Next month, I’m giving a keynote talk at The Future of the Future: The Ethics and Implications of AI, an event at UC Irvine that features Bruce Sterling, Rose Eveleth, David Kaye, and many others!
Preparatory to that event, I wrote an op-ed for the LA Review of Books on AI and its intrinsic conservativism, building on Molly Sauter’s excellent 2017 piece for Real Life.
Sauter’s insight in that essay: machine learning is fundamentally conservative, and it hates change. If you start a text message to your partner with “Hey darling,” the next time you start typing a message to them, “Hey” will beget an autosuggestion of “darling” as the next word, even if this time you are announcing a break-up. If you type a word or phrase you’ve never typed before, autosuggest will prompt you with the statistically most common next phrase from all users (I made a small internet storm in July 2018 when I documented autocomplete’s suggestion in my message to the family babysitter, which paired “Can you sit” with “on my face and”).
This conservativeness permeates every system of algorithmic inference: search for a refrigerator or a pair of shoes and they will follow you around the web as machine learning systems “re-target” you while you move from place to place, even after you’ve bought the fridge or the shoes. Spend some time researching white nationalism or flat earth conspiracies and all your YouTube recommendations will try to reinforce your “interest.” Follow a person on Twitter and you will be inundated with similar people to follow. Machine learning can produce very good accounts of correlation (“this person has that person’s address in their address-book and most of the time that means these people are friends”) but not causation (which is why Facebook constantly suggests that survivors of stalking follow their tormentors who, naturally, have their targets’ addresses in their address books).
Our Conservative AI Overlords Want Everything to Stay the Same [Cory Doctorow/LA Review of Books]
(Image: Groundhog Day/Columbia Pictures)