Computers Math

A new study led by Head of the Rokers Vision Laboratory and NYUAD Associate Professor of Psychology Bas Rokers explored why the severity of motion sickness varies from person to person by investigating sources of cybersickness during VR use.
In the new study, Variations in visual sensitivity predict motion sickness in virtual reality published in the journal Entertainment Computing, Rokers and his team used VR headsets to simulate visual cues and present videos that induced moderate levels of motion sickness. They found that a person’s ability to detect visual cues predicted the severity of motion sickness symptoms. Specifically, discomfort was due to a specific sensory cue called motion parallax, which is defined as the relative movement of different parts of the environment.
A previously reported source of variability in motion sickness severity, gender, was also evaluated but not confirmed. The researchers conclude that previously reported gender differences may have been due to poor personalization of VR displays, most of which default to male settings.
These findings suggest a number of strategies to mitigate motion sickness in VR, including reducing or eliminating specific sensory cues, and ensuring device settings are personalized to each user. Understanding the sources of motion sickness, especially while using technology, not only has the potential to alleviate discomfort, but also to make VR technology a more widely accessible resource for education, job training, healthcare, and entertainment.
“As we tested sensitivity to sensory cues, a robust relationship emerged. It was clear that the greater an individual’s sensitivity to motion parallax cues, the more severe the motion sickness symptoms,” said Rokers. “It is our hope that these findings will help lead to the more widespread use of powerful VR technologies by removing barriers that prevent many people from taking advantage of its potential.”

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Researchers at Uppsala University have developed a digital self-test that trains users to assess news items, images and videos presented on social media. The self-test has also been evaluated in a scientific study, which confirmed the researchers’ hypothesis that the tool genuinely improved the students’ ability to apply critical thinking to digital sources.
The new tool and the scientific review of it are part of the News Evaluator project to investigate new methods of enhancing young people’s capacity for critical awareness of digital sources, a key component of digital literacy.
“As research leader in the project, I’m surprised how complicated it is to develop this type of tool against misleading information — one that’s usable on a large scale. Obviously, critically assessing digital sources is complicated. We’ve been working on various designs and tests, with major experiments in school settings, for years. Now we’ve finally got a tool that evidently works. The effect is clearly positive and now we launch the self-test on our News Evaluator website http://www.newsevaluator.com, so that all anyone can test themselves for free,” says Thomas Nygren, associate professor at Uppsala University.
The tool is structured in a way that allows students to work with it, online, on their own. They get to see news articles in a social-media format, with pictures or videos, and the task is to determine how credible they are. Is there really wood pulp in Parmesan cheese, for instance?
“The aim is for the students to get better at uncovering what isn’t true, but also improve their understanding of what may be true even if it seems unlikely at first,” Nygren says.
As user support, the tool contains guidance. Students can follow how a professional would have gone about investigating the authenticity of the statements or images — by opening a new window and doing a separate search alongside the test, or doing a reverse image search, for example. The students are encouraged to learn “lateral reading” (verifying what you read by double checking news). After solving the tasks, the students get feedback on their performance.
When the tool was tested with just over 200 students’ help, it proved to have had a beneficial effect on their ability to assess sources critically. Students who had received guidance and feedback from the tool showed distinctly better results than those who had not been given this support. The tool also turned out to provide better results in terms of the above-mentioned ability than other, comparable initiatives that require teacher participation and more time.
Apart from practical tips such as opening a new search tab, doing reverse image searches and not always choosing the search result at the top of the hit page (but, rather, the one that comes from a source you recognise), Nygren has a general piece of advice that can help us all become more critically aware in the digital world:
“Make sure you keep up to date with information and news from trustworthy sources with credible practices of fact-checking, such as the national TV news programmes or an established daily newspaper. It’s difficult and arduous being critical about sources all the time.”

