Mayank Kejriwal, University of Southern California

Many people understand the concept of bias at some intuitive level. In society, and in artificial intelligence systems, racial and gender biases are well documented.

If society could somehow remove bias, would all problems go away? The late Nobel laureate Daniel Kahneman, who was a key figure in the field of behavioral economics, argued in his last book that bias is just one side of the coin. Errors in judgments can be attributed to two sources: bias and noise.

Bias and noise both play important roles in fields such as law, medicine and financial forecasting, where human judgments are central. In our work as computer and information scientists, my colleagues and I have found that noise also plays a role in AI.

Statistical noise

Noise in this context means variation in how people make judgments of the same problem or situation. The problem of noise is more pervasive than initially meets the eye. A seminal work, dating back all the way to the Great Depression, has found that different judges gave different sentences for similar cases.

Worryingly, sentencing in court cases can depend on things such as the temperature and whether the local football team won. Such factors, at least in part, contribute to the perception that the justice system is not just biased but also arbitrary at times.

Other examples: Insurance adjusters might give different estimates for similar claims, reflecting noise in their judgments. Noise is likely present in all manner of contests, ranging from wine tastings to local beauty pageants to college admissions.

Noise in the data

On the surface, it doesn't seem likely that noise could affect the performance of AI systems. After all, machines aren't affected by weather or football teams, so why would they make judgments that vary with circumstance? On the other hand, researchers know that bias affects AI, because it is reflected in the data that the AI is trained on.

For the new spate of AI models like ChatGPT, the gold standard is human performance on general intelligence problems such as common sense. ChatGPT and its peers are measured against human-labeled commonsense datasets.

Put simply, researchers and developers can ask the machine a commonsense question and compare it with human answers: “If I place a heavy rock on a paper table, will it collapse? Yes or No.” If there is high agreement between the two – in the best case, perfect agreement – the machine is approaching human-level common sense, according to the test.

So where would noise come in? The commonsense question above seems simple, and most humans would likely agree on its answer, but there are many questions where there is more disagreement or uncertainty: “Is the following sentence plausible or implausible? My dog plays volleyball.” In other words, there is potential for noise. It is not surprising that interesting commonsense questions would have some noise.

But the issue is that most AI tests don't account for this noise in experiments. Intuitively, questions generating human answers that tend to agree with one another should be weighted higher than if the answers diverge – in other words, where there is noise. Researchers still don't know whether or how to weigh AI's answers in that situation, but a first step is acknowledging that the problem exists.

Tracking down noise in the machine

Theory aside, the question still remains whether all of the above is hypothetical or if in real tests of common sense there is noise. The best way to prove or disprove the presence of noise is to take an existing test, remove the answers and get multiple people to independently label them, meaning provide answers. By measuring disagreement among humans, researchers can know just how much noise is in the test.

The details behind measuring this disagreement are complex, involving significant statistics and math. Besides, who is to say how common sense should be defined? How do you know the human judges are motivated enough to think through the question? These issues lie at the intersection of good experimental design and statistics. Robustness is key: One result, test or set of human labelers is unlikely to convince anyone. As a pragmatic matter, human labor is expensive. Perhaps for this reason, there haven't been any studies of possible noise in AI tests.

To address this gap, my colleagues and I designed such a study and published our findings in Nature Scientific Reports, showing that even in the domain of common sense, noise is inevitable. Because the setting in which judgments are elicited can matter, we did two kinds of studies. One type of study involved paid workers from Amazon Mechanical Turk, while the other study involved a smaller-scale labeling exercise in two labs at the University of Southern California and the Rensselaer Polytechnic Institute.

You can think of the former as a more realistic online setting, mirroring how many AI tests are actually labeled before being released for training and evaluation. The latter is more of an extreme, guaranteeing high quality but at much smaller scales. The question we set out to answer was how inevitable is noise, and is it just a matter of quality control?

The results were sobering. In both settings, even on commonsense questions that might have been expected to elicit high – even universal – agreement, we found a nontrivial degree of noise. The noise was high enough that we inferred that between 4% and 10% of a system's performance could be attributed to noise.

To emphasize what this means, suppose I built an AI system that achieved 85% on a test, and you built an AI system that achieved 91%. Your system would seem to be a lot better than mine. But if there is noise in the human labels that were used to score the answers, then we're not sure anymore that the 6% improvement means much. For all we know, there may be no real improvement.

On AI leaderboards, where large language models like the one that powers ChatGPT are compared, performance differences between rival systems are far narrower, typically less than 1%. As we show in the paper, ordinary statistics do not really come to the rescue for disentangling the effects of noise from those of true performance improvements.

Noise audits

What is the way forward? Returning to Kahneman's book, he proposed the concept of a “noise audit” for quantifying and ultimately mitigating noise as much as possible. At the very least, AI researchers need to estimate what influence noise might be having.

