to the EPA.”
- Volkswagon was a data science scandal.
- In his future of programming talk Uncle Bob made a comment about how the number of programmers in the world doubles every 5 years, and therefore at least half of all the people in the industry have less than 5 years experience. So I did some digging to figure out those numbers, because they scare the shit out of me about its implications for our young industry of data science. Bob wrote: Since 1974 the rate of number of estimated programmers in the world has grown by 14.5% every year. That’s a doubling every 5 years. He added a footnote that there’s some evidence this has slowed to 8% a year, but this is the point he wrote: “the industry as a whole will remain dominated by novices, and exist in a state of perpetual immaturity. …This will remain true as long as the exponential growth continues. Time can’t heal this problem.”
- In his future of programming talk Uncle Bob made a comment about how the number of programmers in the world doubles every 5 years, and therefore at least half of all the people in the industry have less than 5 years experience. So I did some digging to figure out those numbers, because they scare the shit out of me about its implications for our young industry of data science. Bob wrote: Since 1974 the rate of number of estimated programmers in the world has grown by 14.5% every year. That’s a doubling every 5 years. He added a footnote that there’s some evidence this has slowed to 8% a year, but this is the point he wrote: “the industry as a whole will remain dominated by novices, and exist in a state of perpetual immaturity. …This will remain true as long as the exponential growth continues. Time can’t heal this problem.”
- Harvard Law https://sjclawlibraryblog.files.wordpress.com/2016/08/cropped-picture1-22.png