Talk delivered 2019-06-25 as part of the Summer Institute in Computational Social Science, held at Princeton University https://compsocialscience.github.io/summer-institute/2019/
Video: https://www.youtube.com/watch?v=0suLWheVji0
title:
what should future statisticians, CEO, and senators know about the
history and ethics of data?
abstract:
What should our future statisticians, senators, and CEOs know about the history and ethics of data?
How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms?
I'll present content from a class co-developed over the past several years with Professor Matt Jones of Columbia's Department of History, based on material absent from both the curriculum for future technologists as well as for future humanists.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Data: Past, Present, and Future (Lecture 1, Spring 2018)chris wiggins
Slides from Lecture 1 of "Data: Past, Present, and Future",
Jan 17 2018.
New class on how data is impacting our professional, political, and personal realities. Taught by Profs Matt Jones and Chris Wiggins
"data: past, present, and future" day 1 lecture 2020-01-20chris wiggins
What should our future statisticians, senators, and CEOs know about the history and ethics of data? How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms? We've developed a course that introduces students, without prerequisites, to a historical view of our present condition, in which data-empowered algorithms shape our personal, professional, and political realities. The course attempts to integrate critical data studies with functional engagement with data (in Python via Jupyter notebooks), and interleaves an applied view of ethics throughout. The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Data: Past, Present, and Future (Cornell Digital Life Seminar on Data Literac...chris wiggins
Data-empowered algorithms are reshaping our professional, personal, and political realities.
However, existing curricula are predominantly designed either for future technologists, focusing on functional capabilities; or for future humanists, focusing on critical and rhetorical context surrounding data.
"Data: Past, Present, and Future" is a new course at Columbia which seeks to define a curriculum at present taught to neither group, yet of interest and utility to future statisticians, CEOs, and senators alike.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
The weekly cadence of the course pairs primary and secondary readings with Jupyter notebooks in Python, engaging directly with the data and intellectual advances under study.
Throughout, these intellectual technical advances are paired with critical inquiry into the forces which encouraged and benefited from these new capabilities, i.e., the political dimension of data and technology.
Syllabus, Jupyter notebooks, and additional info can be found via https://data-ppf.github.io/
"Data: Past, Present, and Future" is supported by the Columbia University Collaboratory Fellows Fund. Jointly founded by Columbia University’s Data Science Institute and Columbia Entrepreneurship, The Collaboratory@Columbia is a university-wide program dedicated to supporting collaborative curricula innovations designed to ensure that all Columbia University students receive the education and training that they need to succeed in today’s data rich world.
data science history / data science @ NYTchris wiggins
talk delivered 2015-07-29 at ICERM workshop on "mathematics in data science"
workshop: https://icerm.brown.edu/topical_workshops/tw15-6-mds/
references: http://bit.ly/icerm
Data: Past, Present, and Future (Lecture 1, Spring 2018)chris wiggins
Slides from Lecture 1 of "Data: Past, Present, and Future",
Jan 17 2018.
New class on how data is impacting our professional, political, and personal realities. Taught by Profs Matt Jones and Chris Wiggins
"data: past, present, and future" day 1 lecture 2020-01-20chris wiggins
What should our future statisticians, senators, and CEOs know about the history and ethics of data? How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms? We've developed a course that introduces students, without prerequisites, to a historical view of our present condition, in which data-empowered algorithms shape our personal, professional, and political realities. The course attempts to integrate critical data studies with functional engagement with data (in Python via Jupyter notebooks), and interleaves an applied view of ethics throughout. The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Data: Past, Present, and Future (Cornell Digital Life Seminar on Data Literac...chris wiggins
Data-empowered algorithms are reshaping our professional, personal, and political realities.
However, existing curricula are predominantly designed either for future technologists, focusing on functional capabilities; or for future humanists, focusing on critical and rhetorical context surrounding data.
"Data: Past, Present, and Future" is a new course at Columbia which seeks to define a curriculum at present taught to neither group, yet of interest and utility to future statisticians, CEOs, and senators alike.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
The weekly cadence of the course pairs primary and secondary readings with Jupyter notebooks in Python, engaging directly with the data and intellectual advances under study.
Throughout, these intellectual technical advances are paired with critical inquiry into the forces which encouraged and benefited from these new capabilities, i.e., the political dimension of data and technology.
