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Expert Q&A: Should We Permit AI to Determine Gender and Race from Resumes?
The banner ads on your browser, the route Google maps suggests for you, the song Spotify plays next: algorithms are inescapable in our daily lives. Some of us are already aware of the mechanisms behind a targeted ad or a dating profile that lights up our phone screen. However, few of us may actually stop to consider how this technology plays out in the hiring sector. As with any major technological advancement, it usually takes society (and legislation) a while to catch up and adjust for unintended consequences. Ultimately, algorithms are powerful tools. Like any tool, they have the potential for societal benefit or harm, depending on how they’re wielded. Here to weigh in on the matter is Assistant Professor of Information Systems & Operations Management Prasanna Parasurama, who recently joined Emory Goizueta Business School’s faculty in fall of 2023. This interview has been edited for clarity. Describe your research interests in six words. Six words…that’s difficult to do on the spot. How about “the impact of AI and other digital technologies on hiring.” Is that condensed enough? That works! What first interested you in the intersection of AI and hiring practices? Before I did my PhD, I was working as a data scientist in the HR analytics space at a start-up company. That is where my interest in the topic began. But this was a long time ago. People hadn’t started talking much about AI, or algorithmic hiring. The conversation around algorithmic bias and algorithmic fairness picked up steam in the second or third year of my PhD. That had a strong influence on my dissertation focus. And naturally, one of the contexts in which both these matters have large repercussions is in the hiring space. What demographics does your research focus on (gender identity, race/ethnicity, socioeconomic status, all of the above)? Do you focus on a particular job sector? My research mostly looks at gender and race for two main reasons. First, prior research has typically looked at race and gender, which gives us a better foundation to build on. Second, it’s much easier to measure gender and race based on the data that we have available—from resumes, from hiring data, like what we collect from the Equal Employment Opportunity Commission. They typically collect data on gender and race, and our research requires those really large data sets to draw patterns. They don’t ask for socioeconomic status or have an easy way to quantify that information. That’s not to say those are less important factors, or that no one is looking at them. One of the papers you’re working on examines resumes written by self-identified men and women. It looks at how their resumes differ, and how that influenced their likelihood of being contacted for an interview. So in this paper, we’re essentially looking at how men and women write their resumes differently and if that impacts hiring outcomes. Take resume screening algorithms, for example. One proposed way to reduce bias in these screening algorithms is to remove names from resumes to blind the applicant’s gender to the algorithm. But just removing names does very little, because there are so many other things that serve as proxies to someone’s gender. While our research is focused on people applying to jobs in the tech sector, this is true across occupations. "We find it’s easy to train an algorithm to accurately predict gender, even with names redacted." Prasanna Parasurama What are some of those gendered “tells” on a resume? People write down hobbies and extracurricular activities, and some of those are very gendered. Dancing and ballet tend to denote female applicants; you’re more likely to see something like wrestling for male applicants. Beyond hobbies, which is sort of obvious, is just how people write things, or the language they use. Female applicants tend to use a lot more affective words. Men, on the other hand, use more of what we call agentic words. Can you explain that a little more? In social psychology, social role theory argues that men are stereotyped to be more agentic, whereas women are stereotyped to be more communal, and that their communication styles reflect this. There’s essentially a list of agentic words that researchers have come up with that men use a lot more than women. And women are more likely to use affective words, like “warmly” or “closely,” which have to do with emotions or attitudes. These communication differences between men and women have been demonstrated in social sciences before, which has helped inform our work. But we’re not just relying on social science tools—our conclusions are driven by our own data. If a word is able to predict that an applicant’s resume belongs to a female versus male applicant, then we assign different weights, depending on how accurately it can predict that. So we’re not just operating on theories. Were there any gendered patterns that surprised you? If you were to assign masculinity and femininity to particular words, an algorithm would likely assign “married” to be a feminine term in most contexts. But in this particular case, it’s actually more associated with men. Men are much more likely to use it in resumes, because it signals something different to society than when women use it. "One of the most predictive terms for men was references to parenthood. It’s much easier for men to reference kids than for women to reveal information about their household status. Women face a penalty where men receive a boost." Prasanna Parasurama Studies show that people perceive fathers as being more responsible employees, whereas mothers are regarded as less reliable in the workplace. We haven’t studied this, but I would speculate that if you go on a platform like LinkedIn, men are more likely to disclose details about fatherhood, marriage, and kids than women are. There were some other tidbits that I didn’t see coming, like the fact that women are much less likely to put their addresses on their resume. Can AI predict race from a resume as easily as it can predict gender? There’s surprisingly very little we know on that front. From existing literature outside of algorithmic literature, we know differences exist in terms of race, not just on the employer side, where there might be bias, but we also on the worker side. People of different races search for jobs differently. The question is, how do we take this into account in the algorithm? From a technical standpoint, it should be feasible to do the same thing we do with gender, but it just becomes a little bit harder to predict race in practice. The cues are so variable. Gender is also more universal – no matter where you live, there are probably men and women and people who identify as in between or other. Whereas the concept of race can be very specific in different geographic regions. Racial identities in America are very different from racial identities in India, for instance. And in a place like India, religion matters a lot more than it does in the United States. So this conversation around algorithms and bias will look different across the globe. Beyond screening resumes, how does AI impact people’s access to job opportunities? A lot of hiring platforms and labor market intermediaries