This document provides an overview of a course on data literacy and ethics in the lab taught by Chris Wiggins and Matt Jones. The course aims to teach skills and knowledge that are not covered in statistics or social science curricula. It pairs technical skills like Python programming with readings on the political and ethical contexts of data and technology.
The course introduces key hypotheses driving the class, provides a student perspective on the first lecture, and discusses approaches to research ethics. It also previews the course structure, which includes lectures, labs using Jupyter notebooks, and discussions on Slack. The goals are to develop multiple literacies, show how capabilities relate to power dynamics, and consider data technologies throughout history and their social impacts.
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
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" 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 (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
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" 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 Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015Big Data Spain
The term 'Data Science' was first described in scientific literature about 15 years ago. It started to become a major trend in industry about 7 years ago.
O'Reilly Media surveys the industry extensively each year. In addition we get a good birds-eye view of industry trends through our conference programs and publications, working closely with some of the best practitioners in Data Science.
By now, the field has evolved far beyond its origins eclipsing an earlier generation of Business Intelligence and Data Warehousing approaches. Data Science is moving up, into the business verticals and government spheres of influence where it has true global impact.
This talk considers Data Science trends from the past three years in particular. What is emerging? Which parts are evolving? Which seem cluttered and poised for consolidation or other change?
Session presented at Big Data Spain 2015 Conference
15th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/thu/slot-2.html
This presentation goes over Data Mining the City, a course taught at Columbia University GSAPP. This lecture also covers, complexity, cybernetics and agent based modeling.
URL: https://professionalschool.eitdigital.eu/generative-ai-essentials
Course on Generative Al
Description:
Generative AI is a world-changing power tool that is getting better by the day. So now is the time to get truly inspired, climb up the learning curve, and unleash more of your creative potential.
Learning Topics:
* Inspiration: What is Generative AI in the context of AI's history, present, and future
* Climbing Up: Ways to accelerate your learning trajectory
* Unleashing Creativity: Ways to stay future-ready in the AI era
What You'll Take Away:
By the end of this session, you'll understand the importance of upskilling with today's generative AI tools to get more work done, both faster and at higher quality, as well as some pitfalls to avoid, all within the broader context of the past, present, and future of Artificial Intelligence (AI) and Intelligence Augmentation (IA).
Learning Topics
Inspiration: What is Generative AI in the context of AI's history, present, and future.
Climbing Up: Ways to accelerate your learning trajectory.
Unleashing Creativity: Ways to stay future-ready in the AI era.
Deep dive into ChatGPT's features.
Techniques for basic and advanced prompting and real-world applications.
November 5, 2023
NHH: FRONT LINES ON ADOPTION OF DIGITAL AND
AI-BASED SERVICES
Thanks to Tor Andreassen for the opportunity
To discuss AI and IA.
Tor Andeassen: https://www.linkedin.com/in/tor-wallin-andreassen-1aa9031/
What does Generative AI mean for public policy?Sam Gilbert
The instant popularity of AI tools like ChatGPT and Stable Diffusion has seen so-called "Generative AI" supplant the metaverse as the hottest trend in tech.
But is the technology really significant, or is it mostly hype?
Focusing on OpenAI's large languages models (LLMs), this presentation by Sam Gilbert to the University of Cambridge's Bennett Institute explores the potential public policy implications of Generative AI -- and how policymakers and policy researchers can use it in their own work.
Competitive intelligence for multimodal data integrationAshley M. Richter
What are some of the areas to watch to determine how things are going and what groups will get there first with respective to innovative multimodal data integration and visualization systems.
vsia gin s ar
A presentation on the value and the risks of identifying, mining, and visualizing data. All this is described in a big-data-aware setting. The presentation is meant for a wide audience, not requiring deep technical background.
The original presentation was done within the KAS Seminar on Data Journalism in Dec 2017.