Syllabus, Jupyter notebooks, and additional info can be found via https://data-ppf.github.io/
"Data: Past, Present, and Future" is supported by the Columbia University Collaboratory Fellows Fund. Jointly founded by Columbia University’s Data Science Institute and Columbia Entrepreneurship, The Collaboratory@Columbia is a university-wide program dedicated to supporting collaborative curricula innovations designed to ensure that all Columbia University students receive the education and training that they need to succeed in today’s data rich world.
data science history / data science @ NYTchris wiggins
talk delivered 2015-07-29 at ICERM workshop on "mathematics in data science"
workshop: https://icerm.brown.edu/topical_workshops/tw15-6-mds/
references: http://bit.ly/icerm
Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 17, 2014
Professors Gieseking & Gaze
Lecture Slides "On Digital Publics of Opening…or Not"
Slides from guest lecture by Drew Endy given at the Cold Spring Harbor Laboratories 2017 Synthetic Biology Course. The slides reprise what's happened in synthetic biology over the past 15 years (using parochial examples) and then poses two questions -- what's most needed (to advance synthetic biology over the next 10-15 years)? And, why? I.e., to what ends? Students were challenged to imagine what five new truths for synthetic biology would they write, if they were writing such an article themselves, today.
Roger hoerl say award presentation 2013Roger Hoerl
Presentation given by Roger Hoerl when he received the Statistical Advocate of the Year Award from the Chicago Section of the American Statistical Association (ASA), May 9th, 2013.
data science @NYT ; inaugural Data Science Initiative Lecturechris wiggins
inaugural Data Science Initiative Lecture @ Brown University
2015-12-04
https://www.eventbrite.com/e/data-science-at-the-new-york-times-tickets-19490272931
Essay Writing Online. . Step-By-Step Guide to Essay Writing - ESL BuzzKari Wilson
Write an Essay Online - Online Custom Essay Writing Service 24/7. College Essay Format: Simple Steps to Be Followed. Free Online Essay Writing Tutorials - Learn to write essay online free .... 10 Tips to Write an Essay and Actually Enjoy It. Write Essay Free Online / How to Write a Remarkable Essay Infographic ....
Scott Edmunds slides from class 7 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering open data policy and practice, and the Hong Kong context.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 10, 2014
Professors Gieseking & Gaze
Lecture Slides "Defining Data & Redefining Privacy"
Environmental Protection Essays. College Essay: Essay environmental protectionHannah Davis
Essay on Environment Protection (1000+ words) - EnglishGrammarSoft. Importance of Environmental Protection Essay | Pollution | Global Warming. Business paper: Essay about environment protection. Environmental studies and forestry sample essay. Protecting an Environment Essay Example | Topics and Well Written .... Introduction to Environmental Protection. Environmental Protection Essay | Essay on Save Environment for Students .... Essay sample how we can help to protect the environment. Argumentative Essay: Environment protection essay. College essay: Essay environmental protection. 016 Water Pollution Essay Example On In Urdu Worksheet Printables Site .... Write Greater Essays with One of These Tips — Uncomplicated Techniques .... Different Aspects of Environmental Protection Essay Example .... How Can We Protect Our Environment Essay In English | Sitedoct.org. Ways To Protect The Environment Essay - How We Can Protect The .... Global Environmental Protection Essay | Legal Studies - Year 12 HSC .... Best Essay Writers Here - natural disaster essay - 2017/10/02. Essay environmental protection need for hour. FREE Protection of the .... Write a short essay on How To Protect The Environment | Essay on .... Conservation Of Environment Essay – Telegraph. Environment Protection Essay - Study-Phi. How Can We Protect Our Environment Essay | Sitedoct.org. College Essay: Essay environmental protection. Pay for Essay and Get the Best Paper You Need - environmental .... Environmental essay - inhisstepsmo.web.fc2.com. Environmental Protection Assignment Example | Topics and Well Written .... The Environmental Protection Agency Free Essay Example.
