
Machine Eatable was a monthly lunch series I curated with Jeanne Brooks from 2015 – 2017 at Civic Hall.
It was a candid discussion led by community leaders on the front lines of data science for civic good. It was supported by DataKind (Jeanne’s employer at the time), and Microsoft Tech & Civic Engagement (mine). The format was anti-panel: we get a few smart people up front, and 45-60 other smart people in the audience, and they get to talk to one another.
I’m really proud of the incredible speakers we got for such a low-key (and ridiculously-named) series, and how early they were on topics that became absolutely essential over the following decade:
- Designing data partnerships: how can big companies and researchers work together to share useful data? With Mitul Desai and Anoush Tatevossian. Machine Eatable, New York, NY. 2015.
- Data as a Process: How does our attitude towards data change if we see it as the result of a relationship rather than an end in itself? With Mimi Onuoha. Machine Eatable, New York, NY. 2015.
- Interrogating Algorithms: how do we understand what’s going on inside the black box? With Cathy O’Neil, Meredith Broussard, and Solon Barocas. Machine Eatable, New York, NY. 2015.
- Best Practices in Pro Bono Data Science Project Scoping and Execution with Raluca Dragusanu and Susan Sun. Machine Eatable, New York, NY. 2016.
- Citizen-Led Social Experiments in Democratic Society with J. Nathan Matias. Machine Eatable, New York, NY. 2016.
- Discovering a path in data science and how machine learning can counter harmful speech online with Warren Reed. Machine Eatable, New York, NY. 2016.
- Computation and/as Journalism with Mark Hansen. Machine Eatable, New York, NY. 2016.
- What happens when you apply machine learning to social sciences? With Hanna Wallach. Machine Eatable, New York, NY. 2016.
- Data, technology and refugees: the benefits and dangers of the new infrastructure for movement, with Mark Latonero and Paula Kift. Machine Eatable, New York, NY. 2016.
- Illustrating Data Literacy with Mimi Onuoha and Zara Rahman. Machine Eatable, New York, NY. 2017.
- When Recommendation Systems Go Bad with Evan Estola. Machine Eatable, New York, NY. 2017.
- The journey from analytics to editorial with Samarth Bhaskar and Hannah Poferl. Machine Eatable, New York, NY. 2017.
- Rescue Government Data with Liz Barry, Sarah Lamdan, Bronwen Densmore, Bethany Wiggins, Laurie Allen, Jerome Whitington, and Brendan O’Brian. Machine Eatable, New York, NY. 2017.
Always pleasant to be #machineeatable, like a great Personal Democracy Forum session monthly – @mstem & @jmfbrooks: pic.twitter.com/ja9jldWttO
— David Moore (@ppolitics) February 26, 2016
Details on some of the #MachineEatable talks:
Illustrating Data Literacy
June 2017
Eyebeam Research Resident Mimi Onuoha (@thistimeitsmimi) and Data & Society Fellow Zara Rahman (@zararah) will share Inform/Transform, a zine about all of the ways that information is aggregated, controlled, and used. Their zine provides a helpful analog tool that can increase data literacy, complete with relatable examples.
Mimi and Zara will give a reading, discuss the thinking behind the project. Then, you’ll flex your creative muscles to illustrate your own experiences in data literacy.
https://twitter.com/KaivanShroff/status/878299828110929920
When Recommendation Systems Go Bad
March 2017
Machine learning and recommendations systems have changed how we experience the internet, and by extension, many of the products and services we use in our civic and private lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly?
As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today.
Evan Estola (@estola), the Data Team Lead at Meetup, will share examples of recommendation systems that have gone wrong across various industries, and what can be done about them. Importantly, Evan will offer concrete technical approaches that can be employed. Evan will also offer arguments data scientists can use to justify making ethical decisions in a field that’s obsessed with optimization.