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Researchers at Toshiba Corporation in Japan have studied the operation of a small device fabricated in the write gap of a hard disk drive’s write head to extend its recording density. The device, developed by HWY Technologies, is based on a design concept known as microwave-assisted magnetic recording, or MAMR.
This technology, reported in the Journal of Applied Physics, by AIP Publishing, uses a microwave field generator known as a spin-torque oscillator. The spin-torque oscillator emits a microwave field causing the magnetic particles of the recording medium to wobble the way a spinning top does. This makes them much easier to flip over when the write head applies a recording magnetic field in the writing process.
In a computer’s hard drive, each bit of data is stored in magnetic particles known as grains. The magnetic orientation of the grains determines whether the bit is a 0 or a 1.
Making the grains smaller allows them to be packed together more tightly. This increases the storage capacity, but it also makes the data bits unstable. The development of MAMR allows more stable magnetic materials to be used but also limits the type of recording media that can be developed.
The investigators focused on another effect known as the flux control (FC) effect, which also occurs in MAMR. This effect improves the recording field and is maximized when the magnetization of the spin torque oscillator is completely reversed against the gap field.
The advantage of the FC effect is that improvement is obtained in any magnetic recording, according to author Hirofumi Suto. This is significant, since it would no longer be necessary to use recording media specially designed for the MAMR technology.
The FC device, a type of spin-torque oscillator designed to maximize the FC effect, consists of two magnetic layers fabricated directly in the write gap of the write head. A bias current supplied to the device reverses the magnetization of one of the layers through an effect known as spin-transfer torque.
The investigators experimented with different bias currents and found the reversal of magnetization occurred more quickly at higher currents. Upon comparing their experiments to a computational model, they also determined the recording field was enhanced by the FC effect, improving the writability of the write head and exceeding the performance of conventional write heads.
The FC device operates effectively at a fast write rate of approximately 3 gigabits per second, according to Suto. These results provide evidence that the FC device operates as designed and show that FC-MAMR is a promising technology for extending the areal density of hard disk drives.
Toshiba plans to introduce hard disk drives using MAMR technology that will increase hard disk capacity to 16-18 terabytes.

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For decades, there has been a trend in microelectronics towards ever smaller and more compact transistors. 2D materials such as graphene are seen as a beacon of hope here: they are the thinnest material layers that can possibly exist, consisting of only one or a few atomic layers. Nevertheless, they can conduct electrical currents — conventional silicon technology, on the other hand, no longer works properly if the layers become too thin.
However, such materials are not used in a vacuum; they have to be combined with suitable insulators — in order to seal them off from unwanted environmental influences, and also in order to control the flow of current via the so-called field effect. Until now, hexagonal boron nitride (hBN) has frequently been used for this purpose as it forms an excellent environment for 2D materials. However, studies conducted by TU Wien, in cooperation with ETH Zurich, the Russian Ioffe Institute and researchers from Saudi Arabia and Japan, now show that, contrary to previous assumptions, thin hBN layers are not suitable as insulators for future miniaturised field-effect transistors, as exorbitant leakage currents occur. So if 2D materials are really to revolutionise the semiconductor industry, one has to start looking for other insulator materials. The study has now been published in the scientific journal “Nature Electronics.”
The supposedly perfect insulator material
“At first glance, hexagonal boron nitride fits graphene and two-dimensional materials better than any other insulator,” says Theresia Knobloch, first author of the study, who is currently working on her dissertation in Tibor Grasser’s team at the Institute of Microelectronics at TU Wien. “Just like the 2D semiconducting materials, hBN consists of individual atomic layers that are only weakly bonded to each other.”
As a result, hBN can easily be used to make atomically smooth surfaces that do not interfere with the transport of electrons through 2D materials. “You might therefore think that hBN is the perfect material — both as a substrate on which to place thin-film semiconductors and also as a gate insulator needed to build field-effect transistors,” says Tibor Grasser.
Small leakage currents with big effects
A transistor can be compared to a water tap — only instead of a stream of water, electric current is switched on and off. As with a water tap, it is very important for a transistor that nothing leaks out of the valve itself.
This is exactly what the gate insulator is responsible for in the transistor: It isolates the controlling electrode, via which the current flow is switched on and off, from the semiconducting channel itself, through which the current then flows. A modern microprocessor contains about 50 billion transistors — so even a small loss of current at the gates can play an enormous role, because it significantly increases the total energy consumption.
In this study, the research team investigated the leakage currents that flow through thin hBN layers, both experimentally and using theoretical calculations. They found that some of the properties that make hBN such a suitable substrate also significantly increase the leakage currents through hBN. Boron nitride has a small dielectric constant, which means that the material interacts only weakly with electric fields. In consequence, the hBN layers used in miniaturised transistors must only be a few atomic layers thick so that the gate’s electric field can sufficiently control the channel. At the same time, however, the leakage currents become too large in this case, as they increase exponentially when reducing the layer thickness.
The search for insulators
“Our results show that hBN is not suitable as a gate insulator for miniaturised transistors based on 2D materials,” says Tibor Grasser. “This finding is an important guide for future studies, but it is only the beginning of the search for suitable insulators for the smallest transistors. Currently, no known material system can meet all the requirements, but it is only a matter of time and resources until a suitable material system is found.”
“The problem is complex, but this makes it all the more important that many scientists devote themselves to the search for a solution, because our society will need small, fast and, above all, energy-efficient computer chips in the future,” Theresia Knobloch is convinced. More