AI in between online and offline discourse - and what has ChatGPT to do with ...Stefan Dietze
Talk at Bonn University on general AI and NLP challenges in the context of online discourse analysis. Specific focus on challenges arising from the widespread adoption of neural large language models.
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.
More Related Content
Similar to Data: Past, Present, and Future (Cornell Digital Life Seminar on Data Literacy & Ethics in the Lab on February 13th, 2018)
Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015Big Data Spain
The term 'Data Science' was first described in scientific literature about 15 years ago. It started to become a major trend in industry about 7 years ago.
O'Reilly Media surveys the industry extensively each year. In addition we get a good birds-eye view of industry trends through our conference programs and publications, working closely with some of the best practitioners in Data Science.
By now, the field has evolved far beyond its origins eclipsing an earlier generation of Business Intelligence and Data Warehousing approaches. Data Science is moving up, into the business verticals and government spheres of influence where it has true global impact.
This talk considers Data Science trends from the past three years in particular. What is emerging? Which parts are evolving? Which seem cluttered and poised for consolidation or other change?
Session presented at Big Data Spain 2015 Conference
15th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/thu/slot-2.html
This presentation goes over Data Mining the City, a course taught at Columbia University GSAPP. This lecture also covers, complexity, cybernetics and agent based modeling.
URL: https://professionalschool.eitdigital.eu/generative-ai-essentials
Course on Generative Al
Description:
Generative AI is a world-changing power tool that is getting better by the day. So now is the time to get truly inspired, climb up the learning curve, and unleash more of your creative potential.
Learning Topics:
* Inspiration: What is Generative AI in the context of AI's history, present, and future
* Climbing Up: Ways to accelerate your learning trajectory
* Unleashing Creativity: Ways to stay future-ready in the AI era
What You'll Take Away:
By the end of this session, you'll understand the importance of upskilling with today's generative AI tools to get more work done, both faster and at higher quality, as well as some pitfalls to avoid, all within the broader context of the past, present, and future of Artificial Intelligence (AI) and Intelligence Augmentation (IA).
Learning Topics
Inspiration: What is Generative AI in the context of AI's history, present, and future.
Climbing Up: Ways to accelerate your learning trajectory.
Unleashing Creativity: Ways to stay future-ready in the AI era.
Deep dive into ChatGPT's features.
Techniques for basic and advanced prompting and real-world applications.
November 5, 2023
NHH: FRONT LINES ON ADOPTION OF DIGITAL AND
AI-BASED SERVICES
Thanks to Tor Andreassen for the opportunity
To discuss AI and IA.
Tor Andeassen: https://www.linkedin.com/in/tor-wallin-andreassen-1aa9031/
What does Generative AI mean for public policy?Sam Gilbert
The instant popularity of AI tools like ChatGPT and Stable Diffusion has seen so-called "Generative AI" supplant the metaverse as the hottest trend in tech.
But is the technology really significant, or is it mostly hype?
Focusing on OpenAI's large languages models (LLMs), this presentation by Sam Gilbert to the University of Cambridge's Bennett Institute explores the potential public policy implications of Generative AI -- and how policymakers and policy researchers can use it in their own work.
Competitive intelligence for multimodal data integrationAshley M. Richter
What are some of the areas to watch to determine how things are going and what groups will get there first with respective to innovative multimodal data integration and visualization systems.
vsia gin s ar
A presentation on the value and the risks of identifying, mining, and visualizing data. All this is described in a big-data-aware setting. The presentation is meant for a wide audience, not requiring deep technical background.
The original presentation was done within the KAS Seminar on Data Journalism in Dec 2017.
AI in between online and offline discourse - and what has ChatGPT to do with ...Stefan Dietze
Talk at Bonn University on general AI and NLP challenges in the context of online discourse analysis. Specific focus on challenges arising from the widespread adoption of neural large language models.
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 .
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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.
