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Bridging the language gap: How AWE software fosters inclusivity for ELs and Non-ELs alike
When ChatGPT burst onto the scene in November 2022, many educators and parents worried that new writing tools powered by artificial intelligence (AI) would help their students bypass important learning opportunities. Instead, as University of Delaware associate professor Joshua Wilson has shown, AI-powered writing and evaluation tools have actually helped students develop their writing skills and have supported teachers in providing meaningful feedback. Now, in a recent study published in Learning and Instruction, Wilson and his co-authors turn their attention to elementary English learners (EL), investigating how this growing population of students interacts with and benefits from automated writing evaluation (AWE) software. AWE is a class of educational technology tools that use natural language processing and AI to provide students with automated formative feedback that supports improvements in writing quality. They found that AWE technologies are equally beneficial for ELs as they are for non-ELs. Study participants accessed writing feedback to a similar extent, achieved equal gains in writing quality, focused on consistent dimensions of writing when revising and endorsed the AWE system to similar degrees, regardless of their language status. “As AI-based feedback applications become increasingly prevalent, it’s critical that researchers examine the consequences of implementing those tools in authentic educational settings, with a particular focus on equity,” said Wilson. Wilson’s study focuses on MI Write, an AWE system designed to improve the teaching and learning of writing by providing students with automated feedback and writing scores. To investigate interaction with the AWE software, Wilson and his co-authors looked at three dimensions of engagement: behavioral, or the actions students take in response to feedback; cognitive, or the thinking and revision strategies that students use in response to feedback; and affective, or how students feel about and perceive feedback. Across all three dimensions, Wilson and his co-authors found similar levels of engagement across all students. They also found that the overall improvements in student writing over the course of the school year was not related to language status. But, even in light of these positive findings, Wilson emphasizes that it’s important to view AWE as a teaching tool rather than as a replacement for classroom teaching. For more on Wilson's research or to speak to him about AI in the classroom, click on his profile and reach out to him.
The Vanderbilt Policy Accelerator for Political Economy and Regulation (VPA) is leading the way in research and policy recommendations on the governance of artificial intelligence. At the Third Annual Networks, Platforms & Utilities conference hosted by the VPA in June, the groundbreaking initiative was commended by FTC Chair Lina Khan for its impact on her work with the agency. As part of Discovery Vanderbilt, Vanderbilt Policy Accelerator for Political Economy and Regulation is a groundbreaking initiative to bolster innovative research and education at Vanderbilt. The mission of VPA is to swiftly develop and advance cutting-edge research, education and policy proposals at a pace that aligns with the urgency of today’s challenges. The VPA encompasses several projects, including one dedicated to revitalizing the study of the law and political economy of networks platforms, and utilities (NPUs) in transportation, communications, energy and banking. “Many of our country’s most pressing economic and social challenges are directly tied to how we govern network, platform, and utility industries, including airline flight cancellations, social media regulation, banking failures and electric grid crashes,” said Ganesh Sitaraman, the New York Alumni Chancellor’s Chair in Law at Vanderbilt Law School and director of VPA. VPA’s Project on Networks, Platforms and Utilities has developed a series of papers and policy proposals to improve the governance of these sectors. Among this work are a set of proposals to policymakers for regulating air travel, a plan for stabilizing and regulating the banking sector, and 40 recommendations to promote competition throughout the American economy. With growing interest in AI, VPA has turned its eye to how policymakers can address the harms that come from concentration in the AI technology stack. VPA’s papers have developed an antimonopoly approach to regulating AI, addressed public capacity for AI, and offered proposals on federal procurement of AI resources. VPA’s work in this field has gotten increasing attention. VPA director Ganesh Sitaraman participated in one of the U.S. Senate’s AI Fora in 2023. And during the Third Annual Networks, Platforms & Utilities conference hosted by the VPA in June, FTC Chair Lina Khan specifically noted VPA’s impact on the agency. “I think the work that VPA has been doing on AI has been so enormously useful,” said Khan. “It’s really striking how it took 15 years before the NPU toolkit was even discussed alongside the Web 2.0 giants. So, the fact that from the very get-go this kind of framework is being applied in the context of AI policy discussions really marks that forward movement.” During the June conference, participants—which included 64 attendees from 15 different countries— discussed how their jurisdictions of study approach the regulation of network, platform and utility industries. This year’s conference was structured around eight panels, one on general themes and seven featuring a specific NPU sector: railroads, electricity, banking & finance, airlines, social infrastructure, tech platforms and telecommunications. “Vanderbilt is a leader in research on these topics, and we were very excited to welcome scholars from around the world to Nashville and to Vanderbilt, in order to explore these issues from a comparative and global perspective,” said Sitaraman. In the coming months, the conference organizers intend to compile the papers presented at the conference into an edited volume. To learn more, visit the Vanderbilt Policy Accelerator website.
