Similarly, New York City is considering Int 1894, a law that would introduce mandatory audits of “automated employment decision tools,” defined as “any system whose function is governed by statistical theory, or systems whose parameters are defined by such systems.” Notably, both bills mandate audits but provide only high-level guidelines on what an audit is.
As decision-makers in both government and industry create standards for algorithmic audits, disagreements about what counts as an algorithm are likely. Rather than trying to agree on a common definition of “algorithm” or a particular universal auditing technique, we suggest evaluating automated systems primarily based on their impact. By focusing on outcome rather than input, we avoid needless debates over technical complexity. What matters is the potential for harm, regardless of whether we’re discussing an algebraic formula or a deep neural network.
Impact is a critical assessment factor in other fields. It’s built into the classic DREAD framework in cybersecurity, which was first popularized by Microsoft in the early 2000s and is still used at some corporations. The “A” in DREAD asks threat assessors to quantify “affected users” by asking how many people would suffer the impact of an identified vulnerability. Impact assessments are also common in human rights and sustainability analyses, and we’ve seen some early developers of AI impact assessments create similar rubrics. For example, Canada’s Algorithmic Impact Assessment provides a score based on qualitative questions such as “Are clients in this line of business particularly vulnerable? (yes or no).”
What matters is the potential for harm, regardless of whether we’re discussing an algebraic formula or a deep neural network.
There are certainly difficulties to introducing a loosely defined term such as “impact” into any assessment. The DREAD framework was later supplemented or replaced by STRIDE, in part because of challenges with reconciling different beliefs about what threat modeling entails. Microsoft stopped using DREAD in 2008.
In the AI field, conferences and journals have already introduced impact statements with varying degrees of success and controversy. It’s far from foolproof: impact assessments that are purely formulaic can easily be gamed, while an overly vague definition can lead to arbitrary or impossibly lengthy assessments.
Still, it’s an important step forward. The term “algorithm,” however defined, shouldn’t be a shield to absolve the humans who designed and deployed any system of responsibility for the consequences of its use. This is why the public is increasingly demanding algorithmic accountability—and the concept of impact offers a useful common ground for different groups working to meet that demand.
Kristian Lum is an assistant research professor in the Computer and Information Science Department at the University of Pennsylvania.
Rumman Chowdhury is the director of the Machine Ethics, Transparency, and Accountability (META) team at Twitter. She was previously the CEO and founder of Parity, an algorithmic audit platform, and global lead for responsible AI at Accenture.
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
—June Kim
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
Solar geoengineering
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.