Researchers at Charité — Universitätsmedizin Berlin and TU Berlin as well as the University of Oslo have developed a new tissue-section analysis system for diagnosing breast cancer based on artificial intelligence (AI). Two further developments make this system unique: For the first time, morphological, molecular and histological data are integrated in a single analysis. Secondly, the system provides a clarification of the AI decision process in the form of heatmaps. Pixel by pixel, these heatmaps show which visual information influenced the AI decision process and to what extent, thus enabling doctors to understand and assess the plausibility of the results of the AI analysis. This represents a decisive and essential step forward for the future regular use of AI systems in hospitals. The results of this research have now been published in Nature Machine Intelligence.
Cancer treatment is increasingly concerned with the molecular characterization of tumor tissue samples. Studies are conducted to determine whether and/or how the DNA has changed in the tumor tissue as well as the gene and protein expression in the tissue sample. At the same time, researchers are becoming increasingly aware that cancer progression is closely related to intercellular cross-talk and the interaction of neoplastic cells with the surrounding tissue — including the immune system.
Although microscopic techniques enable biological processes to be studied with high spatial detail, they only permit a limited measurement of molecular markers. These are rather determined using proteins or DNA taken from tissue. As a result, spatial detail is not possible and the relationship between these markers and the microscopic structures is typically unclear. “We know that in the case of breast cancer, the number of immigrated immune cells, known as lymphocytes, in tumor tissue has an influence on the patient’s prognosis. There are also discussions as to whether this number has a predictive value — in other words if it enables us to say how effective a particular therapy is,” says Prof. Dr. Frederick Klauschen of Charité’s Institute of Pathology.
“The problem we have is the following: We have good and reliable molecular data and we have good histological data with high spatial detail. What we don’t have as yet is the decisive link between imaging data and high-dimensional molecular data,” adds Prof. Dr. Klaus-Robert Müller, professor of machine learning at TU Berlin. Both researchers have been working together for a number of years now at the national AI center of excellence the Berlin Institute for the Foundations of Learning and Data (BIFOLD) located at TU Berlin.
It is precisely this symbiosis which the newly published approach makes possible. “Our system facilitates the detection of pathological alterations in microscopic images. Parallel to this, we are able to provide precise heatmap visualizations showing which pixel in the microscopic image contributed to the diagnostic algorithm and to what extent,” explains Prof. Müller. The research team has also succeeded in significantly further developing this process: “Our analysis system has been trained using machine learning processes so that it can also predict various molecular characteristics, including the condition of the DNA, the gene expression as well as the protein expression in specific areas of the tissue, on the basis of the histological images.
Next on the agenda are certification and further clinical validations — including tests in tumor routine diagnostics. However, Prof. Klauschen is already convinced of the value of the research: “The methods we have developed will make it possible in the future to make histopathological tumor diagnostics more precise, more standardized and qualitatively better.”

