Aurelia Butler

A recent study of how human resources professionals review online information and social media profiles of job candidates highlights the ways in which so-called “cybervetting” can introduce bias and moral judgment into the hiring process.
“The study drives home that cybervetting is ultimately assessing each job candidate’s moral character,” says Steve McDonald, corresponding author of the study and a professor of sociology at North Carolina State University. “It is equally clear that many of the things hiring professionals are looking at make it more likely for bias to play a role in hiring.”
For this study, the researchers conducted in-depth interviews with 61 human resources professionals involved in recruitment and hiring across many industries. Study participants ranged from in-house HR staff to executive recruitment consultants to professionals at staffing agencies.
“One of the things that cropped up repeatedly was that cybervetting not only judges people’s behavior, but how that behavior is presented,” says Amanda Damarin, co-author of the paper and an associate professor of sociology at Georgia State University. “For example, one participant noted that his organization had no problem with employees drinking alcohol, but did not want to see any photos of alcohol in an employee’s social media feed.
“There’s a big disconnect here. One the one hand, HR professionals view social media as being an ‘authentic’ version of who people really are; but those same HR professionals are also demanding that people carefully curate how they present themselves on social media.”
“It was also clear that people were rarely looking for information related to job tasks — a point some study participants brought up themselves,” McDonald says. “And the things they did look for reflected their explicit or implicit biases.”
For example, study participants referenced looking for things like posts about hiking and family photos of Christmas. But most people who hike are white, and most people who post Christmas photos are Christians. Study participants also expressed a preference for online profiles that signaled “active” and “energetic” lifestyles, which could lead to discrimination against older or disabled job seekers.
And it was often unclear what job candidates could do to address concerns about bias in cybervetting. For example, while many study participants noted that putting a photo online created the opportunity for bias to affect the hiring process, other study participants noted that not having a “professional” profile picture was in itself a “red flag.”
“Some workers have a social media profile that sends the right signals and can take advantage of cybervetting,” McDonald says. “But for everyone else, they are not only at a disadvantage, but they don’t even know they are at a disadvantage — much less why they are at a disadvantage. Because they don’t necessarily know what employers are looking for.”
“Some of the people we interviewed were very aware that cybervetting could lead to increased bias; some even avoided cybervetting for that reason,” Damarin says. “But others were enthusiastic about its use.”
Researchers say one of the key takeaways from the work is that there need to be clear guidelines or best practices for the use of cybervetting, if it is going to be used at all.
“The second takeaway is that the biases and moral judgments we are hearing about from these HR professionals are almost certainly being incorporated into software programs designed to automate the review of job candidates,” McDonald says. “These prejudices will simply be baked into the algorithms, making them a long-term problem for both organizations and job seekers.”

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Materials provided by North Carolina State University. Original written by Matt Shipman. Note: Content may be edited for style and length. More

Travellers looking to book a hotel should trust their gut instinct when it comes to online reviews rather than relying on computer algorithms to weed out the fake ones, a new study suggests.
Research, led by the University of York in collaboration with Nanyang Technological University, Singapore, shows the challenges of online ‘fake’ reviews for both users and computer algorithms. It suggests that a greater awareness of the linguistic characteristics of ‘fake’ reviews can allow online users to spot the ‘real’ from the ‘fake’ for themselves.
Dr Snehasish Banerjee, Lecturer in Marketing from the University of York’s Management School, said: “Reading and writing online reviews of hotels, restaurants, venues and so on, is a popular activity for online users, but alongside this, ‘fake’ reviews have also increased.
“Companies can now use computer algorithms to distinguish the ‘fake’ from the ‘real’ with a good level of accuracy, but the extent to which company websites use these algorithms is unclear and so some ‘fake’ reviews slip through the net.
“We wanted to understand whether human analysis was capable of filling this gap and whether more could be done to educate online users on how to approach these reviews.”
The researchers tasked 380 people to respond to questions about three hotel reviews — some authentic, others fake — based on their perception of the reviews. The users could rely on the same cues that computer algorithm use to discern ‘fake’ reviews, which includes the number of superlatives in the review, the level of details, if it was easy to read, and appeared noncommittal.
For those who already sceptical of online reviews this was a relatively straightforward task, but most could not identity factors such as ‘easy to read’ and ‘non-committal’ like a computer algorithm could. In the absence of this skill, the participants relied on ‘gut instinct’.
Dr Banerjee said: “The outcomes were surprisingly effective. We often assume that the human brain is no match for a computer, but in actual fact there are certain things we can do to train the mind in approaching some aspects of life differently.
“Following this study, we are recommending that people need to curb their instincts on truth and deception bias — the tendency to either approach online content with the assumption that it is all true or all fake respectively — as neither method works in the online environment.
“Online users often fail to detect fake reviews because they do not proactively look for deception cues. There is a need to change this default review reading habit, and if reading habit is practised long enough, they will eventually be able to rely on their gut instinct for fake review detection.”
The research also reminds businesses that ethical standards should be upheld to ensure that genuine experiences of their services are reflected online.