Could This Be the Ultimate Way to Showcase Your Experts?
Getting more media coverage is all about helping journalists find everything they need to get their stories out on deadline. Simple right? Well, that depends. Our research shows that most media relations and comms departments are significantly resource-constrained when it comes to pitching experts. And even when you are pitching it’s a challenge. Industry research shows that 97% of pitches fail to generate coverage. The secret is to publish content that draws in journalists in a way that helps them immediately understand (within seconds) how you can help them enhance their stories with your experts. What if there was a way to get all this done in minutes? Not days. Welcome to our latest Spotlight release, designed to help you organize your expert content in the most engaging ways possible. We’ve made enhancements in 5 key areas: Create a More Engaging Design that is Optimized for Mobile Your brand’s identity matters. Our new design ensures your Spotlight Posts reflect your unique style and voice. With bolder headers, enhanced logo placements, customizable fonts, and color schemes, you can create more visually stunning posts. And unlike a lot of other websites, your pages will be beautifully optimized for mobile—which is how most journalists will see your content. Tell a More Visual Story with Images Research from HubSpot indicates that content with relevant images gets 94% more views than content without. It’s time to get more visual. With our higher image resolution plus new editing tools like text wrap and captions, you can really make your images stand out. Plus we’ve helped solve the problem of sourcing images. We’ve now added access to thousands of royalty-free stock images for your posts - it’s all covered as part of your ExpertFile subscription. Make Your Experts Really Stand Out We’ve now made it even easier to display your experts more prominently with enhanced “expert callouts,” which are specially designed to engage journalists with the key information they are looking for. And our pagination features allow you to add content that sets your experts apart. Within seconds you can add videos and images or even stylized quotes from your previous media coverage. Leverage the Latest AI Tools for Faster Content Creation We’ve turbocharged our AI writing tools using OpenAI’s latest release. Enhance your content by generating innovative story ideas and draft posts with AI. This power is all conveniently built right into our editor to save you time. Save Time with Content Repurposing Creating high-quality content takes time and effort, and we want to help you get the most out of it. With our new publishing date feature, along with our current scheduling capabilities, it is easier than ever to make use of existing content effectively. This gives it a second life as part of your expertise marketing efforts while allowing you to better connect it to your experts to drive inquiries. Clone Your Posts for Even Faster Creation. Being able to leverage that perfectly crafted post going forward quickly and easily can help you jump on opportunities as they present themselves. With cloning you can take the layout elements and simply updated the content to highlight new experts or areas of expertise that you wish to showcase. And that's not all… You’ll still enjoy all the current benefits of "Spotlight Posts," including distribution through expertfile.com, integration into expert profiles, full SEO compliance with advanced meta and schema data, and various options for adding this valuable content to your website. Ready to elevate your expertise marketing game? Dive into these new features and watch your content—and your experts—shine brighter than ever. Want to see it in action? Check out the sample we’ve shown here, which we generated with Milwaukee School of Engineering (MSOE) to feature their experts during the Republican National Convention. We can also set you up with a customized demo showing how all this can make your experts stand out. Let us know what you think! About ExpertFile ExpertFile is changing the way organizations tap into the power of their experts to drive valuable inquiries, accelerate revenue growth, and enhance their brand reputation. Used by leading corporate, higher education and healthcare clients worldwide, our award-winning platform helps teams structure, manage and promote their expert content while our search engine features experts on over 50,000+ topics. Download our "Guide to Expertise Marketing", book a demo and more here.