such as LinkedIn use AI. Their task is to match workers to these different jobs. There’s so many jobs and so many workers. No one can manually go through each one. So they have to train algorithms based on existing behavior and existing design decisions on the platform to recommend applicants to particular jobs and vice versa. When we talk about algorithmic hiring, it’s not just hiring per se, but spaces like these which dictate what opportunities you’re exposed to. It has a huge impact on who ends up with what job. What impact do you want your research to have in the real world? Do you think that we actually should use algorithms to figure out gender or race? Is it even possible to blind AI to gender or race? Algorithms are here to stay, for better or worse. We need them. When we think about algorithmic hiring, I think people picture an actual robot deciding who to hire. That’s not the case. Algorithms are typically only taking the space of the initial part of hiring. "I think overall, algorithms make our lives better. They can recommend a job to you based on more sophisticated factors than when the job was chronologically posted. There’s also no reason to believe that a human will be less biased than an algorithm." Prasanna Parasurama I think the consensus is that we can’t blind the algorithm to gender or other factors. Instead, we do have to take people’s demographics into account and monitor outcomes to correct for any sort of demonstrable bias. LinkedIn, for example, does a fairly good job publishing research on how they train their algorithms. It’s better to address the problem head on, to take demographic factors into account upfront and make sure that there aren’t drastic differences in outcomes between different demographics. What advice would you give to hopeful job candidates navigating these systems? Years of research have shown that going through a connection or a referral is by far the best way to increase your odds of getting an interview—by a factor of literally 200 to 300 percent. Hiring is still a very personal thing. People typically trust people they know. Prasanna Parasurama is an Assistant Professor of Information Systems & Operations Management at Emory University’s Goizueta Business School. Prasanna’s research areas include algorithmic hiring, algorithmic bias and fairness, and human-AI interaction. His research leverages a wide array of quantitative methods including econometrics, machine learning, and natural language processing. Prasanna is available to talk about this important and developing topic - simply click on his icon now to arrange an interview today.
Expert Insight: Training Innovative AI to Provide Expert Guidance on Prescription Medications
A new wave of medications meant to treat Type II diabetes is grabbing headlines around the world for their ability to help people lose a significant amount of weight. They are called GLP-1 receptor agonists. By mimicking a glucagon-like peptide (GLP) naturally released by the body during digestion, they not only lower blood sugar but also slow digestion and increase the sense of fullness after eating. The two big names in GLP-1 agonists are Ozempic and Wegovy, and both are a form of semaglutide. Another medication, tirzepatide, is sold as Mounjaro and Zepbound. It is also a glucose-dependent insulinotropic polypeptide (GIP) agonist as well as GLP-1. Physicians have been prescribing semaglutide and tirzepatide with increasing frequency. However, both medications come with a host of side effects, including nausea and stomach pain, and are not suitable for every patient. Many clinics and physicians do not have immediate access to expert second opinions, as do the physicians at Emory Healthcare. Creating a Digital Twin That lack of an expert is one of the reasons Karl Kuhnert, professor in the practice of organization and management at Emory University’s Goizueta Business School, is using artificial intelligence to capture the expertise of physicians like Caroline Collins MD through the Tacit Object Modeler™, or TOM. By using TOM, developed by Merlynn Intelligence Technologies, Kuhnert and Collins can create her “decision-making digital twin.” This allows Collins to reveal her expertise as a primary care physician with Emory Healthcare and an Assistant Professor at Emory School of Medicine, where she has been leading the field in integrating lifestyle medicine into clinical practices and education. Traditional AI, like ChatGPT, uses massive amount of data points to predict outcomes using what’s known as explicit knowledge. But it isn’t necessarily learning as it goes. According to Kuhnert, TOM has been designed to learn how an expert, like Collins, decides whether or not to prescribe a drug like semaglutide to a patient. Wisdom or tacit knowledge is intuitive and rooted in experience and context. It is hard to communicate, and usually resides only in the expert’s mind. TOM’s ability to “peek into the expert’s mind makes it a compelling technology for accessing wisdom.” “Objective or explicit knowledge is known and can be shared with others,” says Kuhnert. "For example, ChatGPT uses explicit knowledge in its answers. It’s not creating something new. It may be new to you as you read it, but somebody, somewhere, before you, has created it. It’s understood as coming from some source." Karl Kuhnert “Tacit knowledge is subjective wisdom. Experts offer this, and we use their tacit know-how, their implicit knowledge, to make their decisions. If it were objective, everyone could do it. This is why we hire experts: They see things and know things others don’t; they see around corners.” Mimicking the Mind of a Medical Expert Teaching TOM to see around the corners requires Collins to work with the AI over the course of a few days. “Essentially what I do is I sit down with, in this case, a physician, and ask them, ‘What are thinking about when you make this decision?'” says Kuhnert. “The layperson might think that there are hundreds of variables in making a medical decision like this. With the expert’s tacit knowledge and experience, it is usually between seven and twelve variables. They decide based on these critical variables,” he says. "These experts have so much experience, they can cut away a lot of the noise around a decision and get right to the point and ask, ‘What am I looking at?’" Karl Kuhnert As TOM learns, it presents Collins with more and different scenarios for prescribing semaglutide. As she makes decisions, it remembers the variables present during her decision-making process. “Obviously, some variables are going to be more important than other variables. Certain combinations are going to be challenging,” says Collins. “Sometimes there are going to be some variables where I think, yes, this patient needs a GLP-1. Then there may be some variables where I think, no, this person really doesn’t need that. And which ones are going to win out? That’s really where TOM is valuable. It can say, okay, when in these difficult circumstances where there are conflicting variables, which one will ultimately be most important in making that decision?” The Process: Trusting AI After working with TOM for several hours, Collins will have reacted to enough scenarios for TOM to learn to make her decision. The Twin will need to demonstrate that it can replicate her decision-making