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Smart speakers, such as Amazon Echo and Google Home, have proven adept at monitoring certain health care issues at home. For example, researchers at the University of Washington have shown that these devices can detect cardiac arrests or monitor babies breathing.
But what about tracking something even smaller: the minute motion of individual heartbeats in a person sitting in front of a smart speaker?
UW researchers have developed a new skill for a smart speaker that for the first time monitors both regular and irregular heartbeats without physical contact. The system sends inaudible sounds from the speaker out into a room and, based on the way the sounds are reflected back to the speaker, it can identify and monitor individual heartbeats. Because the heartbeat is such a tiny motion on the chest surface, the team’s system uses machine learning to help the smart speaker locate signals from both regular and irregular heartbeats.
When the researchers tested this system on healthy participants and hospitalized cardiac patients, the smart speaker detected heartbeats that closely matched the beats detected by standard heartbeat monitors. The team published these findings March 9 in Communications Biology.
“Regular heartbeats are easy enough to detect even if the signal is small, because you can look for a periodic pattern in the data,” said co-senior author Shyam Gollakota, a UW associate professor in the Paul G. Allen School of Computer Science & Engineering. “But irregular heartbeats are really challenging because there is no such pattern. I wasn’t sure that it would be possible to detect them, so I was pleasantly surprised that our algorithms could identify irregular heartbeats during tests with cardiac patients.”
While many people are familiar with the concept of a heart rate, doctors are more interested in the assessment of heart rhythm. Heart rate is the average of heartbeats over time, whereas a heart rhythm describes the pattern of heartbeats.

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For example, if a person has a heart rate of 60 beats per minute, they could have a regular heart rhythm — one beat every second — or an irregular heart rhythm — beats are randomly scattered across that minute but they still average out to 60 beats per minute.
“Heart rhythm disorders are actually more common than some other well-known heart conditions. Cardiac arrhythmias can cause major morbidities such as strokes, but can be highly unpredictable in occurrence, and thus difficult to diagnose,” said co-senior author Dr. Arun Sridhar, assistant professor of cardiology at the UW School of Medicine. “Availability of a low-cost test that can be performed frequently and at the convenience of home can be a game-changer for certain patients in terms of early diagnosis and management.”
The key to assessing heart rhythm lies in identifying the individual heartbeats. For this system, the search for heartbeats begins when a person sits within 1 to 2 feet in front of the smart speaker. Then the system plays an inaudible continuous sound, which bounces off the person and then returns to the speaker. Based on how the returned sound has changed, the system can isolate movements on the person — including the rise and fall of their chest as they breathe.
“The motion from someone’s breathing is orders of magnitude larger on the chest wall than the motion from heartbeats, so that poses a pretty big challenge,” said lead author Anran Wang, a doctoral student in the Allen School. “And the breathing signal is not regular so it’s hard to simply filter it out. Using the fact that smart speakers have multiple microphones, we designed a new beam-forming algorithm to help the speakers find heartbeats.”
The team designed what’s called a self-supervised machine learning algorithm, which learns on the fly instead of from a training set. This algorithm combines signals from all of the smart speaker’s multiple microphones to identify the elusive heartbeat signal.

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“This is similar to how Alexa can always find my voice even if I’m playing a video or if there are multiple people talking in the room,” Gollakota said. “When I say, ‘Hey, Alexa,’ the microphones are working together to find me in the room and listen to what I say next. That’s basically what’s happening here but with the heartbeat.”
The heartbeat signals that the smart speaker detects don’t look like the typical peaks that are commonly associated with traditional heartbeat monitors. The researchers used a second algorithm to segment the signal into individual heartbeats so that the system could extract what is known as the inter-beat interval, or the amount of time between two heartbeats.
“With this method, we are not getting the electric signal of the heart contracting. Instead we’re seeing the vibrations on the skin when the heart beats,” Wang said.
The researchers tested a prototype smart speaker running this system on two groups: 26 healthy participants and 24 hospitalized patients with a diversity of cardiac conditions, including atrial fibrillation and heart failure. The team compared the smart speaker’s inter-beat interval with one from a standard heartbeat monitor. Of the nearly 12,300 heartbeats measured for the healthy participants, the smart speaker’s median inter-beat interval was within 28 milliseconds of the standard monitor. The smart speaker performed almost as well with cardiac patients: of the more than 5,600 heartbeats measured, the median inter-beat interval was within 30 milliseconds of the standard.
Currently this system is set up for spot checks: If a person is concerned about their heart rhythm, they can sit in front of a smart speaker to get a reading. But the research team hopes that future versions could continuously monitor heartbeats while people are asleep, something that could help doctors diagnose conditions such as sleep apnea.
“If you have a device like this, you can monitor a patient on an extended basis and define patterns that are individualized for the patient. For example, we can figure out when arrhythmias are happening for each specific patient and then develop corresponding care plans that are tailored for when the patients actually need them,” Sridhar said. “This is the future of cardiology. And the beauty of using these kinds of devices is that they are already in people’s homes.”
This research was funded by the National Science Foundation. More

