The Tesla tycoon proposed the plan before his buyout bid was disclosed, reiterated it the day his offer was revealed, and pitched it once again after Twitter agreed to the deal. Musk outlined his proposal at a TED conference on April 14: Musk argued that disclosing what amplifies or downranks tweets would reduce the risk of “behind the scenes manipulation.”
— TED Talks (@TEDTalks) April 14, 2022 The approach has won support from some transparency advocates and critics of Twitter’s content moderation. They argue that the move will reveal how Twitter determines what you see on your timeline — and what you don’t. “It has the potential to turn Twitter into a truly trusted platform, where users would understand why certain tweets show up on top of the list, and all concerns about behind-the-scenes secrecy or bias would be removed,” said Marc Linster, CTO at open source database firm EDB. “These concerns have been rampant with Google and Facebook. This open-sourcing move could be game-changing for social media overall.” Skeptics, however, have questioned the plan’s feasibility. They note that Twitter is comprised of various feeds, from the Trending section to your Home timeline, each of which is controlled by a complex mix of recommendation systems and human decisions. These processes produce results that even their developers don’t fully comprehend. Some of them reportedly mocked Musk by adding a (now-removed) public repository on the company’s GitHub platform — with zero code.
Musk said he wanted to make the Twitter algorithm open source. pic.twitter.com/cKNmwh6iuT — Disclose.tv (@disclosetv) April 25, 2022 Another issue is that algorithms alone offer limited insights. There are various other factors behind a tweet’s ranking. They include content that enters the platform, each user’s profile, the algorithms’ training data, moderation rules, and the code that trained the models. These constitute an enormous pool of data, which would be tough to trawl through and costly to disseminate. “You can’t simply open-source an ML [machine learning] model like it’s some bubble sort implementation,” said Steve Teixeira, a Twitter vice president of product.
— Steve Teixeira 🦇 (@stevetex) April 17, 2022 Further complexities arise from the mutability of the system. “Typically, recommender models get re-trained pretty often and will keep changing over time,” Bindu Reddy, CEO and co-founder of Abacus.AI, an artificial intelligence startup, told TNW. “While it is possible to release all trained models as well on an ongoing basis, it won’t be very useful either unless you understand exactly what inputs and outputs go into the model for predictions.” There are also potential dangers of the open-source proposal. The information could be copied by competitors, provide a tempting target for cybercriminals, and violate user privacy. It could also hinder another of Musk’s ambitions: “defeat the spambots.” On the other hand, open source offers new opportunities to find vulnerabilities and flaws.
Yes, but it will also make it easier to debug, audit, fork, trust, test, and build on. — cdixon.eth (@cdixon) April 26, 2022 Reddy is optimistic about the potential benefits. She argues that open-sourcing the ranking algorithm will be useful for research and evaluating any biases. She also expects to find further insights from the infrastructure components that influence what’s flagged and filtered in feeds. “Open sourcing those algorithms — and more importantly, those models — will go a long way in terms of transparency,” she said. Another prominent proponent of the approach is Twitter cofounder Jack Dorsey. The company’s ex-CEO has suggested letting users choose which — if any — algorithm they use. — jack⚡️ (@jack) March 25, 2022 Dorsey envisions creating an open marketplace of algorithms. Users would pick the one that best serves their desires, from prioritizing nuanced conversations to surfacing a constant stream of thirst traps. It sounds potentially idyllic — particularly if it can stop my feed from constantly showing obnoxious tweets by Elon Musk.