with acceptable accuracy—high 90s to 100 percent. Once there, Collins’ Twin is ready to use. “I think it’s important to have concordance between what I would say in a situation and then what my digital twin would say in a situation because that’s our ultimate goal is to have an AI algorithm that can duplicate what my recommendation would be given these circumstances for a patient,” Collins says. “So, someone, whether that be an insurance company, or a patient themselves or another provider, would be able to consult TOM, and in essence, me, and say, in this scenario, would you prescribe a GLP-1 or not given this specific patient’s situation?” The patient’s current health and family history are critical when deciding whether or not to prescribe semaglutide. For example, according to Novo Nordisk, the makers of Ozempic, the drug should not be prescribed to patients with a history of problems with the pancreas or kidneys or with a family history of thyroid cancer. Those are just the start of a list of reasons why a patient may or may not be a good candidate for the medication. Kuhnert says, “What we’re learning is that there are so many primary care physicians right now that if you come in with a BMI over 25 and are prediabetic, you’re going to get (a prescription). But there’s much more data around this to suggest that there are people who are health marginalized, and they can’t do this. They should not have this (medication). It’s got to be distributed to people who can tolerate it and are safe.” Accessing the Digital Twin on TOM Collins’s digital twin could be available via something as easy to access as an iPhone app. “Part of my job is to provide the latest information to primary care physicians. Now, I can do this in a way that is very powerful for primary care physicians to go on their phones and put it in. It’s pretty remarkable, according to Colllins.” It is also transparent and importantly sourced information. Any physician using a digital twin created with TOM will know exactly whose expertise they are accessing, so anyone asking for a second opinion from Colllins will know they are using an expert physician from Emory University. In addition to patient safety, there are a number of ways TOM can be useful to the healthcare industry when prescribing medications like semaglutide. This includes interfacing with insurance companies and the prior approval process, often lengthy and handled by non-physician staff. “Why is a non-expert at an insurance company determining whether a patient needs a medication or not? Would it be better to have an expert?” says Collins. “I’m an expert in internal medicine and lifestyle medicine. So, I help people not only lose weight, but also help people change their behaviors to optimize their health. My take on GLP-1 medications is not that everyone needs them, it’s that they need to be utilized in a meaningful way, so patients will get benefit, given risks and benefits for these medications.” The Power of a Second Opinion Getting second, and sometimes third, opinions is a common practice among physicians and patients both. When a patient presents symptoms to their primary care physician, that physician may have studied the possible disease in school but isn’t necessarily an expert. In a community like Emory Healthcare, the experts are readily available, like Collins. She often serves as a second opinion for her colleagues and others around the country. “What we’re providing folks is more of a second opinion. Because we want this actually to work alongside someone, you can look at this opinion that this expert gave, and now, based on sourced information, you can choose. This person may be one of the best in the country, if not the world, in making this decision. But we’re not replacing people here. We’re not dislocating people with this technology. We need people. We need today’s and tomorrow’s experts as well,” according to Kuhnert. But also, you now have the ability to take an Emory physician’s diagnosing capabilities to physicians in rural areas and make use of this information, this knowledge, this decision, and how they make this decision. We have people here that could really help these small hospitals across the country. Caroline Collin MD Rural Americans have significant health disparities when compared to those living in urban centers. They are more likely to die from heart disease, cancer, injury, chronic respiratory disease, and stroke. Rural areas are finding primary care physicians in short supply, and patients in rural areas are 64 percent less likely to have access to medical specialists for needed referrals. Smaller communities might not have immediate access to experts like a rheumatologist, for example. In addition, patients in more rural areas might not have the means of transportation to get to a specialist, nor have the financial means to pay for specialized visits for a diagnosis. Collins posits that internal medicine generalists might suspect a diagnosis but want to confirm before prescribing a course of treatment. “If I have a patient for whom I am trying to answer a specific question, ‘Does this patient have lupus?’, for instance. I’m not going to be able to diagnose this person with lupus. I can suspect it, but I’m going to ask a rheumatologist. Let’s say I’m in a community where unfortunately, we don’t have a rheumatologist. The patient can’t see a rheumatologist. That’s a real scenario that’s happening in the United States right now. But now I can ask the digital twin acting as a rheumatologist, given these variables, ‘Does this patient have lupus?’ And the digital twin could give me a second opinion.” Sometimes, those experts are incredibly busy and might not have the physical availability for a full consult. In this case, someone could use TOM to create the digital twin of that expert. This allows them to give advice and second opinions to a wider range of fellow physicians. As Kuhnert says, TOM is not designed or intended to be a substitute for a physician. It should only work alongside one. Collins agreed, saying, “This doesn’t take the place of a provider in actual clinical decision-making. That’s where I think someone could use it inappropriately and could get patients into trouble. You still have to have a person there with clinical decision-making capacity to take on additional variables that TOM can’t yet do. And so that’s why it’s a second opinion.” “We’re not there yet in AI says Collins. We have to be really careful about having AI make actual medical decisions for people without someone there to say, ‘Wait a minute, does this make sense?’” AI Implications in the Classroom and Beyond Because organizations use TOM to create digital twins of their experts, the public cannot use the twins to shop for willing doctors. “We don’t want gaming the system,” says Collins. “We don’t want doctor shopping. What we want is a person there who can utilize AI in a meaningful way – not in a dangerous way. I think we’ll eventually get there where we can have AI making clinical decisions. But I don’t think I’d feel comfortable with that yet.” The implications of using decision-making digital twins in healthcare reach far beyond a second opinion for prescription drugs. Kuhnert sees it as an integral part of the future of medical school classrooms at Emory. In the