Anxiety levels in the United States are rising sharply and have especially intensified in younger populations. According to the Anxiety and Depression Association of America, anxiety disorders affect 31.9 percent of children ages 13 to 18 years old. Because of the COVID-19 pandemic, children and adolescents have experienced unprecedented interruptions to their daily lives and it is expected that these disruptions may precipitate mental illness, including anxiety, depression, and/or stress related symptoms.
Traditional anxiety and depression treatments include cognitive behavioral therapy and psychiatric medications, which are somewhat successful in alleviating symptoms in adults. However, they have yielded some mixed results in children. Therefore, discovering appropriate means for reducing childhood anxiety and depression that are both affordable and accessible is paramount.
Using a computerized and completely remote training program, researchers from Florida Atlantic University’s Charles E. Schmidt College of Science have found a way to alleviate negative emotions in preadolescent children. They examined the relationship between anxiety, inhibitory control, and resting-state electroencephalography (EEG) in a critical age-range for social and emotional development (ages 8 to 12 years old). Inhibitory control is the ability to willfully withhold or suppress a thought, action or feeling. Without it, people would act purely on impulses or on old habits of action and thought.
Results of the study published in the journal, Applied Neuropsychology: Child, reveal that computerized inhibitory training helps to mitigate negative emotions in preadolescent children. EEG results also provide evidence of frontal alpha asymmetry shifting to the left after children completed an emotional version of the training. At the baseline time point, there was further indication to support the link between inhibitory control dysfunction and anxiety/depression. Decreased inhibitory control performance predicted higher levels of anxiety and depression, signifying that inhibitory impairments could be a risk factor for the development of these conditions in children.
Prior research has focused on adults and has only used self-report measures to operationalize anxiety and depressive symptoms. This novel study expands upon research investigating cognitive and neurological mechanisms involved in childhood anxiety and depression. In addition, it includes an objective outcome measure (resting-state EEG) to enable more succinct conclusions about training efficacy.
“In the current social climate of the world, internalizing conditions like anxiety and depression are becoming increasingly common in children and adolescents. Meanwhile, the availability and accessibility of computer and tablet technology also has rapidly increased,” said Nathaniel Shanok, lead author and a recent Ph.D. graduate of FAU’s Department of Psychology, who received an award from the American Psychological Society in 2020 for this research. “Providing computerized cognitive training programs to children can be a highly beneficial use of this technology for improving not only academic performance, but as seen in our study, psychological and emotional functioning during a challenging time period of development.”
Participants in the study were assigned to four weeks of either an emotional inhibitory control training program, a neutral inhibitory control training program, or a waitlisted control, and were tested using cognitive, emotional and EEG measures. Researchers evaluated the effects of the four-week, 16-session computerized inhibitory control training program using three tasks (go/no-go, flanker, and Stroop). The training program utilized for the study is gFocus from IQ Mindware and was created by Mark Ashton Smith, Ph.D.
Researchers found that inhibitory control accuracy was significantly and negatively related to anxiety as well as depression. Emotional and neutral training conditions led to significant reductions in anxiety, depression, and negative affect relative to the waitlist group, with the emotional training condition showing the largest reductions in anxiety and negative affect. These two conditions showed comparable improvements in inhibitory control accuracy relative to the waitlist, with greater increases observed in the neutral training condition.
“Given the predominately adverse influence of anxiety on social, psychological and cognitive functioning; early prevention, management and quality treatment plans are critical research areas to explore,” said Nancy Aaron Jones, Ph.D., co-author, an associate professor, and director of the FAU WAVES Emotion Laboratory in the Department of Psychology, Charles E. Schmidt College of Science, and a member of the FAU Brain Institute. “Advancements in technology have made it possible to train certain cognitive abilities using child-friendly applications or games that can be easily accessed from a home computer. Devising computerized training programs, which target underlying cognitive characteristics related to anxiety is a promising method for attenuating symptoms and risk in children.”
Anxiety involves strong cognitive and emotional influences, which are both explicit (obsessive thought processes and ruminations) as well as implicit (negative processing bias and reduced cognitive control). Inhibitory control impairment is believed to be a cognitive mediator of anxiety through both explicit and implicit mechanisms. The explicit theory of generalized anxiety disorder explains that the manifestation of anxiety occurs in part due to the inability of individuals to effectively recognize unrealistic or overly critical thinking patterns (thoughts) and suppress them. More