Ethical Implications of AI in Business: Balancing Innovation with Responsibility
Artificial Intelligence (AI) has revolutionized the business landscape, driving innovation and reshaping industries. From automating routine tasks to enhancing customer experiences, AI's applications are vast and rapidly expanding. As businesses stand on the brink of unprecedented technological advancement, they must also navigate the complex web of ethical implications associated with AI deployment. This delicate balance between innovation and responsibility sets the stage for an ongoing dialogue that is crucial for sustainable growth and societal well-being. Dr. Jeremy Kedziora, associate professor and the PieperPower Endowed Chair in Artificial Intelligence at Milwaukee School of Engineering (and former CIA chief methodologist), is available to discuss how these new technologies are enhancing business operations along with their ethical implications: Automating tasks using AI Using large language models like ChatGPT Algorithmic bias in AI systems Transparency in AI decision-making processes Steps needed to create fair and equitable AI solutions
From Facial Recognition to Deepfakes: What Could Be Done With Your Image?
Facial Recognition: Convenience and Controversy Facial recognition technology is everywhere, making our day-to-day tasks faster and more convenient. It offers substantial benefits, from enhanced security measures to streamlined user experiences. Airports utilize it for faster check-ins, smartphones use it for secure authentication, and law enforcement agencies employ it for identifying suspects. However, the technology also raises considerable privacy concerns. The pervasive deployment of facial recognition without adequate oversight can lead to unwarranted surveillance, potential biases in profiling, and the erosion of personal privacy. The Rise of Deepfake Technology Meanwhile, deepfake technology has advanced rapidly, leveraging AI to create highly realistic synthetic, or "fake", media. These hyper-realistic videos, showing individuals doing or saying things they never actually did, have become a significant concern. The potential misuse of deepfakes ranges from spreading misinformation and manipulating elections to causing personal distress by enabling crimes like fraud and defamation. Dr. Derek Riley, a seasoned media expert, professor and program director of the B.S. in Computer Science program at Milwaukee School of Engineering, is available to discuss how these technologies work, how they're regulated, how they can be used in a positive manner, and how individuals can protect themselves.
Key topics at RNC 2024: Artificial Intelligence, Machine Learning and Cybersecurity
As the Republican National Convention 2024 begins, journalists from across the nation and the world will converge on Milwaukee, not only to cover the political spectacle but also to cover how the next potential administration will tackled issues that weren't likely on the radar or at least front and center last election: Artificial Intelligence, Machine Learning and Cybersecurity With technology and the threats that come with it moving at near exponential speeds - the next four years will see challenges that no president or administration has seen before. Plans and polices will be required that impact not just America - but one a global scale. To help visiting journalists navigate and understand these issues and how and where the Republican policies are taking on these topics our MSOE experts are available to offer insights. Dr. Jeremy Kedziora, Dr. Derek Riley and Dr. Walter Schilling are leading voices nationally on these important subjects and are ready to assist with any stories during the convention. . . . 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 “Artificial intelligence and machine learning are part of everyday life at home and work. Businesses and industries—from manufacturing to health care and everything in between—are using them to solve problems, improve efficiencies and invent new products,” said Dr. John Walz, MSOE president. “We are excited to welcome Dr. Jeremy Kedziora as MSOE’s first PieperPower Endowed Chair in Artificial Intelligence. With MSOE as an educational leader in this space, it is imperative that our students are prepared to develop and advance AI and machine learning technologies while at the same time implementing them in a responsible and ethical manner.” MSOE names Dr. Jeremy Kedziora as Endowed Chair in Artificial Intelligence MSOE online March 22, 2023 . . . 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 “At this point, it's fairly hard to avoid being impacted by AI," said Derek Riley, the computer science program director at Milwaukee School of Engineering. “Generative AI can really make major changes to what we perceive in the media, what we hear, what we read.” Fake explicit pictures of Taylor Swift cause concern over lack of AI regulation CBS News January 26, 2024 . . . 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 School of Engineering cybersecurity professor Walter Schilling said it's a great opportunity for his students. "Just to see what the real world is like that they're going to be entering into," said Schilling. Schilling said cybersecurity is something all local organizations, from small business to government, need to pay attention to. "It's something that Milwaukee has to be concerned about as well because of the large companies that we have headquartered here, as well as the companies we're trying to attract in the future," said Schilling. Could the future of cybersecurity be in Milwaukee?: SysLogic holds 3rd annual summit at MSOE CBS News April 26, 2022 . . . For further information and to arrange interviews with our experts, please contact: 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.
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.