past, teaching case studies have come from books, journals, and papers. Now, they could come alive in the classroom with AI simulation programs like TOM. "I think this would be great for teaching residents. Imagine that we could create a simulation and put this in a classroom, have (the students) do the simulation, and then have the physician come in and talk about how she makes her decisions." Karl Kuhnert “And then these residents could take this decision, and now it’s theirs. They can keep it with them. It would be awesome to have a library of critical health decisions made in Emory hospitals,” Kuhnert says. Collins agreed. “We do a lot of case teaching in the medical school. I teach both residents and medical students at Emory School of Medicine. This would be a really great tool to say, okay, given these set of circumstances, what decision would you make for this patient? Then, you could see what the expert’s decision would have been. That could be a great way to see if you are actually in lockstep with the decision-making process that you’re supposed to be learning.” Kuhnert sees decision-making twins moving beyond the healthcare system and into other arenas like the courtroom, public safety, and financial industries and has been working with other experts to digitize their knowledge in those fields. "The way to think about this is: say there is a subjective decision that gets made that has significant ramifications for that company and maybe for the community. What would it mean if I could digitize experts and make it available to other people who need an expert or an expert’s decision-making?" Karl Kuhnert “You think about how many people aren’t available. Maybe you have a physician who’s not available. You have executives who are not available. Often expertise resides in the minds of just a few people in an organization,” says Kuhnert. “Pursuing the use of technologies like TOM takes the concept of the digital human expert from simple task automation to subjective human decision-making support and will expand the idea of a digital expert into something beyond our current capabilities,” Kuhnert says. “I wanted to show that we could digitize very subjective decisions in such areas as ethical and clinical decision-making. In the near future, we will all learn from the wisdom codified in decision-making digital twins. Why not learn from the best? There is a lot of good work to do.” Karl Kuhnert is a Professor in the Practice of Organization & Management and Associate Professor of Psychiatry, School of Medicine and Senior Faculty Fellow of the Emory Ethics Center. If you're looking to connect with Karl to know more - simply click on his icon now to arrange a time to talk today.
Milwaukee-Based Experts Available During 2024 Republican National Convention
Journalists attending the Republican National Convention (RNC) are invited to engage with leading Milwaukee School of Engineering (MSOE) experts in a range of fields, including artificial intelligence (AI), machine learning, cybersecurity, urban studies, biotechnology, population health, water resources, and higher education. MSOE media relations are available to identify key experts and assist in setting up interviews (See contact details below). As the RNC brings national attention to Milwaukee, discussions are expected to cover pivotal topics such as national security, technological innovation, urban development, and higher education. MSOE's experts are well-positioned to provide research and insights, as well as local context for your coverage. Artificial Intelligence, Machine Learning, Cybersecurity Dr. Jeremy Kedziora Associate Professor, PieperPower Endowed Chair in Artificial Intelligence Expertise: AI, machine learning, ChatGPT, ethics of AI, global technology revolution, using these tools to solve business problems or advance business objectives, political science. View Profile Dr. Derek Riley Professor, B.S. in Computer Science Program Director Expertise: AI, machine learning, facial recognition, deep learning, high performance computing, mobile computing, artificial intelligence View Profile Dr. Walter Schilling Professor Expertise: Cybersecurity and the latest technological advancements in automobiles and home automation systems; how individuals can protect their business operations and personal networks. View Profile Milwaukee and Wisconsin: Culture, Architecture & Urban Planning, Design Dr. Michael Carriere Professor, Honors Program Director Expertise: an urban historian, with expertise in American history, urban studies and sustainability; growth of Milwaukee's neighborhoods, the challenges many of them are facing, and some of the solutions that are being implemented. Dr. Carriere is an expert in Milwaukee and Wisconsin history and politics, urban agriculture, creative placemaking, and the Milwaukee music scene. View Profile Kurt Zimmerman Assistant Professor Expertise: Architectural history of Milwaukee, architecture, urban planning and sustainable design. View Profile Biotechnology Dr. Wujie Zhang Professor, Chemical and Biomolecular Engineering Expertise: Biomaterials; Regenerative Medicine and Tissue Engineering; Micro/Nano-technology; Drug Delivery; Stem Cell Research; Cancer Treatment; Cryobiology; Food Science and Engineering (Fluent in Chinese and English) View Profile Dr. Jung Lee Professor, Chemical and Biomolecular Engineering Expertise: Bioinformatics, drug design and molecular modeling. View Profile Population Health Robin Gates Assistant Professor, Nursing Expertise: Population health expert: understanding and addressing the diverse factors that influence health outcomes across different populations. View Profile Water Resources Dr. William Gonwa Professor, Civil Engineering Expertise: Water Resources, Sewers, Storm Water, Civil Engineering education View Profile Higher Education Dr. Eric Baumgartner Executive Vice President of Academics Expertise: Thought leadership on higher education, relevancy and value of higher ed, role of A.I. in future degrees and workforce development. View Profile Dr. Candela Marini Assistant Professor Expertise: Latin American Studies and Visual Culture View Profile Dr. John Walz President Expertise: Thought leadership on higher education, relevancy and value of higher ed View Profile Media Relations Contact To schedule an interview or for more information, please contact: JoEllen Burdue Senior Director of Communications and Media Relations Phone: (414) 839-0906 Email: burdue@msoe.edu About Milwaukee School of Engineering (MSOE) Milwaukee School of Engineering is the university of choice for those seeking an inclusive community of experiential learners driven to solve the complex challenges of today and tomorrow. The independent, non-profit university has about 2,800 students and was founded in 1903. MSOE offers bachelor's and master's degrees in engineering, business and nursing. Faculty are student-focused experts who bring real-world experience into the classroom. This approach to learning makes students ready now as well as prepared for the future. Longstanding partnerships with business and industry leaders enable students to learn alongside professional mentors, and challenge them to go beyond what's possible. MSOE graduates are leaders of character, responsible professionals, passionate learners and value creators.
