Democra(zing Data Science

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Beverly Bishop
5 years ago
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1 Democra(zing Data Science Sophie William Ramesh

2 Paper: bit.ly/ddspaper Some Links Cathy O Neil s Blog (@mathbabedotorg): @tweetsbyramesh 2

3 [insert technology] for Social Good Technology is a force mul(plier, for bemer or worse 3

4 What is Data Science? Our working defini(on: transforming data into insights/solu(ons/products 1. collec(on & storage 2. cleaning & structuring 3. analyzing & finding pamerns 4. visualizing & communica(ng results 4

5 What is Democra(zing Data Science? The applica(on of data science is undemocra(c: problems that promote the common good receive insufficient amen(on. 5

6 Ford/MacArthur Founda(on, 2013 Technology talent is a key need in government and civil society, but the current state of the pipeline is inadequate to meet that need. Source: hmp://bit.ly/fordmacarthurreport 6

7 Why? misalloca3on of data science incen(ves for data scien(sts lack of diversity in data science data science educa(on 7

8 Outline Incen(ves for data scien(sts Democra(zing data science educa(on Poten(al solu(ons 8

9 Sources of Power in Data Science capital data people 9

Human exper(se...[salaries] between $200,000 and $300,000 a year 100 recruiter emails a day.

10 Human exper(se...[salaries] between $200,000 and $300,000 a year 100 recruiter s a day...working for a consumer Internet firm can be surprisingly rewarding. Source: hmp://bit.ly/wsjdatascience 10

11 Sources of Power in Data Science capital data people 11

12 Outline Structural inequali(es in data science Democra(zing data science educa(on Poten(al solu(ons 12

13 Nega(ve feedback loop Lack of diversity in data scien(sts Lack of diversity in educa(on Lack of diversity in applica(on 13

14 Why care? Diversity is key to innova(on (Forbes Insights, 2011) Lack of diversity perpetuates misalloca(on 14

15 Racial inequality in science and engineering Misrepresenta(on isn t going away Fewer minori(es receive degrees Source: NSF Science and Engineering Indicators

16 1985: 37% 2000: 29% 2014: 18% Women in Compu(ng 2000 to 2014: 5% decrease in math 2% decrease in engineering Source: NSF Science and Engineering Indicators

17 Graduate degrees Women fare even worse 2x as many white males receiving degrees as all minori:es combined Only 1 in 5 PhDs female Source: NSF Science and Engineering Indicators

18 Unlocking the Clubhouse: A Case Study 2014 incoming class: 40% women Source: Unlocking the Clubhouse,

19 Triggering Posi(ve Change insuring science and technology are considered in their social context may be the most important change that can be made in science teaching for all people, both male and female. Source: Taggart & O Gara,

20 Posi(ve feedback loop Relevant applica(ons Increase educa(onal opportuni(es AMract diverse demographics 20

21 Outline Structural inequali(es in data science Democra(zing data science educa(on Poten(al solu(ons 21

22 Technologies Recent technologies target broader audiences Ooen require significant technical literacy Can we broaden access further s(ll? 22

23 Crowd- based efforts Machine learning compe((ons Can promote meaningful problems Low barrier to entry Ooen run by for- profit en((es Can we encourage more ini(a(ves like KDD Cup 2014? 23

24 Educa(on: MOOCs Tremendous poten(al for reaching students Most ooen taken by professionals and people with graduate degrees 24

25 Private Sector Opportuni(es Reaching out to underserved groups Tap new markets Pro bono work could service groups and bring in new customers Meaningful small data to serve the long tail 25

26 Academic Research Research promo(ng social good is par(cularly accessible to academics Social welfare problems ooen rely on public data Academia is well- suited to interdisciplinary research Need for focus on meaningful problems 26

27 Your Solu(on Here! We believe the community has a responsibility to solve these problems Exper(se in policy, business, sta(s(cs, healthcare, computer science will all be crucial Undemocra(c inequali(es persist in data science applica(ons All of us can be part of @tweetsbyramesh 27

In this first of a series of MVision Insights, we commissioned research from the London Business School into the participation of women in the US

In this first of a series of MVision Insights, we commissioned research from the London Business School into the participation of women in the US venture capital business. Our aim is to stimulate a debate