Quiz 3 - SPRING 2016 - MATH 107 - COLLEGE ALGEBRAQuiz 3 .docxmakdul
Quiz 3 - SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
Quiz 3
Problem 1:
Graph f (x) = x2 − 5x + 4
Problem 2:
Graph the following function:
f (x) =
{
4
x
− x2
if
if x <
x ≥
1
1
Problem 3:
Graph the function f(x) =
√
x, and use this plot to graph the function g(x) =
√
x + 1
Problem 4:
Graph the function f(x) = (x−1)2, and use this plot to graph the function
g(x) = (2x−1)2
Problem 5:
Find the slope of the line containing the following pair of points:
P(1,−1), Q(3,−3)
Problem 6:
Write the equation of the line containing the points (0, 1) and (2, 3)
SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
Quiz 3 - SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
Problem 7:
Solve the following equation:
2−|x + 4| = 1
Problem 8:
Graph the following function:
f(x) = |x + 2|
Problem 9:
Find the least squares regression line, and graph both the scatter plot and the regression
line, for the following set of data:
x 1 2 3 4 5 6 7 8 9
y 1.367 1.548 3.172 4.064 4.739 5.913 7.069 8.716 9.554
Problem 10:
Find the least squares regression line, and graph both the scatter plot and the regression
line, for the following set of data:
x 1 2 3 4 5 6 7 8 9
y 2.865 5.304 7.073 8.994 11.072 12.980 14.988 17.149 19.141
SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
ROBERT M. BOHM
University of Central Florida
and
BRENDA L. VOGEL
California State University, Long Beach
A Primer on Crime and
Delinquency Theory
T H I R D E D I T I O N
Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or
eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights
restrictions require it.
R
O
D
D
Y
,
A
N
T
H
O
N
Y
I
S
A
A
C
3
7
2
7
B
U
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or
eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights
restrictions require it.
This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may
be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall
learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights
restrictions require it. For valuable information on pricing, previous editio ...
a mission-driven approach to personalizing the customer journeychris wiggins
Keynote talk at PyData NYC 2019 by Anne Bauer, Lead Data Scientist, The New York Times, and Chris Wiggins, Chief Data Scientist, The New York Times.
"Data science at The New York Times: a mission-driven approach to personalizing the customer journey"
How does The New York Times use data science to further its mission?
We'll talk about the use of machine learning throughout the company,
from social media promotion to targeted advertising to content
recommendations, and the cross-team collaborations that make it
possible.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 17, 2014
Professors Gieseking & Gaze
Lecture Slides "On Digital Publics of Opening…or Not"
Slides from guest lecture by Drew Endy given at the Cold Spring Harbor Laboratories 2017 Synthetic Biology Course. The slides reprise what's happened in synthetic biology over the past 15 years (using parochial examples) and then poses two questions -- what's most needed (to advance synthetic biology over the next 10-15 years)? And, why? I.e., to what ends? Students were challenged to imagine what five new truths for synthetic biology would they write, if they were writing such an article themselves, today.
Roger hoerl say award presentation 2013Roger Hoerl
Presentation given by Roger Hoerl when he received the Statistical Advocate of the Year Award from the Chicago Section of the American Statistical Association (ASA), May 9th, 2013.
data science @NYT ; inaugural Data Science Initiative Lecturechris wiggins
inaugural Data Science Initiative Lecture @ Brown University
2015-12-04
https://www.eventbrite.com/e/data-science-at-the-new-york-times-tickets-19490272931
Essay Writing Online. . Step-By-Step Guide to Essay Writing - ESL BuzzKari Wilson
Write an Essay Online - Online Custom Essay Writing Service 24/7. College Essay Format: Simple Steps to Be Followed. Free Online Essay Writing Tutorials - Learn to write essay online free .... 10 Tips to Write an Essay and Actually Enjoy It. Write Essay Free Online / How to Write a Remarkable Essay Infographic ....
Scott Edmunds slides from class 7 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering open data policy and practice, and the Hong Kong context.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 10, 2014
Professors Gieseking & Gaze
Lecture Slides "Defining Data & Redefining Privacy"
Environmental Protection Essays. College Essay: Essay environmental protectionHannah Davis
Essay on Environment Protection (1000+ words) - EnglishGrammarSoft. Importance of Environmental Protection Essay | Pollution | Global Warming. Business paper: Essay about environment protection. Environmental studies and forestry sample essay. Protecting an Environment Essay Example | Topics and Well Written .... Introduction to Environmental Protection. Environmental Protection Essay | Essay on Save Environment for Students .... Essay sample how we can help to protect the environment. Argumentative Essay: Environment protection essay. College essay: Essay environmental protection. 016 Water Pollution Essay Example On In Urdu Worksheet Printables Site .... Write Greater Essays with One of These Tips — Uncomplicated Techniques .... Different Aspects of Environmental Protection Essay Example .... How Can We Protect Our Environment Essay In English | Sitedoct.org. Ways To Protect The Environment Essay - How We Can Protect The .... Global Environmental Protection Essay | Legal Studies - Year 12 HSC .... Best Essay Writers Here - natural disaster essay - 2017/10/02. Essay environmental protection need for hour. FREE Protection of the .... Write a short essay on How To Protect The Environment | Essay on .... Conservation Of Environment Essay – Telegraph. Environment Protection Essay - Study-Phi. How Can We Protect Our Environment Essay | Sitedoct.org. College Essay: Essay environmental protection. Pay for Essay and Get the Best Paper You Need - environmental .... Environmental essay - inhisstepsmo.web.fc2.com. Environmental Protection Assignment Example | Topics and Well Written .... The Environmental Protection Agency Free Essay Example.
