Instead of halting AI research, an unfeasible task, the industry must improve transparency and accountability by being open to external audits and to regulation
This intervention sits in a longer accountability debate: researchers had already criticized unequal access to code, data, and hardware as barriers to scrutiny in earlier calls for more transparent AI research.
The proposed alternative to pausing research is to make oversight operational. That aligns with prior demands that companies open algorithms to auditing and accept regulation, including calls for facial-recognition oversight.
First-order effects
The article shifts the policy ask from stopping AI work to requiring AI developers to expose their systems to independent review and regulatory oversight.
AI companies face a clearer legitimacy test: willingness to document, audit, and account for systems rather than rely solely on internal safety claims.
Second-order effects
External-audit readiness would make transparency practices a competitive and procurement consideration, differentiating developers that can substantiate their claims.
Regulators and civil-society accountability researchers gain a more concrete basis to press for access, evaluation standards, and enforceable disclosure obligations.
Third-order effects
If this approach is adopted broadly, AI governance could move from voluntary principles toward operational assurance, where deployment depends increasingly on demonstrable auditability.
The enduring tension will be whether meaningful external scrutiny can coexist with proprietary models, data, and infrastructure—the access problem identified in transparency critiques of AI research.
The trend: AI governance is shifting from debates over whether to slow development toward institutions that require developers to make safety and accountability claims independently testable.
My editorial about the (infamous) open letter is live on WIRED! https://www.wired.com/... I did my best to shift the narrative and recognize existing work by people like @timnitGebru, @ruha9 and @rajiinio, who have been proposing ways to make AI safer and less harmful for years.
“the risks of AI which are named in the letter are all hypothetical, based on a longtermist mindset that tends to overlook real problems like algorithmic discrimination & predictive policing, which are harming individuals now, in favor of potential existential risks to humanity.”…
“In reality, current AI systems are simply stochastic parrots built using data from underpaid workers and wrapped in elaborate engineering that provides the semblance of intelligence.” Important read (thank you @SashaMTL !) https://twitter.com/...
The article in itself is also very unbiased and incorrect in its assumptions and suggestions. “Regulatory authorities are developing legislation and protocols” - same “authorities” that develop surveillance tech and / or try regulating Facebook data usage for over a decade⁉️ http…
You gotta take the recommendations with a massive grain of salt because would hurt innovation (and coincidentally would strongly advantage the author's company), but the framing here is good. This train will not be stopped. https://www.wired.com/...
“It's not too late to flip the narrative, to start questioning the capabilities and limitations of these systems & to demand accountability and transparency...users of these technologies..have the power to help shape both the present and the future of AI.” https://www.wired.com/.…
damn this @SashaMTL piece is so persuasive! both grounds the conversation in existing proposals and scholarship and makes the world of AI feels so much more expansive and global https://www.wired.com/...
A really smart, nuanced piece by @SashaMTL. As she notes, @timnitGebru, @ruha9, @rajiinio (and many more!) have pushed for more ethical AI for years. Why Halt AI Research When We Already Know How To Make It Safer | WIRED https://www.wired.com/...
When it comes to AI, we must both support responsible innovation and ensure appropriate guardrails to protect folks' rights and safety. Our Administration is committed to that balance, from addressing bias in algorithms - to protecting privacy and combating disinformation.
Whether you liked the letter or not, it is a good thing that actions like the letter and @theCAIDP FTC complaint have brought the need for thoughtful responses to the foreground. https://twitter.com/...