AI Art: What Should Fair Compensation Look Like?
New research from Goizueta’s David Schweidel looks at questions of compensation to human artists when images based on their work are generated via artificial intelligence. Artificial intelligence is making art. That is to say, compelling artistic creations based on thousands of years of art production may now be just a few text prompts away. And it’s all thanks to generative AI trained on internet images. You don’t need Picasso’s skillset to create something in his style. You just need an AI-powered image generator like DALL-E 3 (created by OpenAI), Midjourney, or Stable Diffusion. If you haven’t tried one of these programs yet, you really should (free or beta versions make this a low-risk proposal). For example, you might use your phone to snap a photo of your child’s latest masterpiece from school. Then, you might ask DALL-E to render it in the swirling style of Vincent Van Gogh. A color printout of that might jazz up your refrigerator door for the better. Intellectual Property in the Age of AI Now, what if you wanted to sell your AI-generated art on a t-shirt or poster? Or what if you wanted to create a surefire logo for your business? What are the intellectual property (IP) implications at work? Take the case of a 35-year-old Polish artist named Greg Rutkowski. Rutkowski has reportedly been included in more AI-image prompts than Pablo Picasso, Leonardo da Vinci, or Van Gogh. As a professional digital artist, Rutkowski makes his living creating striking images of dragons and battles in his signature fantasy style. That is, unless they are generated by AI, in which case he doesn’t. “They say imitation is the sincerest form of flattery. But what about the case of a working artist? What if someone is potentially not receiving payment because people can easily copy his style with generative AI?” That’s the question David Schweidel, Rebecca Cheney McGreevy Endowed Chair and professor of marketing at Goizueta Business School is asking. Flattery won’t pay the bills. “We realized early on that IP is a huge issue when it comes to all forms of generative AI,” Schweidel says. “We have to resolve such issues to unlock AI’s potential.” Schweidel’s latest working paper is titled “Generative AI and Artists: Consumer Preferences for Style and Fair Compensation.” It is coauthored with professors Jason Bell, Jeff Dotson, and Wen Wang (of University of Oxford, Brigham Young University, and University of Maryland, respectively). In this paper, the four researchers analyze a series of experiments with consumers’ prompts and preferences using Midjourney and Stable Diffusion. The results lead to some practical advice and insights that could benefit artists and AI’s business users alike. Real Compensation for AI Work? In their research, to see if compensating artists for AI creations was a viable option, the coauthors wanted to see if three basic conditions were met: – Are artists’ names frequently used in generative AI prompts? – Do consumers prefer the results of prompts that cite artists’ names? – Are consumers willing to pay more for an AI-generated product that was created citing some artists’ names? Crunching the data, they found the same answer to all three questions: yes. More specifically, the coauthors turned to a dataset that contains millions of “text-to-image” prompts from Stable Diffusion. In this large dataset, the researchers found that living and deceased artists were frequently mentioned by name. (For the curious, the top three mentioned in this database were: Rutkowski, artgerm [another contemporary artist, born in Hong Kong, residing in Singapore] and Alphonse Mucha [a popular Czech Art Nouveau artist who died in 1939].) Given that AI users are likely to use artists’ names in their text prompts, the team also conducted experiments to gauge how the results were perceived. Using deep learning models, they found that including an artist’s name in a prompt systematically improves the output’s aesthetic quality and likeability. The Impact of Artist Compensation on Perceived Worth Next, the researchers studied consumers’ willingness to pay in various circumstances. The researchers used Midjourney with the following dynamic prompt: “Create a picture of ⟨subject⟩ in the style of ⟨artist⟩”. The subjects chosen were the advertising creation known as the Most Interesting Man in the World, the fictional candy tycoon Willy Wonka, and the deceased TV painting instructor Bob Ross (Why not?). The artists cited were Ansel Adams, Frida Kahlo, Alphonse Mucha and Sinichiro Wantabe. The team repeated the experiment with and without artists in various configurations of subjects and styles to find statistically significant patterns. In some, consumers were asked to consider buying t-shirts or wall art. In short, the series of experiments revealed that consumers saw more value in an image when they understood that the artist associated with it would be compensated. Here’s a sample of imagery AI generated using three subjects names “in the style of Alphonse Mucha.” Source: Midjourney cited in http://dx.doi.org/10.2139/ssrn.4428509 “I was honestly a bit surprised that people were willing to pay more for a product if they knew the artist would get compensated,” Schweidel explains. “In short, the pay-per-use model really resonates with consumers.” In fact, consumers preferred pay-per-use over a model in which artists received a flat fee in return for being included in AI training data. That is to say, royalties seem like a fairer way to reward the most popular artists in AI. Of course, there’s still much more work to be done to figure out the right amount to pay in each possible case. What Can We Draw From This? We’re still in the early days of generative AI, and IP issues abound. Notably, the New York Times announced in December that it is suing OpenAI (the creator of ChatGPT) and Microsoft for copyright infringement. Millions of New York Times articles have been used to train generative AI to inform and improve it. “The lawsuit by the New York Times could feasibly result in a ruling that these models were built on tainted data. Where would that leave us?” asks Schweidel. "One thing is clear: we must work to resolve compensation and IP issues. Our research shows that consumers respond positively to fair compensation models. That’s a path for companies to legally leverage these technologies while benefiting creators." David Schweidel To adopt generative AI responsibly in the future, businesses should consider three things. First, they should communicate to consumers when artists’ styles are used. Second, they should compensate contributing artists. And third, they should convey these practices to consumers. “And our research indicates that consumers will feel better about that: it’s ethical.” AI is quickly becoming a topic of regulators, lawmakers and journalists and if you're looking to know more - let us help. David A. Schweidel, Professor of Marketing, Goizueta Business School at Emory University To connect with David to arrange an interview - simply click his icon now.