Quiz 3 - SPRING 2016 - MATH 107 - COLLEGE ALGEBRAQuiz 3 .docxmakdul
Quiz 3 - SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
Quiz 3
Problem 1:
Graph f (x) = x2 − 5x + 4
Problem 2:
Graph the following function:
f (x) =
{
4
x
− x2
if
if x <
x ≥
1
1
Problem 3:
Graph the function f(x) =
√
x, and use this plot to graph the function g(x) =
√
x + 1
Problem 4:
Graph the function f(x) = (x−1)2, and use this plot to graph the function
g(x) = (2x−1)2
Problem 5:
Find the slope of the line containing the following pair of points:
P(1,−1), Q(3,−3)
Problem 6:
Write the equation of the line containing the points (0, 1) and (2, 3)
SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
Quiz 3 - SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
Problem 7:
Solve the following equation:
2−|x + 4| = 1
Problem 8:
Graph the following function:
f(x) = |x + 2|
Problem 9:
Find the least squares regression line, and graph both the scatter plot and the regression
line, for the following set of data:
x 1 2 3 4 5 6 7 8 9
y 1.367 1.548 3.172 4.064 4.739 5.913 7.069 8.716 9.554
Problem 10:
Find the least squares regression line, and graph both the scatter plot and the regression
line, for the following set of data:
x 1 2 3 4 5 6 7 8 9
y 2.865 5.304 7.073 8.994 11.072 12.980 14.988 17.149 19.141
SPRING 2016 - MATH 107 - COLLEGE ALGEBRA
ROBERT M. BOHM
University of Central Florida
and
BRENDA L. VOGEL
California State University, Long Beach
A Primer on Crime and
Delinquency Theory
T H I R D E D I T I O N
Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or
eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights
restrictions require it.
R
O
D
D
Y
,
A
N
T
H
O
N
Y
I
S
A
A
C
3
7
2
7
B
U
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or
eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights
restrictions require it.
This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may
be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall
learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights
restrictions require it. For valuable information on pricing, previous editio ...
a mission-driven approach to personalizing the customer journeychris wiggins
Keynote talk at PyData NYC 2019 by Anne Bauer, Lead Data Scientist, The New York Times, and Chris Wiggins, Chief Data Scientist, The New York Times.
"Data science at The New York Times: a mission-driven approach to personalizing the customer journey"
How does The New York Times use data science to further its mission?
We'll talk about the use of machine learning throughout the company,
from social media promotion to targeted advertising to content
recommendations, and the cross-team collaborations that make it
possible.
Data Science at The New York Times: what industry can learn from us; what we ...chris wiggins
Keynote talk at RSG with DREAM 2019 | November 4-6, 2019 | New York, USA | HOME - RECOMB/ISCB RSG 2019; 12th annual RECOMB/ISCB Conference on Regulatory & Systems Genomics .
pecial Session on Cancer Systems Biology
Regulatory and Systems Genomics 2019 will include an abstract submissions track for a Special Session of Cancer Systems We welcome submissions on computational and experimental advances in the systems-level modeling of cancer. Topics include but are not limited to: regulatory programs and signaling pathways in cancer cells, tumor-immune interactions and the tumor microenvironment, developmental plasticity in tumors and epigenetic analyses, tumor metabolism, genetic and non-genetic sources of heterogeneity, drug response and precision oncology. The session will include presentations from keynote speakers as well as talks from selected abstracts. This special session is sponsored by the Research Center for Cancer Systems Immunology at Memorial Sloan Kettering Cancer Center, an NCI-funded Cancer Systems Biology Consortium (CSBC) Center.
slides uploaded by request
talk presented at the MIDAS seminar, University of Michigan, 2019-04-15. Video available via https://www.youtube.com/watch?v=c7t4LMkq_SU . For more information: https://midas.umich.edu/event/chris-wiggins/
abstract
The Data Science group at The New York Times develops and deploys
machine learning solutions to newsroom and business problems.