An engaged audience asking questions of @estola here at #MachineEatable pic.twitter.com/uZYgRk9vfb
— John Paul Farmer (@johnpaulfarmer) March 31, 2017
The gang is back together again #machineeatable @mstem @MicrosoftNY @CivicHall when recommendations go bad @Meetup pic.twitter.com/bWgHYhzl5U
— Melanie Lavelle (@Mglavelle) March 31, 2017
The journey from analytics to editorial
February 2017
Samarth Bhaskar (@samarthbhaskar) is an editor for Digital Transition at the NYTimes. He works with editors, journalists and the masthead in the newsroom to support editorial and operational decision making. He was previously a newsroom analytics manager at the Times, and an analyst at Etsy and Obama for America.
Hannah Poferl (@hannahpoferl) is a newsroom strategy editor at the NYTimes. She supports newsroom leadership through data and strategy projects. She has played a key role in the introduction and adoption of data in the newsroom since 2014. Prior to to The Times Hannah worked as an analytics lead for digital agencies including Brooklyn-based Huge and Threespot in Washington DC.
Journalism’s adjustment to the digital age has required a crash course in data and analytics. The editor’s hunch now sits next to realtime metrics on content performance across a dizzying array of channels.
How do leading publications like The New York Times adjust to a wide range of new competitors? How has data been introduced to newsroom staff and leadership? How has it affected newsroom decision making? What can be done to ensure that we protect the craft of investigative journalism while ensuring it finds and resonates with a meaningful audience?
The gray lady doing very cool data analytics to better tell stories @nytimes @mstem #machineeatable pic.twitter.com/Lrvp27FKZg
— Melanie Lavelle (@Mglavelle) February 17, 2017
Rescue Government Data
January 2017
Liz Barry, Sarah Lamdan, Bronwen Densmore, Bethany Wiggins, Laurie Allen, Jerome Whitington, Brendan O’Brian
A decentralized network of civic hackers, scientists, librarians, and archivists have come together to protect at-risk US government data. A war on empiricism threatens climate science and access to the important government data that makes it possible. This has inspired the formation of the Environmental Data & Governance Initiative, an international network of academics and non-profits dedicated to monitoring and preserving environmental evidence.
We’ll talk about the organizing work they’re doing to protect these data, and most importantly, how you can help fortify and rescue government data.
https://twitter.com/brianavecchione/status/825046168002523136
Best Practices in Pro Bono Data Science Project Scoping and Execution
October 2016
Raluca Dragusanu, MicroCred DataCorps Volunteer, Susan Sun, VOTO Mobile DataCorps Volunteer
It’s International Pro Bono Week and we’re excited to celebrate by showcasing our volunteers that have recently completed projects applying data science to tough social challenges.
The same algorithms and techniques that companies use to boost profits can be leveraged by mission-driven organizations to improve the world, but these projects must be responsibly scoped and executed to not only achieve maximum impact, but also mitigate potentailly damaging interpretations of the results. To explore this topic, we’ll learn about two recent DataKind projects focused on increasing accesibility to resources for underserved communities:
MicroCred: Financial Inclusion to Strengthen Communities Worldwide
VOTO Mobile: Giving Voice to the Voiceless Through Mobile Engagement
About Machine Eatable
Q&A time at #MachineEatable, discussing #data4good with @DataKind #DataCorps pic.twitter.com/6e3B5Nxytv
— John Paul Farmer (@johnpaulfarmer) October 28, 2016
Citizen-Led Social Experiments in Democratic Society
July 2016
J. Nathan Matias, Data Scientist, PhD student at the MIT Media Lab Center for Civic Media, a fellow at the Berkman Center at Harvard and a DERP Institute fellow (@natematias)
As data science is poised to offer substantial benefits to society, it faces deep public mistrust, especially large-scale social experimentation. Do experiments undermine democracy by advancing paternalism and favoring experts over citizen voices? What might an “experimenting society” look like if the public had an active role in the design and interpretation of experiments on important public questions? The roots of this debate go back to the 1930s, and they have a profound influence on how we do data science today.