Aston University researcher takes on leadership role within biomedical engineering
Dr Antonio Fratini is the new chair of the Institute of Mechanical Engineers Biomedical Engineering Division It is one of the largest group of professional biomedical engineers in the UK The specialism merges professional engineering with medical knowledge of the human body, such as artificial limbs and robotic surgery. An Aston University researcher has been given a leading role within the biomedical engineering sector. Dr Antonio Fratini CEng MIMechE has been elected as the new chair of the Biomedical Engineering Division (BmED) of the Institution of Mechanical Engineers (IMechE), one of the largest groups of professional biomedical engineers in the UK. The IMechE has around 115,000 members in 140 countries and has been active since 1847. Biomedical engineering, also known as medical engineering or bioengineering, is the integration of engineering with medical knowledge to help tackle clinical problems and improve healthcare outcomes. Dr Fratini previously served as chair of the Birmingham centre of the division for five years and as vice-chair of the division for one year. His research includes responsible use of AI, 3D segmentation and anatomical modelling to improve surgical training and planning, motor functions and balance rehabilitation. He leads Aston University’s Engineering for Health Research Centre within the College of Engineering and Physical Sciences and has vast experience in the design, development and testing of new medical devices. Currently he is the University’s principal investigator for the West Midlands Health Tech Innovation Accelerator and he has a growing reputation in the UK and internationally within the biomedical engineering profession. He said: “Biomedical engineering is continuously evolving and our graduates will create the future of health tech and med tech for more effective, sustainable, responsible and personalised healthcare. “I am very honoured of this appointment. This three-year post will be a great opportunity to further develop the biomedical engineering profession worldwide and to show Aston University’s commitment to an inclusive, entrepreneurial and transformational impact within the field.” Professor Helen Meese, outgoing chair of the division, said: “I am delighted to see Antonio take on the chair’s position. He has, over the years, contributed significantly to the growth of the Birmingham regional centre and has actively supported me throughout my tenure as chair. I know how passionate he is about our profession and will undoubtedly continue to drive the division forward over the next three years.” Dr Frattini was presented with his new title on 20 June at the IMECHE HQ at 1 Birdcage Walk, London during the Institution’s technology strategy board meeting. For media inquiries in relation to this release, contact Nicola Jones, Press and Communications Manager, on (+44) 7825 342091 or email: n.jones6@aston.ac.uk
One week to go - Let's look at the role debates play in US Elections
US Presidential debates are a cornerstone of American democratic tradition, playing a critical role in shaping public perception and voter decision-making during election cycles. This topic is not only newsworthy because of its historical significance but also due to its influence on political discourse, media coverage, and the democratic process. The evolution of these debates reflects broader societal changes, technological advancements, and shifts in political strategy. Furthermore, presidential debates provide a platform for candidates to present their policies and personalities, thereby directly impacting election outcomes. Key story angles include: Historical Evolution of Presidential Debates: Exploring the origins, key moments, and changes in format and style of presidential debates from the Kennedy-Nixon debate in 1960 to the present day. Impact on Voter Perception and Behavior: Analyzing how debates influence public opinion, voter turnout, and the overall electoral process. Media's Role in Shaping Debates: Investigating the role of media in organizing, broadcasting, and moderating debates, including the influence of television, social media, and real-time fact-checking. Debate Strategies and Candidate Performance: Examining how candidates prepare for debates, notable performances, gaffes, and their impact on campaign momentum. Civic Engagement and Public Discourse: Discussing the role of debates in promoting civic engagement, political education, and public discourse on key issues facing the nation. Technological Advancements and Future Trends: Exploring how technology has transformed debates, from live streaming and interactive features to virtual debates and the use of AI in analysis. These angles offer journalists a comprehensive framework to explore the historical significance and ongoing impact of US Presidential debates on American politics and society. Connect with an Expert about the history of Presidential Debates: Jingsi Christina Wu Associate Professor of Journalism, Media Studies, and Public Relations · Hofstra University John Koch Senior Lecturer and Director of Debate · Vanderbilt University Kevin Wagner, Ph.D. Professor and Department Chair · Florida Atlantic University Juliana Fernandes Assistant Professor · University of Florida Sandra Pavelka, Ph.D. Expert in political science and justice · Florida Gulf Coast University To search our full list of experts visit www.expertfile.com Photo credit: Library of Congress
Aston University researcher develops method of making lengthy privacy notices easier to understand
It has been estimated it would take 76 days per year to fully read privacy notices New method makes notices quicker and easier to understand by converting them into machine-readable formats Team designed a JavaScript Object Notation schema which allowed them to validate, annotate, and manipulate documents. An Aston University researcher has suggested a more human-friendly way of reading websites’ long-winded privacy notices. A team led by Dr Vitor Jesus has developed a system of making them quicker and easier to understand by converting them into machine-readable formats. This technique could allow the browser to guide the user through the document with recommendations or highlights of key points. Providing privacy information is one of the key requirements of the UK General Data Protection Regulation (GDPR) and the UK Data protection Act but trawling through them can be a tedious manual process. In 2012, The Atlantic magazine estimated it would take 76 days per year to diligently read privacy notices. Privacy notices let people know what is being done with their data, how it will be kept safe if it’s shared with anyone else and what will happen to it when it’s no longer needed. However, the