Are.na / critical machine learning

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The algorithm at the heart of the Facebook data breach sounds almost too dystopian to be real. It trawls through the most apparently trivial, throwaway postings -the "likes" users dole out as they browse the site - to gather sensitive personal information about sexual orientation, race, gender, even intelligence and childhood trauma.

How Cambridge Analytica turned Facebook...

Added by critical machine learning
Updated a month ago

Nothing sounds more futuristic than artificial intelligence (AI). Our predictions about the future of AI are largely shaped by science fiction. Go to any conference, skim any WIRED article, peruse any gallery of stock images depicting AI*, and you can't help but imagine AI as a disembodied cyberbabe (as in Spike Jonze's Her), a Tin Man (who...

The Temporality of Artificial...

Added by critical machine learning
Updated 2 months ago

Last Friday I attended a Data Visualization seminar by the Responsible Data Forum. I got a fascinatingly succinct introduction to the concerns of applying data visualization in a responsible way.

Crash Course in 'Responsible' Data...

Added by critical machine learning
Updated 3 months ago

Nick Seaver on what we mean when we critique "algorithms"

Algorithms as culture: Some tactics for...

Added by critical machine learning
Updated 4 months ago

"All models are wrong, but some are useful." So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now.

The End of Theory: The Data Deluge...

Added by critical machine learning
Updated 5 months ago

Facebook and the New Colonialism

Added by critical machine learning
Updated 5 months ago

The controversy about the human curators behind Facebook Trends has grown, since the allegations made last week by Gizmodo. Besides being a major headache for Facebook, it has helped prod a growing discussion about the power of Facebook to shape the information we see and what we take to be most important.

Algorithms, clickworkers, and the...

Added by critical machine learning
Updated 5 months ago

In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone - no matter what their politics - can agree on. Yet in recent years, divergent levels of trust in statistics has become one of the key schisms that have opened up in western liberal democracies.

How statistics lost their power - and...

Added by critical machine learning
Updated 5 months ago

January 7: Introduction to the course January 14: Media, mediation - Marshall McLuhan and Quentin Fiore, The Medium is the Massage (Bantam Books, 1967) - Sarah Kember and Joanna Zylinska, Life after New Media: Mediation as a Vital Process (Cambridge: MIT Press, 2012), xiii-28. - Alexander Galloway, "Love of the Middle," in Excommunication (U Chicago...

Added by critical machine learning
Updated 5 months ago

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitmen

Where are Human Subjects in Big Data...

Added by critical machine learning
Updated 5 months ago

In February of 2014, New York City Mayor Bill de Blasio announced the creation of IDNYC, a municipal identification card primarily designed to ease bureaucratic burdens for the city's immigrant population. When the card became available a year later, de Blasio described the program as "fraud-proof, secure and appealing to anyone."

What It Takes To Truly Delete Data

Added by critical machine learning
Updated 5 months ago

ai-resources - Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning

memo/ai-resources

Added by critical machine learning
Updated 5 months ago

ml4a is a collection of free educational resources devoted to machine learning for artists.

Machine Learning for Artists

Added by critical machine learning
Updated 5 months ago

This list is an attempt to collect and categorize a growing critical literature on algorithms as social concerns. The work included spans sociology, anthropology, science and technology studies, geography, communication, media studies, and legal studies, among others. Our interest in assembling this list was to catalog the emergence of "algorithms" as objects of interest for...

Critical Algorithm Studies: a Reading...

Added by critical machine learning
Updated 5 months ago

Tactical Tech is a non profit that explores the political and social role of technology in our lives.

Tactical technology collective

Added by critical machine learning
Updated 5 months ago

Cybernetics Conference 11.18.17 in New York.

Cybernetics Conference

Added by critical machine learning
Updated 5 months ago

Data & Society, Fifteen Scholars File Amicus Brief in Pending SCOTUS Case Aug 11, 2017, Marcia Hoffman, Kendra Albert, Andrew D Selbst On August 11, 2017, Data & Society and fifteen individual scholars-including danah boyd, Julia Ticona, and Amanda Lenhart-filed an amicus brief in a pending U.S. Supreme Court case, Carpenter...

Added by critical machine learning
Updated 5 months ago

As artificial intelligence and related technologies are used to make determinations and predictions in high stakes domains such as criminal justice, law enforcement, housing, hiring, and education, they have the potential to impact basic rights and liberties in profound ways. AI Now is partnering with the ACLU and other stakeholders to better understand and address these impacts.

AI Now Institute

Added by critical machine learning
Updated 5 months ago

Algorithmic systems are being adopted in a growing number of contexts. Fueled by big data, these systems filter, sort, score, recommend, personalize, and otherwise shape human experiences of socio-technical systems. Although these systems bring myriad benefits, they also contain inherent risks, such as codifying and entrenching biases; reducing accountability and hindering due process; and increasing the information assymmetry between data producers and data holders.

Added by critical machine learning
Updated 5 months ago

$http://www.deeplearningbook.org/ @book{Goodfellow-et-al-2016, title={Deep Learning}, author={Ian Goodfellow and Yoshua Bengio and Aaron Courville}, publisher={MIT Press}, note={\url{http://www.deeplearningbook.org}}, year={2016} }$

Deep Learning: Ch 1. Introduction

Added by critical machine learning
Updated 5 months ago

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