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https://www.eventbrite.com/e/data-science-at-the-new-york-times-tickets-19490272931
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workshop: https://icerm.brown.edu/topical_workshops/tw15-6-mds/
references: http://bit.ly/icerm
presentation for the NYCRIN / NSF meeting 2013-07-24,
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open source tools to quantify and drive early stage startups,
with support from NSF award 1305023
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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.
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Data: Past, Present, and Future (Cornell Digital Life Seminar on Data Literacy & Ethics in the Lab on February 13th, 2018)
1. Data Literacy
and
Ethics in the Lab
Chris Wiggins + Matt Jones
data-ppf.github.io
Course supported by Collaboratory Fellows Fund, Columbia University
(talk presented 2018-02-13 at Digital Life Seminar, Cornell Tech)
2. overview
1. hypotheses driving the class
2. student-eye view of the course (following “lecture 1”)
3. ethics
a. defining vs enforcing
b. research vs industry: two institutional moments
c. curricula extant vs curricula needed
4. show and tell:
a. syllabus/readings
b. paired Python/Jupyter notebooks (readings on monday; Python on wednesdays)
c. Slack (not just for discussions)
4. hypotheses driving the class
1. there is important material being taught neither to future statisticians nor to
future senators
a. outside the technical canon yet also
b. present only at the advanced level in STS, to our knowledge unpaired with technical
engagement
2. multicapabilities [1] are teachable without prerequisite
a. functional: via pre-authored Jupyter notebooks, as in-class labs
b. rhetorical: in-class labs as well as discussion
c. critical: discussions + readings as well as in-class labs
3. pair intellectual changes with political and ethical context
a. what powers motivated this advance?
b. how did this advance rearrange power? (cf. Rogaway) [2]
[1] cf. Selber, S. (2004). Multiliteracies for a digital age. SIU Press.
[2] Rogaway, P. (2015). The Moral Character of Cryptographic Work. IACR Cryptology ePrint Archive, 2015, 1162
5. hypotheses driving the class
1. there is important material being taught neither to future statisticians nor to
future senators
a. outside the technical canon yet also
b. present only at the advanced level in STS, to our knowledge unpaired with technical
engagement
2. multicapabilities [1] are teachable without prerequisite
a. functional: via pre-authored Jupyter notebooks, as in-class labs
b. rhetorical: in-class labs as well as discussion
c. critical: discussions + readings as well as in-class labs
3. pair intellectual changes with political and ethical context
a. what powers motivated this advance?
b. how did this advance rearrange power? (cf. Rogaway) [2]
[1] cf. Selber, S. (2004). Multiliteracies for a digital age. SIU Press.
[2] Rogaway, P. (2015). The Moral Character of Cryptographic Work. IACR Cryptology ePrint Archive, 2015, 1162
17. how did this end up in my news feed?
- math
- hardware
- system
- funding
- market
- regulation
- data
this was not possible 20 years ago.
- why?
- what did people do instead?
23. “Automated Inference on Criminality using Face Images”
(arXiv:1611.04135v1)
“In all cultures and all
periods of recorded human
history, people share the
belief that the face alone
suffices to reveal innate
traits of a person.”
24. We’ve been here before
J Am Inst. Criminal Law, 1912, on Lombroso, 1899
28. Statistical sciences always political
Dream of sciences of social difference
central to development of
statistics
and the
data sciences
29. Florence Nightingale
& Data Visualization
“Experience has shown that
without special information and
skilful application of the resources
of science in preserving health,
the drain on our home population
must exhaust our means. The
introduction, therefore, of a proper
sanitary system into the British
army is of essential importance to
the public interests.” (1858)
30. Florence Nightingale
& Data Visualization
“Upon the British race alone the
integrity of that empire at this
moment appears to depend. The
conquering race must retain
possession.” (1858)
31. Every week:
Scientific and mathematical development
Technologies and engineering
Driving forces: money, prestige, resources, Imperial competition
Power, ethics, and data intensive knowledge
32. Tech story: three chronological stages
Data and Math
Data and Engineering
Data and Technology
33. Data technologies
Census and government survey
Information processing machines and
digital computers
Always on network infrastructure
34. Power
How should social and political order be organized on basis
of science and engineering?
