The work, which appears in the journal Proceedings of the National Academy of Sciences (PNAS), is among the latest to uncover how artificial intelligence (AI) can alter our perceptions and actions.

“There is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded,” says Madalina Vlasceanu, a postdoctoral fellow in New York University’s Department of Psychology and the paper’s lead author. “As a consequence, their use by humans may result in the propagation, rather than reduction, of existing disparities.”

“These findings call for a model of ethical AI that combines human psychology with computational and sociological approaches to illuminate the formation, operation, and mitigation of algorithmic bias,” adds author David Amodio, a professor in NYU’s Department of Psychology and the University of Amsterdam.

Technology experts have expressed concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are ingrained.

“Certain 1950s ideas about gender are actually still embedded in our database systems,” Meredith Broussard, author of Artificial Unintelligence: How Computers Misunderstand the World and a professor at NYU’s Arthur L. Carter Journalism Institute, told the Markup earlier this year.

The use of AI by human decision makers may result in the propagation, rather than reduction, of existing disparities, Vlasceanu and Amodio say.

To address this possibility, they conducted studies that sought to determine whether the degree of inequality within a society relates to patterns of bias in algorithmic output and, if so, whether exposure to such output could influence human decision makers to act in accordance with these biases.

First, they drew from the Global Gender Gap Index (GGGI), which contains rankings of gender inequality for more than 150 countries. The GGGI represents the magnitude of gender inequality in economic participation and opportunity, educational attainment, health and survival, and political empowerment in 153 nations, thereby providing societal-level gender inequality scores for each country.

Next, to assess possible gender bias in search results, or algorithmic output, they examined whether words that should refer with equal probability to a man or a woman, such as “person,” “student,” or “human,” are more often assumed to be a man. Here, they conducted Google image searches for “person” within a nation (in its dominant local language) across 37 countries. The results showed that the proportion of male images yielded from these searches was higher in nations with greater gender inequality, revealing that algorithmic gender bias tracks with societal gender inequality.

The researchers repeated the study three months later with a sample of 52 countries, including 31 from the first study. The results were consistent with those from the initial study, reaffirming societal-level gender disparities are reflected in algorithmic output (i.e. internet searches).

Vlasceanu and Amodio then sought to determine whether exposure to such algorithmic outputs — search-engine results — can shape people’s perceptions and decisions in ways consistent with pre-existing societal inequalities.

To do so, they conducted a series of experiments involving a total of nearly 400 female and male U.S. participants.

In these experiments, the participants were told they were viewing Google image search results of four professions they were likely to be unfamiliar with: chandler, draper, peruker, and lapidary. The gender composition of each profession’s image set was selected to represent the Google image search results for the keyword “person” for nations with high global gender inequality scores (roughly 90% men to 10% women in Hungary or Turkey) as well as those with low global gender inequality scores (roughly 50% men to 50% women in Iceland or Finland) from the 52-nation study above. This allowed the researchers to mimic the results of internet searches in different countries.

Prior to viewing the search results, the participants provided prototypicality judgements regarding each profession (e.g., “Who is more likely to be a peruker, a man or a woman?”), which served as a baseline assessment of their perceptions. Here, the participants, both female and male, judged members of these professions as more likely to be a man than a woman.

However, when asked these same questions after viewing the image search results, the participants in the low-inequality conditions reversed their male-biased prototypes relative to the baseline assessment. By contrast, those in the high-inequality condition maintained their male-biased perceptions, thereby reinforcing their perceptions of these prototypes.

The researchers then assessed how biases driven by internet searches could potentially influence hiring decisions. To do so, they asked participants to judge the likelihood that a man or woman would be hired in each profession (“What type of person is most likely to be hired as a peruker?”) and, when presented with images of two job candidates (one woman and one man) for a position in that profession, to make their own hiring choice (e.g., “Choose one of these applicants for a job as a peruker.”).

Consistent with the other experimental results, exposure to images in the low-inequality condition produced more egalitarian judgments of male vs. female hiring tendencies within a profession and a higher likelihood of choosing a woman job candidate compared with exposure to image sets in the high-inequality condition.

