Posts Tagged ‘datascience’

New York City’s Bold, Flawed Attempt to Make Algorithms Accountable

January 20, 2018

NYC’s Bold, Flawed Attempt to Make #Algorithms Accountable
https://www.NewYorker.com/tech/elements/new-york-citys-bold-flawed-attempt-to-make-algorithms-accountable QT: “#NYC should commit to demanding openness in all future contracts with vendors of these algorithmic services…It’s a dereliction of duty to allow vital decisions to be made by a black box.”

QT:{{”
“Frank Pasquale,… told me much the same. “While the terms of past contracts are hard to revisit, New York City should commit to demanding openness in all future contracts with venders of these algorithmic services,” he said. “They have the leverage here, not the firms. Secrecy may incentivize tiny gains in efficiency, but those are not worth the erosion of legitimacy and public confidence in government. It’s a dereliction of duty to allow vital decisions to be made by a black box.”

Cathy O’Neil, the author of “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” told me. “What we’re finding is that the world of algorithms is one ugly wormhole.” In insulating algorithms and their creators from public scrutiny, rather than responding to civic concerns about bias and discrimination, the existing system “propagates the myth that those algorithms are objective and fair,” O’Neil said. “There’s no reason to believe either.””

Training Calendar | Research Data Support

September 16, 2017

http://researchdata.yale.edu/training-calendarthe Research Data Support website has published a unified calendar for data and research skills training provided by the Library, Center for Research Computing, Medical Library, and Center for Teaching and Learning.

John Tukey – Wikipedia

July 16, 2017

https://en.wikipedia.org/wiki/John_Tukey

Co-directors of newly launched Harvard Data Science Initiative discuss new era

June 19, 2017

fellowships, grants, space
QT:{{”
“DOMINICI: Because of the new advances in technology, almost every field right now has data, and more data than ever. Clearly, there’s the explosion of genetics and genomics data in the life sciences, in molecular data, as well as astronomy and economics. Even in the humanities, you can scan documents and turn it into data that you can analyze.

PARKES: To add some numbers to this, IBM has estimated that we’re generating more than one quintillion bytes of data a day. (A quintillion is a 10 to the 18th.)

DOMINICI: One of the reasons we are so excited that Harvard is launching the Data Science Initiative is because of all the advances our faculty have made in recent years. We can now describe the entire genome, define the exposome (the environmental analogue to the genome), characterize social interactions and mood via cellphone data, and can digitize historical data relevant for the humanities. ….

DOMINICI: We have launched the Harvard Data Science Postdoctoral Fellowship, which is among the largest programs of its kind, and we want to recruit talented individuals in a highly interdisciplinary ways.

We have also launched a competitive research fund that will catalyze small research projects around the University. Through our friends in the Faculty of Arts and Sciences and the Medical School, we’ve identified some spaces in the near term where people can get together. …

PARKES: We are launching the initiative because we want to get to a point where we have a Harvard Data Science Institute. The aspiration is that the Data Science Institute will have some physical space associated with it,

Then the third one I wanted to mention is privacy.
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http://news.harvard.edu/gazette/story/2017/03/co-directors-of-newly-launched-harvard-data-science-initiative-discuss-new-era/

How statistics lost their power – and why we should fear what comes next | William Davies | Politics | Th e Guardian

January 30, 2017

How stats lost their power via @alexvespi
https://www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy Death of #DataScience in a “post-truth” world; anecdotes v elitist numbers

News Outlets Wonder Where the Predictions Went Wrong – The New York Times

November 12, 2016

News Outlets Wonder Where the Predictions Went Wrong
http://www.nytimes.com/2016/11/10/business/media/news-outlets-wonder-where-the-predictions-went-wrong.html The real loser in the election: #datascience-based polling

Research Parasites

January 23, 2016

Dara sharing http://www.nejm.org/doi/full/10.1056/NEJMe1516564 Deems #datascientists as “research parasites,” using another’s data for their own ends via @dspakowicz

QT:{{”
“A second concern held by some is that a new class of research person will emerge — people who had nothing to do with the design and execution of the study but use another group’s data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited. There is concern among some front-line researchers that the system will be taken over by what some researchers have characterized as “research parasites.””
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10 types of regressions. Which one to use?

December 8, 2015

10 types of #regressions. Which one to use?
http://www.datasciencecentral.com/forum/topics/10-types-of-regressions-which-one-to-use Pitfalls of common approaches, eg linear or logistic via @KirkDBorne

Are Polls Ruining Democracy?

November 29, 2015

Are Polls Ruining Democracy?
http://www.newyorker.com/magazine/2015/11/16/politics-and-the-new-machine “#Datascience is the child of a rocky marriage between the academy & Silicon Valley”

QT:{{”
“If public-opinion polling is the child of a strained marriage between the press and the academy, data science is the child of a rocky marriage between the academy and Silicon Valley. The term “data science” was coined in 1960, one year after the Democratic National Committee hired Simulmatics Corporation, a company founded by Ithiel de Sola Pool, a political scientist from M.I.T., to provide strategic analysis in advance of the upcoming Presidential election. Pool and his team collected punch cards from pollsters who had archived more than sixty polls from the elections of 1952, 1954, 1956, 1958, and 1960, representing more than a hundred thousand interviews, and fed them into a UNIVAC. They then sorted voters into four hundred and eighty possible types (for example, “Eastern, metropolitan,
lower-income, white, Catholic, female Democrat”) and sorted issues into fifty-two clusters (for example, foreign aid). Simulmatics’ first task, completed just before the Democratic National Convention, was a study of “the Negro vote in the North.” Its report, which is thought to have influenced the civil-rights paragraphs added to the Party’s platform, concluded that between 1954 and 1956 “a small but
significant shift to the Republicans occurred among Northern Negroes, which cost the Democrats about 1 per cent of the total votes in 8 key states.” After the nominating convention, the D.N.C. commissioned Simulmatics to prepare three more reports, including one that involved running simulations about different ways in which Kennedy might discuss his Catholicism.”
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NIH approves strategic vision to transform National Library of Medicine

June 17, 2015

Great focus on datascience for the NLMhttp://www.nih.gov/news/health/jun2015/od-11.htm