Auditing AI systems for bias is somewhat commonplace, so we believe that the concept of a noise audit should naturally follow. We hope that this study, as well as others like it, leads to their adoption.

Mayank Kejriwal, Research Assistant Professor of Industrial & Systems Engineering, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Benjamin Hurrell, University of Southern California and Omid Akbari, University of Southern California

You've likely heard that you can get iron from eating spinach and steak. You might also know that it's an essential trace element that is a major component of hemoglobin, a protein in red blood cells that carries oxygen from your lungs to all parts of the body.

A lesser known important function of iron is its involvement in generating energy for certain immune cells.

In our lab's newly published research, we found that blocking or limiting iron uptake in immune cells could potentially ease up the symptoms of an asthma attack caused by allergens.

Immune cells that need iron

During an asthma attack, harmless allergens activate immune cells in your lungs called ILC2s. This causes them to multiply and release large amounts of cytokines – messengers that immune cells use to communicate – and leads to unwanted inflammation. The result is symptoms such as coughing and wheezing that make it feel like someone is squeezing your airways.

To assess the role iron plays in how ILC2s function in the lungs, we conducted a series of experiments with ILC2s in the lab. We then confirmed our findings in mice with allergic asthma and in patients with different severities of asthma.

First, we found that ILC2s use a protein called transferrin receptor 1, or TfR1, to take up iron. When we blocked this protein as the ILC2s were undergoing activation, the cells were unable to use iron and could no longer multiply and cause inflammation as well as they did before.

We then used a chemical called an iron chelator to prevent ILC2s from using any iron at all. Iron chelators are like superpowered magnets for iron and are used in medical treatments to help manage conditions where there's too much iron in the body.

When we deprived ILC2s with an iron chelator, the cells had to change their metabolism and switch to a different way of getting energy, like trading in a sports car for a bicycle. The cells weren't as effective at causing inflammation in the lungs anymore.

Next, we limited cellular iron in mice with sensitive airways due to ILC2s. We did this in three different ways: by inhibiting TfR1, adding an iron chelator or inducing low overall iron levels using a synthetic protein called mini-hepcidin. Each of these methods helped reduce the mice's airway hyperreactivity – basically reducing the severity of their asthma symptoms.

Lastly, we looked at cells from patients with asthma. We noticed something interesting: the more TfR1 protein on their ILC2 cells, the worse their asthma symptoms. In other words, iron was playing a big role in how bad their asthma got. Blocking TfR1 and administering iron chelators both reduced ILC2 proliferation and cytokine production, suggesting that our findings in mice apply to human cells. This means we can move these findings from the lab to clinical trials as quickly as possible.

Iron therapy for asthma

Iron is like the conductor of an orchestra, instructing immune cells such as ILC2s how to behave during an asthma attack. Without enough iron, these cells can't cause as much trouble, which could mean fewer asthma symptoms.

Next, we're working on targeting a patient's immune cells during an asthma attack. If we can lower the amount of iron available to ILC2s without depleting overall iron levels in the body, this could mean a new therapy for asthma that tackles the root cause of the disease, not just the symptoms. Available treatments can control symptoms to keep patients alive, but they are not curing the disease. Iron-related therapies may offer a better solution for patients with asthma.

Our discovery applies to more than just asthma. It could be a game-changer for other diseases where ILC2s are involved, such as eczema and type 2 diabetes. Who knew iron could be such a big deal to your immune system?

Benjamin Hurrell, Assistant Professor of Research in Molecular Microbiology and Immunology, University of Southern California and Omid Akbari, Professor of Molecular Microbiology and Immunology, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Saverio Eric Spagnolie, University of Wisconsin-Madison

Scientific discovery doesn't always require a high-tech laboratory or a hefty budget. Many people have a first-rate lab right in their own homes – their kitchen.

The kitchen offers plenty of opportunities to view and explore what physicists call soft matter and complex fluids. Everyday phenomena, such as Cheerios clustering in milk or rings left when drops of coffee evaporate, have led to discoveries at the intersection of physics and chemistry and other tasteful collaborations between food scientists and physicists.

Two students, Sam Christianson and Carsen Grote, and I published a new study in Nature Communications in May 2024 that dives into another kitchen observation. We studied how objects can levitate in carbonated fluids, a phenomenon that's whimsically referred to as dancing raisins.

The study explored how objects like raisins can rhythmically move up and down in carbonated fluids for several minutes, even up to an hour.

An accompanying Twitter thread about our research went viral, amassing over half a million views in just two days. Why did this particular experiment catch the imaginations of so many?

Bubbling physics

Sparkling water and other carbonated beverages fizz with bubbles because they contain more gas than the fluid can support – they're “supersaturated” with gas. When you open a bottle of champagne or a soft drink, the fluid pressure drops and CO₂ molecules begin to make their escape to the surrounding air.