How do technologies transform the social and political order?
How do technologies augment and diminish democratic orders? Autocratic ones?
35. Power and politics*
New technologies mean new capabilities.
These capabilities are first available to those in power
(cf. “The future is already here — it's just not very evenly distributed.” --Gibson)
- How does this distribution of capability reorder power?
- How are data-empowered algorithms an example of this dynamic
■ of capability, and
■ of reinforcing or distributing power?
* politics here meaning the dynamics of power, not to be confused with “voting”
46. Experimental design,
hypothesis tests, and
decision theory
“To play this game with the greatest chance of
success, the experimenter cannot afford to exclude
the possibility of any possible arrangement of soil
fertilities, and his best strategy is to equalize the
chance that any treatment shall fall on any plot by
determining it by chance himself.”
- Joan Fisher
47. World War 2: Turing and statistical cryptography
55. Weekly structure
Monday
Lecture and discussion
Expectation
arrive having done the week’s readings
Wednesday
Laboratory
Expectation
arrive with laptop ready to collaborate
57. Two tracks
more technical background track (60%)
● pursue a semester long project
culminating in a 15pp paper and any
associated code
● complete 3 problem sets
● short final presentation on paper
more humanistic background track (60%)
● write a 10 pp paper on a topic of their
choice
● complete 5 problem sets, these problem
sets will involve both computational work
and writing work
● short final presentation on paper
61. ethics
1. placement of ethics within multicapabilities:
Q: integrate or separate?
A: our approach: lead up to, i.e., foreshadow throughout, via shock and awe, then provide framing
62. ethics
2. A curriculum / ideational setting for:
- granularities
- belmont, menlo park [1]
common rule / IRB
- users+ products
- society+markets [2]
three-party game (including
law/regulation) [3]
define v. enforceindustryv.“research”
[1] Salganik, M. J. (2017). Bit by bit: social research in the digital age. Princeton University Press.
[2] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information.
[3] Janeway, W. H. (2012). Doing capitalism in the innovation economy: markets, speculation and the state.
63. defining via granularities: principles->standards->rules [1]
1. Respect for Persons:
- informed consent; respect for individuals’ autonomy and individuals impacted;
- protection for individuals with diminished autonomy or decision making capability.
2. Beneficence: do not harm; assess risk.
3. Justice: fair distribution of benefits of research; selection of subjects; and allocation of
burdens.
4. Respect for Law and Public Interest:
- legal due diligence;
- transparency in methods and results;
- accountability.
[1] Solum, L. B (2009). Legal theory lexicon: Rules, standards, and principles (blog post).
[2] Dittrich, D., & Kenneally, E. (2012). The Menlo Report: Ethical principles guiding information and
communication technology research. US Department of Homeland Security.
64. 4. show and tell:
a. syllabus
b. paired Python/Jupyter notebooks
c. Slack
65. recap
1. hypotheses driving the class
2. student-eye view of the course (following “lecture 1”)
3. what we talk about when we talk about ethics
a. defining vs enforcing
b. two industrial moments: Research vs industry
c. curricula extant, curricula needed
4. show and tell:
a. syllabus/readings
b. paired Python/Jupyter notebooks (readings on monday; Python on wednesdays)
c. Slack (not just for discussions)
66. for more info, code, syllabus, etc. please see
“data: past present and future” course page
data-ppf.github.io
Course supported by Collaboratory Fellows Fund, Columbia University
68. responses: uses of history
1. “make the present strange” (- J. Grimmelmann)
- emphasize the diverse ways of thinking about the problem from
before it was “settled”: help us see what could have been?
- make human by revealing the human interests and conflicts
2. provides a new way into the technical materials which doesn’t
presume or advantage a particular prior curriculum
3. provides window into the ethical questions
- distance us from our “settled” narrative via the past
- show debates that are different now (e.g., eugenics debates)