“These results suggest a cycle of bias propagation between society, AI, and users,” Vlasceanu and Amodio write, adding that the “findings demonstrate that societal levels of inequality are evident in internet search algorithms and that exposure to this algorithmic output can lead human users to think and potentially act in ways that reinforce the societal inequality.”

The study was funded by the NYU Alliance for Public Interest Technology and the Netherlands Organization for Scientific Research (VICI 016.185.058).

The study is published in Nature Materials.

In a previous study, the University of Minnesota-led team found that cells have the ability to sense the stiffness of their environment — which ranges from stiff (bone tissue) to soft (fatty tissue) to medium stiffness (muscle tissue) — and their ability to move is dependent upon that environment. Their research showed that the cells can have a “sweet spot” of stiffness, that isn’t too hard or too soft, in which they have better traction and can move faster.

In this study, the researchers found that not only does the stiffness of the environment impact the speed at which cells move, but it also affects the direction in which they move.

For many years, scientists have thought that cells would always gravitate toward a stiffer environment, but the University of Minnesota researchers observed for the first time that cells can actually move toward a “sweet spot” that’s more in the middle.

“This discovery challenges the current thinking in the field, which is that cells only move toward stiffer environments,” said David Odde, a professor in the University of Minnesota Twin Cities Department of Biomedical Engineering and senior author of the study. “I think that this finding will change how people think about this phenomenon. Our mathematical model predicted, and we’ve shown through experiments, that cells actually can move toward the softer side.”

During the study, Odde and his team looked at both brain cancer and breast cancer cells. They placed cells between two environments — a stiffer region and a softer region — and observed where they accumulated.

The research team also found that some cells, like the breast cancer cells they studied, have a feedback mechanism that causes them to grip more strongly onto stiffer environments, which explains why many previous studies showed cells moving to the stiffer side. However, if you turn that mechanism off genetically, the cells will then gravitate more toward the middle.

“We’re basically decoding how cancer cells invade tissue,” Odde said. “They don’t just move randomly. They actually have particular ways in which they like to move, and if we can understand that, we may be better able to trip them up.”

The next step for the researchers is to use this information to build a simulator that shows how cancer cells move through an entire tumor, which will help them better predict cells’ movements based on their environments.

This research was supported primarily by the National Institutes of Health and the National Science Foundation Science and Technology Center for Engineering Mechanobiology with additional support from the University of Turku Doctoral Programme in Molecular Life Sciences, the Company of Biologists Travelling Fellowship, the Finnish Cultural Foundation, the Academy of Finland, the Sigrid Juselius Foundation, the Finnish Cancer Organization, the National Natural Science Foundation of China, the Natural Science Basic Research Plan in Shaanxi Province of China, the Shaanxi Province Youth Talent Support Program, and the Young Talent Support Plan of Xi’an Jiaotong University.

In addition to Odde, the research team included University of Minnesota Department of Biomedical Engineering researchers Jay Hou, Ghaidan Shamsan, Benjamin Fuller, and Jesse Kasim; University of Minnesota Twin Cities Department of Chemistry researchers Keun-Young Park, M. Mohsen Mahmoodi, and Professor Mark Distefano; University of Turku, Finland, researchers Aleksi Isomursu, Mathilde Mathieu, and Professor Johanna Ivaska; and Xi’an Jiaotong University researchers Bo Cheng, Tian Jian Lu, Guy Genin, Feng Xu, and Professor Min Lin.

“These images are going to remind the world that America can do big things, and remind the American people — especially our children — that there’s nothing beyond our capacity,” said President Biden in remarks during the event. “We can see possibilities no one has ever seen before. We can go places no one has ever gone before.”

Webb’s first full-color image reveals thousands of galaxies, including the faintest objects ever observed in the infrared.