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Researchers at Tel Aviv University report that they have successfully connected the ear of a dead locust to a robot that receives the ear’s electrical signals and responds accordingly. The result is extraordinary: When the researchers clap once, the locust’s ear hears the sound and the robot moves forward; when the researchers clap twice, the robot moves backwards.
The interdisciplinary study was led by Idan Fishel, a joint master student under the joint supervision of Dr. Ben M. Maoz of the Iby and Aladar Fleischman Faculty of Engineering and the Sagol School of Neuroscience, Prof. Yossi Yovel and Prof. Amir Ayali, experts from the School of Zoology and the Sagol School of Neuroscience together with Dr. Anton Sheinin, Idan, Yoni Amit, and Neta Shavil. The results of the study were published in the journal Sensors.
The researchers explain that at the beginning of the study, they sought to examine how the advantages of biological systems could be integrated into technological systems, and how the senses of dead locust could be used as sensors for a robot. “We chose the sense of hearing, because it can be easily compared to existing technologies, in contrast to the sense of smell, for example, where the challenge is much greater,” says Dr. Maoz. “Our task was to replace the robot’s electronic microphone with a dead insect’s ear, use the ear’s ability to detect the electrical signals from the environment, in this case vibrations in the air, and, using a special chip, convert the insect input to that of the robot.”
To carry out this unique and unconventional task, the interdisciplinary team (Maoz, Yovel and Ayali) faced number of challenged. In the first stage the researchers built a robot capable of responding to signals it receives from the environment. Then, in a multidisciplinary collaboration, the researchers were able to isolate and characterize the dead locust ear and keep it alive, that is, functional, long enough to successfully connect it to the robot. In the final stage, the researchers succeeded in finding a way to pick up the signals received by the locust’s ear in a way that could be used by the robot. At the end of the process, the robot was able to “hear” the sounds and respond accordingly.
“Prof. Ayali’s laboratory has extensive experience working with locusts, and they have developed the skills to isolate and characterize the ear,” explains Dr. Maoz. “Prof. Yovel’s laboratory built the robot and developed code that enables the robot to respond to electrical auditory signals. And my laboratory has developed a special device — Ear-on-a-Chip — that allows the ear to be kept alive throughout the experiment by supplying oxygen and food to the organ, while allowing the electrical signals to be taken out of the locust’s ear and amplified and transmitted to the robot.
“In general, biological systems have a huge advantage over technological systems — both in terms of sensitivity and in terms of energy consumption. This initiative of Tel Aviv University researchers opens the door to sensory integrations between robots and insects — and may make much more cumbersome and expensive developments in the field of robotics redundant.
“It should be understood that biological systems expend negligible energy compared to electronic systems. They are miniature, and therefore also extremely economical and efficient. For the sake of comparison, a laptop consumes about 100 watts per hour, while the human brain consumes about 20 watts a day. Nature is much more advanced than we are, so we should use it. The principle we have demonstrated can be used and applied to other senses, such as smell, sight and touch. For example, some animals have amazing abilities to detect explosives or drugs; the creation of a robot with a biological nose could help us preserve human life and identify criminals in a way that is not possible today. Some animals know how to detect diseases. Others can sense earthquakes. The sky is the limit.”