documents are written in non-computer, often legal language, so in the paper Feasibility of Structured, Machine-Readable Privacy Notices Dr Jesus and his team explored the feasibility of representing privacy notices in a machine-readable format. Dr Jesus said: “The notices are essential to keep the public informed and data controllers accountable, however they inherit a pragmatism that was designed for different contexts such as software licences or to meet the - perhaps not always necessary - verbose completeness of a legal contract. “And there are further challenges concerning updates to notices, another requirement by law, and these are often communicated off-band e.g., by email if a user account exists.” Between August and September 2022, the team examined the privacy notices of 50 of the UK’s most popular websites, from globally organisation such as google.com to UK sites such as john-lewis.com. They covered a number of areas such as online services, news and fashion to be representative. The researchers manually identified the notices’ apparent structure and noted commonly-themed sections, then designed a JavaScript Object Notation (JSON) schema which allowed them to validate, annotate, and manipulate documents. After identifying an overall potential structure, they revisited each notice to convert them into a format that was machine readable but didn’t compromise both legal compliance and the rights of individuals. Although there has been previous work to tackle the same problem, the Aston University team focused primarily on automating the policies rather than data collection and processing. Dr Jesus, who is based at the University’s College of Engineering and Physical Sciences said: “Our research paper offers a novel approach to the long-standing problem of the interface of humans and online privacy notices. “As literature and practice, and even art, for more than a decade have identified, privacy notices are nearly always ignored and ”accepted” with little thought, mostly because it is not practical nor user-friendly to depend on reading a long text simply to access, for example a news website. Nevertheless, privacy notices are a central element in our digital lives, often mandated by law, and with dire, often invisible, consequences.” The paper was published and won best paper at the International Conference on Behavioural and Social Computing, November 2023, now indexed at IEEE Xplore. The team are now examining if AI can be used to further speed up the process by providing recommendations to the user, based on past preferences.
The 2024 Cision State of the Media Report is jam-packed with all sorts of detailed PR info which can be somewhat overwhelming. But there's an important theme to be found in the data. Kudos to the team at Cision for running this survey that polled over 3,000 staff journalists and freelancers, which is now in its 15th year. The big takeaway for me? Give journalists what they want. Sounds simple enough. Yet, with so many organizations competing for media attention amidst a sea of new AI-enabled platform hacks, many need to focus on the fundamentals of media relations, which this report nicely captures. The media is inundated with pitches. So, the secret to success lies in understanding what jobs journalists have to do and giving them what they need to file their stories…fast. According to the Cision 2024 survey, at the top of the journalists' wish list are: Topical Relevance (68%): Understanding the target audience and what they find relevant. Access to Experts (52%): Connecting journalists with experts and setting up interviews. Credible Data and Research (48%): Providing data and key research. Speed of Response (47%): Responding quickly to inquiries and respecting journalists’ deadlines. In short, journalists want relevant pitches, expert connections, and credible data, and they want it ASAP so they can meet their deadlines. While the Cision report outlines many other best practices that will undoubtedly improve your coverage rate (such as helping journalists quickly source multimedia assets like images), I want to focus on the importance of nailing these first four rules. Rule #1: Pitch Relevant Topics to Journalists Irrelevant pitches not only waste a journalist's time but also damage your credibility. In fact, 77% of journalists in the Cision study cited being spammed with irrelevant pitches as a reason to block a PR professional or put them on the "do not call" list. The study also reported that journalists are "fed up" with follow-ups to unsolicited pitches. Now, only 8% of journalists think it's okay to follow up more than once to check on a story they have pitched. Rule #2: Get Your Experts in Front of Journalists Connecting with credible expert sources is time-consuming. Joint research conducted by ExpertFile and the Associated Press revealed it takes on average, over 2 hours for journalists to secure an expert source for an interview. We can do better than that. As a PR/Media Relations pro, one of your "superpowers" needs to be the ability to spot a story opportunity and get your subject matter experts lined up for the media interview. This is an area where journalists see comms and media pros playing a vital role inside the organization. But if your pitch "sounds like a marketing brochure" the Cision survey shows that 55% of journalists would add you to their naughty list. One of the best ways to avoid this trap and enrich your story is to bring experts and their unique, specialized knowledge to the interview. That means ensuring you are attributing the source of your blog posts to experts in your organization and including links to their expert profile in your pitch. Enclosing a link to an outdated, boring biography on your website or a LinkedIn profile that hasn't been updated since the Yankees last won the World Series (2009), won't score points with journalists. Rule 3: Provide Journalists with Credible Data and Key Research Providing this information not only supports your story but also builds trust. Ensure that your pitches include the latest research findings, statistics, and data from reputable sources within your institution. This evidence-based approach enhances the credibility of your pitches and increases the likelihood of them being picked up by the media. While primary data is best, if you are curating data from other sources, it's critical to cite sources and, ideally, create derivative insights that help the journalist look at the information in a fresh way. For example we have many economists on our ExpertFile network that provide insights regularly on data they didn't gather. But their ability to critically analyze economic data from trusted sources such as the US Census Bureau or the European Union and generate unique, often counterintuitive or provocative insights