Bubbles do not usually form spontaneously in a fluid. A fluid is composed of molecules that like to stick together, so molecules at the fluid boundary are a bit unhappy. This results in surface tension, a force which seeks to reduce the surface area. Since bubbles add surface area, surface tension and fluid pressure normally squeeze any forming bubbles right back out of existence.

But rough patches on a container's surface, like the etchings in some champagne glasses, can protect new bubbles from the crushing effects of surface tension, offering them a chance to form and grow.

Bubbles also form inside the microscopic, tubelike cloth fibers left behind after wiping a glass with a towel. The bubbles grow steadily on these tubes and, once they're big enough, detach and float upward, carrying gas out of the container.

But as many champagne enthusiasts who put fruits in their glasses know, surface etchings and little cloth fibers aren't the only places where bubbles can form. Adding a small object like a raisin or a peanut to a sparkling drink also enables bubble growth. These immersed objects act as alluring new surfaces for opportunistic molecules like CO₂ to accumulate and form bubbles.

And once enough bubbles have grown on the object, a levitation act may be performed. Together, the bubbles can lift the object up to the surface of the liquid. Once at the surface, the bubbles pop, dropping the object back down. The process then begins again, in a periodic vertical dancing motion.

Dancing raisins

Raisins are particularly good dancers. It takes only a few seconds for enough bubbles to form on a raisin's wrinkly surface before it starts to rise upward – bubbles have a harder time forming on smoother surfaces. When dropped into just-opened sparkling water, a raisin can dance a vigorous tango for 20 minutes, and then a slower waltz for another hour or so.

We found that rotation, or spinning, was critically important for coaxing large objects to dance. Bubbles that cling to the bottom of an object can keep it aloft even after the top bubbles pop. But if the object starts to spin even a little bit, the bubbles underneath make the body spin even faster, which results in even more bubbles popping at the surface. And the sooner those bubbles are removed, the sooner the object can get back to its vertical dancing.

Small objects like raisins do not rotate as much as larger objects, but instead they do the twist, rapidly wobbling back and forth.

Modeling the bubbly flamenco

In the paper, we developed a mathematical model to predict how many trips to the surface we would expect an object like a raisin to make. In one experiment, we placed a 3D-printed sphere that acted as a model raisin in a glass of just-opened sparkling water. The sphere traveled from the bottom of the container to the top over 750 times in one hour.

The model incorporated the rate of bubble growth as well as the object's shape, size and surface roughness. It also took into account how quickly the fluid loses carbonation based on the container's geometry, and especially the flow created by all that bubbly activity.

The mathematical model helped us determine which forces influence the object's dancing the most. For example, the fluid drag on the object turned out to be relatively unimportant, but the ratio of the object's surface area to its volume was critical.

Looking to the future, the model also provides a way to determine some hard to measure quantities using more easily measured ones. For example, just by observing an object's dancing frequency, we can learn a lot about its surface at the microscopic level without having to see those details directly.

Different dances in different theaters

These results aren't just interesting for carbonated beverage lovers, though. Supersaturated fluids exist in nature, too – magma is one example.

As magma in a volcano rises closer to the Earth's surface, it rapidly depressurizes, and dissolved gases from inside the volcano make a dash for the exit, just like the CO₂ in carbonated water. These escaping gases can form into large, high-pressure bubbles and emerge with such force that a volcanic eruption ensues.

The particulate matter in magma may not dance in the same way raisins do in soda water, but tiny objects in the magma may affect how these explosive events play out.

The past decades have also seen an eruption of a different kind – thousands of scientific studies devoted to active matter in fluids. These studies look at things such as swimming microorganisms and the insides of our fluid-filled cells.

Most of these active systems do not exist in water but instead in more complicated biological fluids that contain the energy necessary to produce activity. Microorganisms absorb nutrients from the fluid around them to continue swimming. Molecular motors carry cargo along a superhighway in our cells by pulling nearby energy in the form of ATP from the environment.

Studying these systems can help scientists learn more about how the cells and bacteria in the human body function, and how life on this planet has evolved to its current state.

Meanwhile, a fluid itself can behave strangely because of a diverse molecular composition and bodies moving around inside it. Many new studies have addressed the behavior of microorganisms in such fluids as mucus, for instance, which behaves like both a viscous fluid and an elastic gel. Scientists still have much to learn about these highly complex systems.

While raisins in soda water seem fairly simple when compared with microorganisms swimming through biological fluids, they offer an accessible way to study generic features in those more challenging settings. In both cases, bodies extract energy from their complex fluid environment while also affecting it, and fascinating behaviors ensue.

New insights about the physical world, from geophysics to biology, will continue to emerge from tabletop-scale experiments – and perhaps from right in the kitchen.

Saverio Eric Spagnolie, Professor of Mathematics, University of Wisconsin-Madison

This article is republished from The Conversation under a Creative Commons license. Read the original article.