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Materials provided by Tel Aviv University. Note: Content may be edited for style and length. More

In the group of Prof. Karl Leo, physicists, material scientists and engineers are working jointly on the development of novel organic materials and devices for high performance, flexible and possibly even biocompatible electronics and optoelectronics of the future. Increasing the performance of organic devices is one of the key challenges in their research. It was only last year, when the team headed by Dr. Hans Kleemann announced an important breakthrough with the development of efficient, printable vertical organic transistors.
Now Dr. Zhongbin Wu, Dr. Yuan Liu, and PhD student Erjuan Guo present the first electronic device that combines a vertical organic permeable base transistor (OPBT) and an OLED. With this novel device concept of an organic permeable base light-emitting transistor (OPB-LET), the researchers succeeded in combining the function of a highly efficient switching transistor and an organic light-emitting diode as commonly employed in active matrix displays. Active matrix liquid crystal displays (AMLCD) usually contain a matrix of thin-film transistors to drive LCD pixels. Each individual pixel has a circuit with active components (mostly transistors). In this context, organic light-emitting transistors, three-terminal devices combining a thin-film transistor with a light-emitting diode, have generated increasing interest. However, increasing their efficiency while keeping the operating voltage low remains a key challenge.
“The key to construct the high performance OPB-LETs is the permeable base electrode located at the center of the device, forming a distinctive optical microcavity and regulating charge carrier injection and transport. The thus designed three-terminal vertical optoelectronic devices can simultaneously high efficiencies (up to 24.6%), high luminance (up to 12,513 cd m-2), and low driving voltages ( More

Nearly everything author Malcolm Gladwell said about how information spreads in his 2000 bestseller “The Tipping Point” is wrong, according to a recent study led by UCLA professor of sociology Gabriel Rossman.
“The main point of ‘The Tipping Point’ is if you want your idea to spread, you find the most popular person in the center of any given network and you sell them on your idea, and then they’ll sell the rest of the world on it,” Rossman said.
But Rossman’s latest study, recently published in Proceedings of the National Academy of Sciences, pokes holes in that widely accepted notion by showing how the presence of even just a bit of advertising or other mass communication — “top-down” information that comes from outside the network — effectively equalizes the influence of everyone across the network.
Rossman, together with co-author Jacob Fisher of Duke University and the University of Michigan, used a statistical programming language called R to build out network maps based on several different datasets. One set harnessed Twitter posts, along with retweets and mentions, over two weeks in 2011. Another used the Democratic National Committee email network from WikiLeaks’ 2016 data dump. Another used the emails of Enron executives subpoenaed in 2002. Six others were randomly generated.
These provided a network structure — a web of dots and lines showing how users in each network were connected to one another. Once those maps existed, Rossman and Fisher were able to see how quickly an idea might spread throughout the network if it started from the network’s single most important person or if it started from someone chosen at random.
They looked at that information spread in several ways, comparing via computer simulation how information moved throughout the networks when it came solely through word-of-mouth within a network (“bottom up”), when it came solely through external advertising or public information (“top down”) and when it came through varying bottom-up and top-down combinations.
What they discovered refutes Gladwell’s concept that network position is always paramount. They found that in instances where there is even a small amount of advertising — even when it is just a quarter of a percent as strong as word-of-mouth — there’s virtually no difference between the influence of the person at the center of a network and those further out on the string.
“It’s not that word-of-mouth doesn’t matter — it’s that nobody is particularly important for the word-of-mouth process,” Rossman said. “What we saw is that when advertising doesn’t exist, when advertising is exactly zero, it looks like whoever is Mr. Popular, whoever has the most central connections, really matters. And in that scenario, if you start with that person at the center of the network, like the leader of an organization or company, rather than the intern, then whatever you’re selling gets an uptick.”
But it takes only an incredibly weak amount of advertising to effectively neutralize the dominance of Mr. Popular, Rossman said. “Just a small amount changes the dynamic so that it practically doesn’t matter whether you start with Mr. Popular or the intern.”
Rossman is an expert on information spread in culture and mass media and is the author of “Climbing the Charts: What Radio Airplay Tells Us About the Diffusion of Innovation.”
The findings of his latest study, he notes, have wide-ranging implications, from selling products to a specific audience to understanding how to share information on vaccines with vulnerable communities.
“There’s a reasonably big body of literature that says you should find someone who appears to be structurally important to the network you’re trying to connect with,” he said. “We’re arguing that, if advertising exists, you can just pick somebody at random in the network and you’ll get just as good results as if you found the absolutely ideal person to start with.”

