What is the impact of algorithms around us on our society and the choices we make? How are these algorithms affected by competition and how can they exploit monopoly? When are decisions due to learning algorithms biased, why are they biased, and what is the cure? What considerations must we keep in mind while developing algorithms so that they do what we expect them to? When is a decision/pricing/service mechanism bordering on the illegal, and what are the alternatives?
Growing list of articles on these questions and issues, apart from some others on my blog:
- 11/14/2017: Artificial Intelligence Drones Become Terrifying Killing Machines in a Dystopian Short Film, but well we know a lot of other dystopian futures thanks to Black Mirror.
- 11/09/2017: Four Ethical Priorities for Neurosciences and AI, Nature comment article by Rafael Yuste, Sara Goering et al.
- 03/10/2017: 11th Circuit Rules that Title VII does not prohibit anti-gay discrimination in a deeply confused opinion, by Mark Joseph Stern. (ongoing debate of whether the Civil Rights Act of 1964 prohibits employers from discriminating against workers on the basis of their sexual orientation, apparently not by this article).
- 12/30/2016: Bias in Criminal Risk Scores is Mathematically Inevitable, Julia Angwin and Jeff Larson.
- 12/29/2016: Rating systems may discriminate against Uber drivers, Cornell Chronicle. Paper on SSRN: Discriminating Tastes – Customer Ratings as Vehicle for Bias, by Alex Rosenblat, Karen EC Levy, Solon Barocas, Tim Hwang.
- 12/27/2016: Finding inspiration in art in the betrayal of privacy, Jenna Wortham, New York Times. “The cameras were a chilling reflection of our documentation-obsessed times, how our need for validation usurps our desire for privacy and the protection of personal information.“
- 12/19/2016: Education Technology and the Ideology of Personalization, Audrey Watters, Hack Education. “But in this case, they must also sign away their right to sue Facebook or Summit Public Schools in case of a problem (like, say, a data breach)“. (stripping customers of their rights, what kind of an education system is this? why should I learn according to my personality traits?)
- 11/11/2016: Facebook to stop allowing some advertisers to exclude users by race, Julia Angwin, ProPublica. (good job on taking quick notice)
- 10/23/2016: When the Teaching Assistant is a Robot, Ben Gose, Chronicle of Higher Education. “Jill Watson was “trained” on a data set of 40,000 questions asked in previous classes.” (has this data set been vetted properly?)
- 10/26/2016: Facebook lets advertisers exclude users by race, blatant violation of the Federal Fair Housing Act of 1968. “The Fair Housing Act of 1968 makes it illegal “to make, print, or publish, or cause to be made, printed, or published any notice, statement, or advertisement, with respect to the sale or rental of a dwelling that indicates any preference, limitation, or discrimination based on race, color, religion, sex, handicap, familial status, or national origin.” Violators can face tens of thousands of dollars in fines.” (true of many other advertising, price, recommendation systems that feed on customer data)
- 12/3/2016: Infinite loop of Google v/s Alexa, hilarious!
- 11/30/2016: Transparency \neq Accountability, Danah Boyd
- 10/21/2016: How Facebook Algorithms Impact Democracy
- 10/14/2016: CODE 2016 fireside Panel – “the tyranny of algorithms?”
- 09/19/2016: Inherent Trade-offs in the Fair Determination of Risk Scores – Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
- 09/22-23/2016: Institute of Data, Systems and Society launch event
- 09/16/2016: The AI Now Summary, held on 7th July 2016
- 09/02/2016: Upcoming: Weapons of Math Destruction, by Cathy O’Neil.
- 09/01/2016: Algorithm may not fix Facebook’s trending topic bias issue, CNBC.
- 09/01/2016: How Algorithms Rule Our Working Lives, The Guardian.
- 09/01/2016: AI Wants To Be Your Bro, Not Your Foe, MIT Tech Review.
Well, AI isn’t either.
- 08/25/2016: Semantics derived automatically from language corpora necessarily contain human biases, Aylin Caliskan-Islam, Joanna J. Bryson, and Arvind Narayanan.
- 08/12/2016: Holding Algorithms Accountable, Pacific Standard.
- 07/29/2016: How Pokemon GO could have a big impact on Yelp, by Daniel Roberts, Yahoo! Finance
(Interaction of a game with a recommendation engine; people might go to Pokemon stop restaurants, rather than following reviews. Yelp has already introduced a Pokemon filter!)
- 07/21/2016: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings, Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai (arxiv)
- 07/19/2016: Uber faces court battle with drivers over employment status, the Guardian.
- 07/13/2016: Court: Judges Can Consider Predictive Algorithms in Sentencing, The Wall Street Journal.
- 07/07/2016: Is This Time Different? The Opportunities and Challenges of AI, by Jason Furman
- 07/07/2016: (Conference) Artifical Intelligence Now, NYU
- 06/29/2016: Be Careful What You Code For, by danah boyd, Data & Soceity, NYU
- 06/25/2016: Artificial Intelligence’s White Guy Problem, by Kate Crawford, NY Times
Known biases in algorithms due to limitations in available data: image recognition (darker skin tones v/s white), prediction of risk of recidivism (black v/s white), predictive policing (some neighborhoods overpoliced than others), recommendation of jobs (women shown lower paying jobs
- 06/21/2016: The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity, by Gerard Cachon, Kaitlin Daniels, Ruben Lobel, SSRN.
- 06/12/2016: Artificial Intelligence: We’re like children playing with a bomb, by Tim Adams, Interviewed Nick Bostrom, The Future of Humanity Institute
“A group of sparrows tweet about how easy their lives would be if they had an owl who could help with their nests, protect them, .. and couldn’t contain their excitement at the idea. However Skronkfinckle, an old owl, was skeptical – shouldn’t we master the art of owl-domestication and owl-taming first, before bringing an owl into our midst?”
- 04/21/2016: Amazon Doesn’t Consider the Race of Its Customers, should it?, by David Ingold and Spencer Soper, Bloomberg
“Amazon chose to not “prime” deliver in a neighborhood for purely business/optimization reasons, however the only “prime” excluded neighborhood was predominantly black.”
- 04/05/2016: Where Do We Find Ethics?, danah boyd, Data & Society
- 04/05/2016: Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights, Executive Office of the President
- 04/01/2016: Can An Algorithm Be Agnostic?, by Kate Crawford, in Science Technology and Human Values
- 03/30/2016: Microsoft’s Racist Chatbot Returns With Drug-smoking Meltdown, by Samuel Gibbs, The Guardian
“Microsoft released a Twitter chatbot, Tay, made in the image of a teenage girl, but after a day of interaction with Twitter millennials it quickly evolved into a racist, sexist bot that tweeted about taking drugs.”
- 03/21-22/2016: (Conference) Tyranny of the Algorithm?, Bernstein Institute for Human Rights, NYU
- 02/21/2016: What You Need to Know About Predictive Policing, The Marshall Project
- 02/16/2016: Why should I trust you? Explaining the predictions of any classifier, Ribiero, Singh and Guestrin.
- 02/09/2016: The Emerging Law of Algorithms, Robots and Predictive Analytics, by Frank Pasquale, Concurring Opinions
- 10/09/2014: Designer or Journalist: Who Shapes the News You Read in Your Favorite Apps, by Kate Crawford and Mike Ananny, Nieman Lab.
- 05/30/2014: The Anxieties of Big Data, by Kate Crawford, The New Inquiry
- 07/30/2013: JP Morgan Accused of Gaming Energy Bids as FERC Deal Looms, Bloomberg.