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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.

Can you benefit in transferring high-interest credit card debt?

Photo credit: paulaveryevans According to Lendingtree, Americans have over $1 trillion in credit card debt. The average American has around $6,500 in credit card debt. When you factor in the high interest that credit cards charge, it can be a daunting task to get the balance to zero. Many cards offer 0% APR on balance transfers for certain length of times. But is it worth it if you don’t plan on paying off the entire balance during the promotional period? Wendy Habegger, PhD, senior lecturer in the James M. Hull College of Business, said you need to be careful when taking advantage of such offers. “The benefit one would get in this situation is short-lived,” said Habegger. “While one might enjoy no interest for the promo period, when that period is over, the interest rate they are charged could be more than the credit card from which they transferred. My recommendation is that if one does a balance transfer, then only do so if you are able to pay off the balance before the period ends.” Some may think of doing a second balance transfer but Habegger said that it is not a good idea and could have a negative impact on a person’s credit score. It also gives the appearance the customer is at increased risk of default, which could trigger an even higher interest rate and higher fees. Not only may one incur higher rates, it could certainly impact their credit score, which can have a long-lasting financial impact. Even a large purchase on a 0% APR card will affect someone’s credit score. “A large purchase indirectly impacts one’s credit score based on credit utilization,” she added. “If one uses more than 30% credit utilization, it could impact credit scores.” Personal debt and credit are trending and important topics in America today - and if you're looking to know more, we can help. Wendy Habegger is a respected finance expert available to offer advice on making the right money moves during volatile times. To arrange an interview, simply click on her icon now.

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.

Is the U.S. banking system on the edge of collapse? Our #expert can explain

The recent bailout of First Foundation Inc, parent of First Foundation Bank, by a group of investors has reignited concerns about the stability of the U.S. banking system, specifically banks exposed to debt in the commercial real estate market teetering on the verge of collapse. First Foundation Bank reported more than $6 billion in commercial real estate mortgages on its financial statements for the first quarter of the year, equal to almost six times its equity capital and almost half of its $13.6 billion in total assets. Bank regulators consider any exposure greater than three times equity to be excessive. An analysis by Rebel Cole, Ph.D., Lynn Eminent Scholar Chaired Professor of Finance in the College of Business at Florida Atlantic University, previously found that the First Foundation Bank was fourth highest among the largest banks in the nation in terms of its exposure to commercial real estate debt. It’s likely that other banks are also at similar risk: among banks with more than $10 billion in total assets, there are 67 that exceed this 300% concentration ratio, Cole’s analysis showed. Among the approximately 4,600 banks of any size, Cole reports that 1,871 have total CRE exposures greater than 300%, 1,112 have exposures greater than 400%, 551 have exposures greater than 500%, and 243 have exposures greater than 600%. Cole is available for expert commentary on the stability of the U.S. banking system, other banks at risk due to their CRE exposure, and investor confidence.

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.