Re-framing real-world questions as machine learning tasks requires not
only adapting and extending models and algorithms to new or special
cases but also sufficient breadth to know the right method for the
right challenge. I'll first outline how unsupervised, supervised, and
reinforcement learning methods are increasingly used in human
applications for description, prediction, and prescription,
respectively. I'll then focus on the 'prescriptive' cases, showing how
methods from the reinforcement learning and causal inference
literatures can be of direct impact in engineering, business, and
decision-making more generally.
lean + design thinking in building data productschris wiggins
talk given to "Columbia startup teams including 2016 CVC winners, Ignition Grant winners, TFP fellows, and ASCENT fellows" as part of a mini-bootcamp for founders at columbia's engineering school 2016-05-24
presentation for the NYCRIN / NSF meeting 2013-07-24,
showing 1st demo of leanworkbench.com,
open source tools to quantify and drive early stage startups,
with support from NSF award 1305023
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
1. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
chris.wiggins@columbia.edu
& &
chris.wiggins@nytimes.com
chris.wiggins@hackNY.org
@chrishwiggins
data-ppf.github.io
2. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
chris.wiggins@columbia.edu
& &
chris.wiggins@nytimes.com
chris.wiggins@hackNY.org
@chrishwiggins
data-ppf.github.io
3. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
chris.wiggins@columbia.edu
& &
joint work with:
Matt Jones
Department of History, Columbia
@nescioquid
data-ppf.github.io
7. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
1. why history?
2. why ethics?
3. what we taught
8. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
1. why history?
2. why ethics?
3. what we taught
4. what we learned
9. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
10. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
16. 0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
what should future
statisticians CEOs, and senators
know about the history and ethics of data?
26. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
31. 1. why ethics?
2017-09-05: cathy o’neil
2018-01-08: safiya noble
2018-01-23: virginia eubanks
something is wrong on the internet
32. 1. why ethics?
2017-09-05: cathy o’neil
2018-01-08: safiya noble
2018-01-23: virginia eubanks
2019-01-15:
shoshana zuboff
(among increasingly many others)
something is wrong on the internet
35. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
46. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
47. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
48. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
49. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
2. harms
50. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
2. harms
3. justice
51. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
2. harms
3. justice
(week 1 & 2 had plenty of harms+injustice)
53. 1 intro
2 setting the stakes
3 risk and social physics
4 statecraft and quantitative racism
5 intelligence, causality, and policy
6 data gets real: mathematical baptism
7 WWII, dawn of digital computation
8 birth and death of AI
9 big data, old school (1958-1980)
10 data science, 1962-2017
11 AI2.0
12 ethics
13 present problems & VC-backed attention economy
14 future solutions
github.com/data-ppf/data-ppf.github.io/wiki/Syllabus
3. what we taught: 14 weeks: Tuesday discussion
54. 3. what we taught: 14 weeks: Thursday Labs
github.com/data-ppf/data-ppf.github.io/wiki/Syllabus
55. 1. first steps in Python interrogating the UCI dataset
2. EDA with the UCI dataset
3. Quetelet and GPAs
4. Galton
5. statistics and society; Yule, Spearman, Simpson
6. p-hacking; Fisher
7. the first data science
8. AI 1.0; Expert systems; Perceptron
9. databases and recsys; the Netflix Prize story
10. trees along with in-lab lecture on trees
11. interactive: 3 ML’s; FAT 1.0 disparate impact, disparate
treatment, and COMPAS
12. normative+technical approaches to defining and defending
privacy; our own database of ruin: constructing and de-
identifying; FAT 2.0 featuring ToS/EULAs
13. problems along with in-lab lecture on NSA history
14. solutions
3. what we taught: 14 weeks: Thursday Labs
github.com/data-ppf/data-ppf.github.io/wiki/Syllabus
56. 1 intro
2 setting the stakes
3 risk and social physics
4 statecraft and quantitative racism
5 intelligence, causality, and policy
6 data gets real: mathematical baptism
7 WWII, dawn of digital computation
8 birth and death of AI
9 big data, old school (1958-1980)
10 data science, 1962-2017
11 AI2.0
12 ethics
13 present problems & VC-backed attention economy
14 future solutions
2. why ethics?