In this talk, Nathan will share the unlikely history of arguments over the role of experimentation in democracies. He will also showcase early work on CivilServant, a system that supports online communities to conduct large-scale social experiments, developing mass-replicated open knowledge on community moderation questions, independently of internet platforms.
https://twitter.com/brianavecchione/status/759067034114285568
Discovering a path in data science and how machine learning can counter harmful speech online
April 2016
Warren Reed, Data Scientist, Office of Financial Research and DataKind volunteer (@JWarrenReed)
Moving from a career in finance to a career in data science was an easy step for Warren Reed. His discovery that he most enjoyed the quantitative aspects of working in the finance industry led him to a Masters degree in Data Science and in the first class at CUSP. Eventually, this lead to a job with the Obama Administration’s Office of Financial Research, he now works with any type of data that would benefit the entire financial community. With a strong sense of service Warren still wanted to give back and recently completed a project with DataKind and the Dangerous Speech Project. Hear Warren’s inspiring path into the data science field and the fascinating way he’s used machine learning to help counter harmful speech online.
At #MachineEatable, @JWarrenReed discusses using #machinelearning to combat hate speech: https://t.co/HpNimLUESe pic.twitter.com/8MglhCzagd
— Microsoft New York (@MicrosoftNY) May 1, 2016
Computation and/as Journalism
March 2016
Mark Hansen, Director, Brown Institute for Media Innovation at Columbia University (@cocteau)
In 1904 Joseph Pulitzer wrote at length about what should be taught in “The College of Journalism.” Data, or a turn of that century view of it, was on his list. “You want statistics to tell you the truth. You can find truth there if you know how to get at it, and romance, human interest, humor and fascinating revelations as well.” Fast forward 100 years and it seems that data and computation have lost their expressive voice. But data or computational journalism promises to change all that. These practices prioritize narrative and fill a gap (presumably opened by an over-emphasis on formal mathematics) between data and lived experience. In time, they will feedback to “official” data science, offering new tools and new methods endowed with the values and ethics of journalism. In this lunchtime discussion, Mark Hansen (@cocteau) will talk about the work he’s leading at Columbia’s School of Journalism, training new generations in a full-throated data practice.
Thrilled to have @cocteau at #MachineEatable talking data and storytelling (and yes that's Katy Perry) pic.twitter.com/mIrQeEeuyv
— Jeanne Brooks (@jmfbrooks) March 25, 2016
What happens when you apply machine learning to social sciences?
February 2016
Hanna Wallach (@hannawallach), Senior Researcher at Microsoft
Computational social science is the use of computational and statistical techniques to study social processes. Hanna will open a discussion on the opportunities, challenges, and implications involved in developing and using machine learning methods to analyze real-world data about society. Among other things, Hanna will discuss fairness, accountability, and transparency.
Social processes all have structure, content, temporal dynamics @hannawallach #MachineEatable pic.twitter.com/TSFxKAaPaL
— DataKind (@DataKind) February 26, 2016
Data, technology and refugees: the benefits and dangers of the new infrastructure for movement
January 2016
Mark Latonero, Fellow, Data & Society Research Institute (@latonero) and Paula Kift, PhD candidate at New York University (@paukif)
https://twitter.com/csicsaie/status/693127528677052416
Designing data partnerships: how can big companies and researchers work together to share useful data?
December 2015
Mitul Desai, Director for Research Partnerships and Analytics at the Mastercard Center for Inclusive Growth (@CNTR4Growth) and Anoush Tatevossian (@artate), Strategic Communications & Partnerships Officer at UN Global Pulse (@UNGlobalPulse).
Key for public sector: open source tools plus grant programs from @MicrosoftNY and others. – @ARTate #MachineEatable pic.twitter.com/iLwR2aSMdV
— John Paul Farmer (@johnpaulfarmer) December 11, 2015
Mimi Onuoha (@thistimeitsmimi)
https://twitter.com/csicsaie/status/665224276652531712
Interrogating Algorithms: how do we understand what’s going on inside the black box?
October 2015
Cathy O’Neil, Meredith Broussard, and Solon Barocas
#MachineEatable is kicking off at @CivicHall with @mstem & @jmfbrooks introducing our speakers! pic.twitter.com/0bmVcACsx6
— DataKind (@DataKind) October 22, 2015
Big crowd for inaugural #MachineEatable panel, shouts @datakind for convening- hey @jqnatividad is here. pic.twitter.com/uVe1hExG3g
— David Moore (@ppolitics) October 22, 2015
https://twitter.com/MicrosoftNY/status/657607455099121665