|||

The over- and misuse of AI is one of my biggest tech pet peeves. It truly is evil to tack the AI term onto the description of most products. It also damages the long-term potential of AI by corrupting what it means—especially for the everyday people who aren’t involved or invested in building these tools, but who will use them (or be used by them).

Arvind Narayanan on Twitter:

Much of what’s being sold as AI today is snake oil. It does not and cannot work. In a talk at MIT yesterday, I described why this happening, how we can recognize flawed AI claims, and push back. Here are my annotated slides: https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf

Key point #1: AI is an umbrella term for a set of loosely related technologies. Some of those technologies have made genuine, remarkable, and widely-publicized progress recently. But companies exploit public confusion by slapping the AI label on whatever they’re selling.

Key point #2: Many dubious applications of AI involve predicting social outcomes: who will succeed at a job, which kids will drop out, etc. We can’t predict the future — that should be common sense. But we seem to have decided to suspend common sense when AI is involved.

Key point #3: transparent, manual scoring rules for risk prediction can be a good thing! Traffic violators get points on their licenses and those who accumulate too many points are deemed too risky to drive. In contrast, using AI to suspend people’s licenses would be dystopian. Harms of AI for predicting social outcomes

Check out the whole thread.

Up Next Next → The Demon Haunted World I have a foreboding of an America in my children’s or grandchildren’s time—when the United States is a service and information economy; when nearly ← Previous A Systemic View of Research Impact If academia ceases to have an impact it loses its raison d’être. Impact is what differentiates meaningful academic work from mere busywork. It
Latest posts
▵  Elon Musk attempts to explain Twitter to normal people in court
▵  TED and YouTube launch global climate initiative
▵  Embracing multilingualism to enhance complexity sensitive research
▵  The ‘Amazon effect’ is flooding a struggling recycling system with cardboard
▵  John Kerry, Arnold Schwarzenegger wage ‘World War Zero’ on climate change
▵  Combining semantic and term frequency similarities for text clustering
▵  Bad RCS implementations are creating big vulnerabilities, security researchers claim
▵  2019 Tech Trends Report — The Future Today Institute
▵  Medical Crowdsourcing: Harnessing the “Wisdom of the Crowd” to Solve Medical Mysteries
▵  Report Launch - OPSI Primer on AI for the Public Sector
▵  “Level Up”: Leveraging Skill and Engagement to Maximize Player Gameplay
▵  Beautiful is Good and Good is Reputable: Multiple-Attribute Charity Website Evaluation and Initial Perceptions of Reputation Under the Halo Effect
▵  Piret Tõnurist & Systems Change: how to get started and keep going?
▵  IBM expert Tamreem El Tohamy on bridging the skills gap in Africa
▵  The changing work of innovation for public value and social impact
▵  Former Go champion beaten by DeepMind retires after declaring AI invincible
▵  What part of “viral” content makes platforms want to encourage its spread?
▵  MTA floods NYC subway entrance because ‘climate change is real’
▵  The Demon Haunted World
▵  How to recognize AI snake oil
▵  A Systemic View of Research Impact
▵  Nobel Economics Prize Goes to Pioneers in Reducing Poverty
A brief, informal guide to doing grounded theory
▵  Adam Savage on Lists, More Lists, and the Power of Checkboxes
▵  Systems Practice, Abridged
▵  Fukushima reinvents itself with a $2.7 billion bet on renewables
▵  How Tesla’s first Gigafactory is changing Reno, Nevada
▵  “This is Sticking with Them:” Professor Explores Benefits of Model-Based Learning
Keeping the buzz in buzzwords
▵  README.txt: Introducing Into the Dataverse, the article series
▵  A ton of people received text messages overnight that were originally sent on Valentine’s Day