“Webb’s First Deep Field is not only the first full-color image from the James Webb Space Telescope, it’s the deepest and sharpest infrared image of the distant universe, so far. This image covers a patch of sky approximately the size of a grain of sand held at arm’s length. It’s just a tiny sliver of the vast universe,” said NASA Administrator Bill Nelson. “This mission was made possible by human ingenuity — the incredible NASA Webb team and our international partners at the European Space Agency and the Canadian Space Agency. Webb is just the start of what we can accomplish in the future when we work together for the benefit of humanity.”

This record-setting deep field provides a preview of the full set of Webb’s first images, which will be released at 10:30 a.m. EDT Tuesday, July 12, in a live broadcast on NASA Television. The images will be available at: https://www.nasa.gov/webbfirstimages

More information about how to watch the live reveal of the full set of Webb’s first images on Tuesday, July 12, is available online: https://www.nasa.gov/press-release/nasa-updates-coverage-for-webb-telescope-s-first-images-reveal

“Scientists are thrilled that Webb is alive and as powerful as we hoped, far beyond Hubble, and that it survived all hazards to be our golden eye in the sky,” said John Mather, Webb senior project scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “What happened after the big bang? How did the expanding universe cool down and make black holes and galaxies and stars and planets and people? Astronomers see everything twice: first with pictures, and then with imagination and calculation. But there’s something out there that we’ve never imagined, and I will be as amazed as you are when we find it.”

Learn more about this deep field image: https://www.nasa.gov/image-feature/goddard/2022/nasa-s-webb-delivers-deepest-infrared-image-of-universe-yet/

The James Webb Space Telescope is the world’s premier space science observatory. Webb will solve mysteries in our solar system, look beyond to distant worlds around other stars, and probe the mysterious structures and origins of our universe and our place in it. Webb launched Dec. 25, 2021, on an Ariane 5 rocket from Europe’s Spaceport in French Guiana, South America. After completing a complex deployment sequence in space, Webb underwent months of commissioning where its mirrors were aligned, and its instruments were prepared for science.

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Materials provided by NASA. Note: Content may be edited for style and length.

“We’ve shown that there is real potential to repurpose these drugs, which are already known to be safe, for appetite suppression and weight loss,” said study leader Chen Liu, Ph.D., Assistant Professor of Internal Medicine and Neuroscience and an investigator in the Peter O’Donnell Jr. Brain Institute.

Obesity affects more than 41% of all adults in the U.S. and increases the risk of heart disease, stroke, diabetes, and certain types of cancer. Most treatments for obesity focus on eating habits and physical activity.

Scientists have long known that serotonin, a chemical messenger found throughout the brain and body, plays a key role in appetite. However, there are 15 different serotonin receptors — molecules that sense serotonin and signal for cells to change their behavior in response. Researchers have struggled to understand the role of each serotonin receptor in appetite, and previous drugs — including fen-phen and lorcaserin (Belviq) — that targeted certain individual receptors have been withdrawn from the market due to side effects.

Triptans, which are used to treat acute migraines and cluster headaches, work by targeting a different receptor — the serotonin 1B receptor (Htr1b) — that had not previously been well studied in the context of appetite and weight loss, said Dr. Liu.

For the new study, the researchers tested six prescription triptans in obese mice that were fed a high-fat diet for seven weeks. Mice fed two of these drugs ate about the same amount, but mice fed the other four ate less. After 24 days, mice given a daily dose of the drug frovatriptan lost, on average, 3.6% of their body weight, while mice not given a triptan gained an average of 5.1% of their body weight. Dr. Liu and his colleagues saw similar results when they implanted devices into the animals that gave them a steady dose of frovatriptan for 24 days.

“We found that these drugs, and one in particular, can lower body weight and improve glucose metabolism in less than a month, which is pretty impressive,” said Dr. Liu.

Since triptans are generally prescribed for short-term use during migraines, Dr. Liu suspects that patients would not have noticed the longer-term impacts on appetite and weight in the past.

To determine exactly how frovatriptan impacts food intake and weight, the researchers engineered mice to lack either Htr1b or Htr2c, the serotonin receptor targeted by fen-phen and lorcaserin. In mice without Htr1b, frovatriptan no longer could decrease appetite or cause weight loss, while cutting out Htr2c had no effect. This confirmed that the drug worked by targeting the serotonin 1B receptor.