is what sets them apart from other experts. Rule #4: Help Meet the Journalist's Deadline Journalists often work under tight deadlines and timely responses from PR professionals. Our software has helped organizations handle thousands of media requests every year and if there is one thing we've learned, media is all about speed. If you are a "serious player" you need protocols and processes to quickly respond to media inquiries and get your expert sources lined up to provide the necessary information and insights to meet same-day deadlines. This shows journalists you respect their time and are a reliable source and you will be on speed dial for future stories. Are You Pitching Effectively? Here’s a few tough questions. Answer truthfully. Are You Personally Wasting Time Pitching? How much time do you spend pitching the media vs. responding to inbound media opportunities? Data from Propel Media shows 97% of media pitches fail. While journalists open approximately half of the pitches they receive they only respond to an average of 2.99% of the pitches. Yet the Cision data shows that it's not always your fault. Why? Well, unless you're a gifted psychic, you simply can't know for sure how a journalist is going to react to your pitch. That's why more media departments and their PR agencies are cutting back on spammy pitch activities and moving to more strategic activities that get more traction. With the extra time they save, they can focus on promoting their experts online where journalists are actively searching for credible sources. The result is more qualified inbound inquiries from journalists genuinely interested in interviewing your experts. And that means a lot less anxiety about meeting your media coverage targets. Are You Wasting Journalists' Time? Is your newsroom or media relations page set up to allow journalists to quickly serve themselves 24x7? Can they easily search by specific topics to find an expert within seconds to help meet their deadlines? Or are you expecting them to email or call you for help. (hint: journalists don't have time for that kind of friction). Here's a nice example of how US-based health system, ChristianaCare makes their medical experts available to journalists round the clock while saving hundreds of hours a year for their Comms and Digital team. I'd love to hear more about how you are helping journalists and how that's paying off with increased media coverage. Let me know in the comments below or connect/follow me on LinkedIn or on ExpertFile.
Students at Georgia Southern University now have even more opportunities to excel with the help of two new grants from the National Institute for Student Success (NISS) at Georgia State University totaling up to $600,000. The first is a $500,000 Acceleration Grant that will be distributed to Georgia Southern over the next two years to cover start-up costs for critical implementation steps of an ambitious program that further catalyzes Georgia Southern’s student success outcomes. “As Georgia Southern’s Fall 2024 enrollment continues to increase at record levels through freshman applications and admits, we are pleased to partner with the National Institute for Student Success to reinforce our commitment to providing exceptional student experiences and support that nurtures future leaders and global citizens,” stated Alejandra C. Sosa Pieroni, Ed.D., executive vice president, Division of Enrollment, Marketing and Student Success. “We are steadfast in our commitment to providing comprehensive, coordinated and differentiated student care to ensure every one of our students achieves their educational goals.” Funding through the Acceleration Grant will contribute to Georgia Southern’s existing initiatives to improve graduation rates and student success outcomes through the adoption of a suite of innovative, evidence-based programs. These programs, which deploy tools such as predictive analytics, AI technology, CRM platform, and big data to deliver personalized support to students, have been shown to help universities increase their graduation rates by 50% or more and to reduce discrepancies in graduation rates significantly. The second NISS award to Georgia Southern is the Keep HOPE Alive grant, which offers $100,000 across one year to support students who have lost the HOPE Scholarship as they work to regain eligibility. “We are enthusiastic about our partnership with Georgia Southern and working to deploy strategies that will contribute to substantive improvement in student outcomes,” said Timothy M. Renick, Ph.D., NISS executive director. Housed at Georgia State University, the NISS Acceleration Grants have been awarded to partner institutions that have completed a rigorous diagnostic analysis and have demonstrated a commitment to addressing structural and institutional barriers to the success of their students. Georgia Southern is a member of the second cohort to receive support from the NISS Accelerator Grant program, which launched in 2022. Unique to the Accelerator Grant program is up to three years of implementation coaching, webinars, meetings with expert practitioners and online resources provided by the NISS to support each awardee in implementing the new programs. The NISS coaching model is built on more than a decade of experience at Georgia State University developing and disseminating new approaches to student success that include predictive-analytics-based advising, AI-enhanced chatbots, and data-informed models for distributing financial aid. “These programs have helped Georgia State University increase its graduation rates for its bachelor’s students by 70% and its associate’s students by 300%,” according to Renick. “Black, Hispanic and low-income students now graduate from Georgia State at or above the rate of the student body overall.” The Acceleration Grant program will advance the NISS goal of producing 500,000 additional college graduates across its partner institutions over the next decade. Interested in learning more about this programming at Georgia Southern University or to arrange an interview with Alejandra Sosa Pieroni simply contact Georgia Southern's Director of Communications Jennifer Wise at jwise@georgiasouthern.edu to arrange an interview today.
#ExpertSpot: How Does the Ukraine/Russia War Finally End?
With the war between Russia and Ukraine now approaching two and a half years - there's no end in sight. How does it end? Check out this ExpertSpot from Augusta's Dr. Craig Albert for some insight and perspective. Craig Albert, PhD, is director of the Master of Arts in Intelligence and Security Studies at Augusta University. He is a leading expert on war, terrorism and American politics. AI and the upcoming election is a serious topic. Albert is available to speak with media – simply click on his name to arrange an interview today.