3. what we taught: 14 weeks: Tuesdays
4. what we learned
90. e.g., week 9 “big data” + privacy 1950-1980
- 1967: FOIA
- 1970: Social Security Number Task Force
- 1970: Fair Credit Reporting Act
- 1973: Watergate hearings
- 1974: Privacy Act
- 1975: "Church" (Select Committee to Study
Governmental Operations with Respect to Intelligence
Activities of the United States Senate)
- 1975: Rockefeller Commission
- 1975: Pike Committee
- 1974: The Family Educational Rights and Privacy Act
111. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
112. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1. articulate principles
2. articulate tensions among them
3. design to support them
- interaction design
- process design
- (in this case, the IRB process)
113. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
114. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
115. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
116. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1. articulate ethics as principles
2. articulate tensions among them
3. articulate design to support them
5.
134. e.g., week 12 the ethics of data
Belmont principles
135. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
136. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
137. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
138. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
139. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
140. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
- legal-> fair, e.g., “veil of ignorance”
141. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
- legal-> fair, e.g., “veil of ignorance”
142. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
- legal-> fair, e.g., “veil of ignorance”
gives analytical, hierarchical, durable framework
for ethical audit of decisions,
from which rules, code, “design” should derive
143. e.g., week 12 the ethics of data
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
144. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
145. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
146. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
147. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
4. Develop a stable list of non-arbitrary of standards where
the selection of certain values, ethics and rights over
others can be plausibly justified.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
148. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
4. Develop a stable list of non-arbitrary of standards where
the selection of certain values, ethics and rights over
others can be plausibly justified.
5. Ensure that ethics do not substitute fundamental rights or
human rights.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
149. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
4. Develop a stable list of non-arbitrary of standards where
the selection of certain values, ethics and rights over
others can be plausibly justified.
5. Ensure that ethics do not substitute fundamental rights or
human rights.
6. Provide a clear statement on the relationship between the
commitments made and existing legal or regulatory
frameworks, in particular on what happens when the two are
in conflict.”
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).
150. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
151. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
152. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
153. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
154. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
155. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
156. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
- interaction design
157. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
- interaction design
- process design
158. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
- interaction design
- process design
- (in this case, the IRB process)
159. e.g., week 12 define + design for ethics
“The Commission’s deliberations on Institutional Review Boards began
with the premise that investigators should not have sole
responsibility for determining whether research involving human
subjects fulfills ethical standards. Others who are independent of
the research must share this responsibility, because investigators
have a potential conflict by virtue of their concern with the pursuit
of knowledge as well as the welfare of the human subjects of their
research.”
1978-09-01 IRB recommendation
160. e.g., week 12 define + design for ethics
reminder: “design is the intentional
solution to a problem within a set of
constraints.” — Mike Monteiro
“The Commission’s deliberations on Institutional Review Boards began
with the premise that investigators should not have sole
responsibility for determining whether research involving human
subjects fulfills ethical standards. Others who are independent of
the research must share this responsibility, because investigators
have a potential conflict by virtue of their concern with the pursuit
of knowledge as well as the welfare of the human subjects of their
research.”
1978-09-01 IRB recommendation
161. e.g., week 12 define + design for ethics
reminder: “design is the intentional
solution to a problem within a set of
constraints.” — Mike Monteiro
“The Commission’s deliberations on Institutional Review Boards began
with the premise that investigators should not have sole
responsibility for determining whether research involving human
subjects fulfills ethical standards. Others who are independent of
the research must share this responsibility, because investigators
have a potential conflict by virtue of their concern with the pursuit
of knowledge as well as the welfare of the human subjects of their
research.”
1978-09-01 IRB recommendation
162. e.g., week 12 ethics lab:
- database of ruin
- k-anonymity
- terms of service
kdnuggets.com (2014):
“Big Data Comic Explains the Current State of Privacy”
183. e.g., week 14 (future) solutions
2017-10-15 (FT) “privacy has become a competitive
advantage.”
2015-10-01, APPL: “privacy is a fundamental human right”
2019-04-28 FB: “The future is private”
2019-02-07 CSCO: “privacy is a fundamental human right”
194. 0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
what should future
statisticians CEOs, and senators
know about the history and ethics of data?
195. 4. what we learned
1. history + ethics:
how to integrate throughout a “tech” education
2. draw parallels to today
3. capabilities rearrange power
4. story of “data” is story of truth+power
- contested
5. find the future by analyzing
- present contests
- present powers
196. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
data-ppf.github.io
“ethics”
define & design
chris.wiggins@columbia.edu
@chrishwiggins