“This finding could be important for drug development,” said Dr. Liu. “We not only shed light on the potential to repurpose existing triptans but also brought attention to Htr1b as a candidate to treat obesity and regulate food intake.”

The team went on to show exactly which neurons in the brain were most important for the role of Htr1b in mediating appetite, homing in on a small group of cells within the brain’s hypothalamus.

Other researchers who contributed to this study include Li Li, Steven C. Wyler, Luis A. León-Mercado, Baijie Xu, Swati, Xiameng Chen, Rong Wan, and Amanda G. Arnold of UT Southwestern; Youjin Oh and Jong-Woo Sohn of Korea Advanced Institute of Science and Technology; Lin Jia of UT Dallas; Guanlin Wang of the University of Oxford; Katherine Nautiyal of Dartmouth College; and René Hen of Columbia University.

The study was funded by the National Institutes of Health (R01 DK114036, DK130892, F32DK116427, K01AA024809), the American Health Association (16SDG27260001), a UTSW Pilot and Feasibility Award, and a Grossman Endowment Award for Excellence in Diabetes Research.

“This approach brings us one step closer to early intervention,” says Ioannis Paschalidis, a coauthor on the paper and a BU College of Engineering Distinguished Professor of Engineering. He says faster and earlier detection of Alzheimer’s could drive larger clinical trials that focus on individuals in early stages of the disease and potentially enable clinical interventions that slow cognitive decline: “It can form the basis of an online tool that could reach everyone and could increase the number of people who get screened early.”

The research team trained their model using audio recordings of neuropsychological interviews from over 1,000 individuals in the Framingham Heart Study, a long-running BU-led project looking at cardiovascular disease and other physiological conditions. Using automated online speech recognition tools — think, “Hey, Google!” — and a machine learning technique called natural language processing that helps computers understand text, they had their program transcribe the interviews, then encode them into numbers. A final model was trained to assess the likelihood and severity of an individual’s cognitive impairment using demographic data, the text encodings, and real diagnoses from neurologists and neuropsychologists.

Paschalidis says the model was not only able to accurately distinguish between healthy individuals and those with dementia, but also detect differences between those with mild cognitive impairment and dementia. And, it turned out, the quality of the recordings and how people spoke — whether their speech breezed along or consistently faltered — were less important than the content of what they were saying.

“It surprised us that speech flow or other audio features are not that critical; you can automatically transcribe interviews reasonably well, and rely on text analysis through AI to assess cognitive impairment,” says Paschalidis, who’s also the new director of BU’s Rafik B. Hariri Institute for Computing and Computational Science & Engineering. Though the team still needs to validate its results against other sources of data, the findings suggest their tool could support clinicians in diagnosing cognitive impairment using audio recordings, including those from virtual or telehealth appointments.

Screening before Symptom Onset

The model also provides insight into what parts of the neuropsychological exam might be more important than others in determining whether an individual has impaired cognition. The researchers’ model splits the exam transcripts into different sections based on the clinical tests performed. They discovered, for instance, that the Boston Naming Test — during which clinicians ask individuals to label a picture using one word — is most informative for an accurate dementia diagnosis. “This might enable clinicians to allocate resources in a way that allows them to do more screening, even before symptom onset,” says Paschalidis.

Early diagnosis of dementia is not only important for patients and their caregivers to be able to create an effective plan for treatment and support, but it’s also crucial for researchers working on therapies to slow and prevent Alzheimer’s disease progression. “Our models can help clinicians assess patients in terms of their chances of cognitive decline,” says Paschalidis, “and then best tailor resources to them by doing further testing on those that have a higher likelihood of dementia.”

Want to Join the Research Effort?

The research team is looking for volunteers to take an online survey and submit an anonymous cognitive test — results will be used to provide personalized cognitive assessments and will also help the team refine their AI model.

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Materials provided by Boston University. Original written by Gina Mantica. Note: Content may be edited for style and length.