The perils of machine learning — using computers to identify and analyze data patterns, such as in facial recognition software — have made headlines lately. Yet the technology also holds promise to help enforce federal regulations, including those related to the environment, in a fair, transparent way, according to a new study by Stanford researchers.
The analysis, published this week in the proceedings of the Association of Computing Machinery Conference on Fairness, Accountability and Transparency(link is external), evaluates machine learning techniques designed to support a U.S. Environmental Protection Agency (EPA) initiative to reduce severe violations of the Clean Water Act. It reveals how two key elements of so-called algorithmic design influence which communities are targeted for compliance efforts and, consequently, who bears the burden of pollution violations. The analysis — funded through the Stanford Woods Institute for the Environment’s Realizing Environmental Innovation Program — is timely given recent executive actions(link is external) calling for renewed focus on environmental justice.
“Machine learning is being used to help manage an overwhelming number of things that federal agencies are tasked to do — as a way to help increase efficiency,” said study co-principal investigator Daniel Ho, the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School. “Yet what we also show is that simply designing a machine learning-based system can have an additional benefit.”
Pervasive noncompliance
The Clean Water Act aims to limit pollution from entities that discharge directly into waterways, but in any given year, nearly 30 percent of such facilities self-report persistent or severe violations of their permits. In an effort to halve this type of noncompliance by 2022, EPA has been exploring the use of machine learning to target compliance resources.
To test this approach, EPA reached out to the academic community. Among its chosen partners: Stanford’s Regulation, Evaluation and Governance Lab (RegLab), an interdisciplinary team of legal experts, data scientists, social scientists and engineers that Ho heads. The group has done ongoing work with federal and state agencies to aid environmental compliance.

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In the new study, RegLab researchers examined how permits with similar functions, such as wastewater treatment plants, were classified by each state in ways that would affect their inclusion in the EPA national compliance initiative. Using machine learning models, they also sifted through hundreds of millions of observations — an impossible task with conventional approaches — from EPA databases on historical discharge volumes, compliance history and permit-level variables to predict the likelihood of future severe violations and the amount of pollution each facility would likely generate. They then evaluated demographic data, such as household income and minority population, for the areas where each model indicated the riskiest facilities were located.
Devil in the details
The team’s algorithmic process helped surface two key ways that the design of the EPA compliance initiative could influence who receives resources. These differences centered on which types of permits were included or excluded, as well as how the goal itself was articulated.
In the process of figuring out how to achieve the compliance goal, the researchers first had to translate the overall objective into a series of concrete instructions — an algorithm — needed to fulfill it. As they were assessing which facilities to run predictions on, they noticed an important embedded decision. While the EPA initiative expands covered permits by at least sevenfold relative to prior efforts, it limits its scope to “individual permits,” which cover a specific discharging entity, such as a single wastewater treatment plant. Left out are “general permits,” intended to cover multiple dischargers engaged in similar activities and with similar types of effluent. A related complication: Most permitting and monitoring authority is vested in state environmental agencies. As a result, functionally similar facilities may be included or excluded from the federal initiative based on how states implement their pollution permitting process.
“The impact of this environmental federalism makes partnership with states critical to achieving these larger goals in an equitable way,” said co-author Reid Whitaker, a RegLab affiliate and 2020 graduate of Stanford Law School now pursuing a PhD in the Jurisprudence and Social Policy Program at the University of California, Berkeley.
Second, the current EPA initiative focuses on reducing rates of noncompliance. While there are good reasons for this policy goal, the researchers’ algorithmic design process made clear that favoring this over pollution discharges that exceed the permitted limit would have a powerful unintended effect. Namely, it would shift enforcement resources away from the most severe violators, which are more likely to be in densely populated minority communities, and toward smaller facilities in more rural, predominantly white communities, according to the researchers.
“Breaking down the big idea of the compliance initiative into smaller chunks that a computer could understand forced a conversation about making implicit decisions explicit,” said study lead author Elinor Benami, a faculty affiliate at the RegLab and assistant professor of agricultural and applied economics at Virginia Tech. “Careful algorithmic design can help regulators transparently identify how objectives translate to implementation while using these techniques to address persistent capacity constraints.” More