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Materials provided by University of California – Los Angeles. Original written by Jessica Wolf. Note: Content may be edited for style and length. More

A novel computer algorithm that could create a broadly reactive influenza vaccine for swine flu also offers a path toward a pan-influenza vaccine and possibly a pan-coronavirus vaccine as well, according to a new paper published in Nature Communications.
“This work takes us a step closer to a pan-swine flu virus vaccine,” said Bette Korber, a computational biologist at Los Alamos National Laboratory and a co-author on the paper. “The hope is to eventually be prepared with an effective and rapid response if another swine flu epidemic begins to spread in humans, but this swine flu vaccine could also be useful in a veterinary setting.” The immune responses to the vaccine showed very promising breadth against diverse viral variants. “The same basic principles may be applicable to developing a pan-coronavirus vaccine to enable a rapid vaccine response to future coronavirus cross-species jumps,” said Korber.
The algorithm, Epigraph, has already been used to predict therapeutic HIV vaccine candidates, and it has also shown promising potential as a pan-filovirus vaccine against highly diverse Ebola and Marburg viruses, protecting against disease when tested in an animal model.
Vaccination with the Epigraph-designed product led to the development of a strong cross-reactive antibody response in mice, the study showed. In swine, it induced strong cross-reactive antibody and T-cell responses. The research was conducted in close collaboration with researchers from the Nebraska Center for Virology at the University of Nebraska, St. Jude Children’s Research Hospital, and Los Alamos National Laboratory.
“We developed the Epigraph strategy for this kind of problem, and it can, in theory, be applied to many diverse pathogens,” said Korber, who created it in partnership with her husband, James Theiler, a Los Alamos Fellow. “The tool creates a cocktail of vaccine antigens designed to maximize efficacy across a highly diverse population.”
Since 2010, more than 460 swine-flu variant infections have been reported in humans in the United States. Pigs are susceptible to swine, avian, and human influenza viruses, making them the perfect “mixing vessel” for novel reassorted influenza viruses, the authors note. These novel reassorted viruses have significant pandemic potential if zoonosis (transfer from pigs to humans) occurs, as seen with 2009 H1N1 swine flu pandemic.

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The era of widespread remote learning brought about by the COVID-19 pandemic requires online testing methods that effectively prevent cheating, especially in the form of collusion among students. With concerns about cheating on the rise across the country, a solution that also maintains student privacy is particularly valuable.
In research published today in npj Science of Learning, engineers from Rensselaer Polytechnic Institute demonstrate how a testing strategy they call “distanced online testing” can effectively reduce students’ ability to receive help from one another in order to score higher on a test taken at individual homes during social distancing.
“Often in remote online exams, students can talk over the phone or internet to discuss answers,” said Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and the corresponding author on this paper. “The key idea of our method is to minimize this chance via discrete optimization aided by knowledge of a student’s competencies.”
When a distanced online test is performed, students receive the same questions, but at varying times depending on their skill level. For instance, students of highest mastery levels receive each question after other groups of students have already answered those questions. This approach, Wang said, reduces the incentive for students to receive help from those who have more mastery of the material. In order to determine the order of each student’s questions, their competence levels are estimated using their grade point averages, SAT scores, or midterm scores, depending on what is available at a specific point in the semester.
According to statistical tests and post-exam surveys, this method reduced the points gained through collusion by orders of magnitude when compared to conventional exam methods. As an added benefit, Wang said, when students knew collusion would not be possible, they were more motivated to study class material. Wang and his collaborators hope to share this pedagogical innovation beyond the Rensselaer campus.
“We plan to develop a good platform so that others can easily use this method,” said Wang, a member of the Center for Biotechnology and Interdisciplinary Studies at Rensselaer.

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