5 Reasons Why Experts Should Drive Your Content Marketing Strategy

It’s a fact: buyers today don’t want to be prospected, demoed, or closed. Whether it’s a procurement officer on the other end of the phone, a prospect reviewing a product online, or a journalist assessing the credibility of a potential spokesperson, “buyers” today expect a more authentic, reliable and practical experience when getting to know an organization, product or service. That’s why understanding how your expertise fits into the buyer’s journey to attract attention, drive interactions and earn trust is becoming critical to success. For marketers today the purchase process has increased in complexity. Today, audiences advance through a process known as the buyer’s journey” – the research and decision-making process that customers go through which progresses from awareness to evaluation and ultimately purchase. The Shift to Expertise Marketing In the early days of marketing and sales, organizations practiced a features oriented “buy what I have” approach; however, these traditional product-oriented marketing approaches are failing to yield the benefits they once did.  Audiences have become far more sophisticated. Research clearly shows that expert content is setting the bar for relevance, credibility and attractiveness for every stage of the buyer journey. Here’s 5 major trends you need to know plus some helpful tips to help you deal with this reality. #1 – Buyers Have Shifted into Self-Serve Mode When Researching Purchases Approximately 67% of the buyer’s journey is complete prior to contacting a vendor (Source: Sirius Decisions) The research continues to show that many buyers would sooner help themselves to content rather than speak to a salesperson, especially in the early stage of the buyer journey. Audiences are increasingly venturing online to doing more of their own research to validate the buying decision.  And they are digging deeper into content and are looking to see the people you have on board to support their decision-making. Tip: Remember that people buy from people. Think about how you can create a more human user experience by giving your experts and their content more profile on your website to drive engagement and build trust. It’s time to go beyond simple headshots and biographies to develop a richer amount of supporting information that feeds your website and search engines. #2- The Buyer Journey is More Collaborative & Non-Linear Than Ever Its clear that the traditional linear sales funnel has disappeared. In B2B markets, buyers now engage with an average of 11.4 pieces of content prior to making a purchase (Source: Forrester Research). They are now more likely to bounce around in a variety of sites. Tip: Evaluate the touchpoints you provide with expert content across your websites and how they interact at various buyer stages, from initial search to content to the connection process. In the end are you making it easy for buyers to engage with the content your experts have to offer? #3 – Experts are a Top Source of Influence in Purchase Behavior Research by the Information Technology Sales and Marketing Association (ITSMA) has consistently ranked subject-matter experts as a top source of information influencing purchase behavior in B2B, higher consideration purchases. In this new model, buyers validate the purchase decision by seeking out reliable information from trusted sources.   Decisions such as what lawyer to choose; what IT platform to invest in or where to study for graduate school can be very positively influenced by expert content. Tip: Ensure you have engaging expert content available online to support buyers across all stages of the buyer journey. Remember they may be looking for additional validation as well as education. #4 – The Buying Process is More Inclusive than Ever with Multiple Personas Playing a Part In addition to consulting industry peers on social media channels, buyers work with colleagues inside their organizations when making purchase decisions. Marketers and salespeople cannot be content with focusing on key decision makers. If you aren’t known company-wide this will present challenges. Tip: Marketers must reach the broader buying group in an organization, which means making larger amounts of expert content with messages targeted to specific personas. Weaving experts into the discussion and engaging more departments within a buyer’s organization will help wield influence on the final buying decision. #5 – Feeding the Search Engines The Right Content Matters More Than Ever According to a Google/Millward Brown study, 71% of business purchases begin with a non-branded search. These generic queries, are from people looking for product first, not for a specific brand or organization name. Huge improvements in organic search rank are possible once when your content is optimized to support the customer at all phases of the buyer journey. Expert content, in the form of articles, infographics, or videos, not only strengthens the trust relationship with your buyer, but also reinforces your value and expertise with search engines. you pay a little more attention to the information structure on your website and add assets such as multimedia content to expert profiles. Search engines continue to reward well developed expert content that has personal attribution with higher trust and authority rankings as it views this content as more relevant. Tip: Start with some tests using Google and Bing to assess how your experts are surfacing on key topics. Also do some searches on the names of your experts to see what position they surface at organically. Where possible add videos, photos, audio, books and social content that you can add to their profiles. Also ensure that the information is properly tagged to allow search engines to properly index this content. 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.

Decoding Hierarchies in Business: When is Having a Boss a Benefit for an Organization?

Most companies around the world have a leader, whether that title is a President, CEO, or Founder. There’s almost always someone at the very top of a corporate food chain, and from that position down, the company is structured hierarchically, with multiple levels of leadership supervising other employees. It’s a structure with which most people in the working world are familiar, and it dates back as long as one can remember. The word itself—leader—dates back to as far as the 12th Century and is derived from the Old English word “laedere,” or one who leads. But in 2001, a group of software engineers developed the Agile Workflow Methodology, a project development process that puts a priority on egalitarian teamwork and individual independence in searching for solutions. A number of businesses are trying to embrace a flatter internal structure, like the agile workflow. But is it necessarily the best way to develop business processes? That’s the question posed by researchers, including Goizueta Business School’s Özgecan Koçak, associate professor of organization and management, and fellow researchers Daniel A. Levinthal and Phanish Puranam in their recently published paper on organizational hierarchies. “Realistically, we don’t see a lot of non-hierarchical organizations,” says Koçak. “But there is actually a big push to have less hierarchy in organizations.” "Part of it is due to the demotivating effects of working in authoritarian workplaces. People don’t necessarily like to have a boss. We place value in being more egalitarian, more participatory." Özgecan Koçak, Associate Professor of Organization & Management “So there is some push to try and design organizations with flatter hierarchies. That is specifically so in the context of knowledge-based work, and especially in the context of discovery and search.” Decoding Organizational Dynamics While the idea of an egalitarian workplace is attractive to many people, Koçak and her colleagues wanted to know if, or when, hierarchies were actually beneficial to the health of organizations. They developed a computational agent-based model, or simulation, to explore the relationships between structures of influence and organizational adaptation. The groups in the simulation mimicked real business team structures and consisted of two types of teams. In the first type, one agent had influence over the beliefs of rest of the team. For the second type, no one individual had any influence over the beliefs of the team. The hierarchical team vs. the flat structured team. “When you do simulations, you want to make sure that your findings are robust to those kinds of things like the scale of the group, or the how fast the agents are learning and so forth,” says Koçak. "What’s innovative about this particular simulation is that all the agents are learning from their environment. They are learning through trial and error. They are trying out different alternatives and finding out their value." Özgecan Koçak Koçak is very clear that the hierarchies in the simulation are not exactly like hierarchies in a business organization. Every agent was purposefully made to be the same without any difference in wisdom or knowledge. “It’s really nothing like the kinds of hierarchies you would see in organizations where there is somebody who has a corner office, or somebody who is has a management title, or somebody’s making more than the others. In the simulation, it’s nothing to do with those distributional aspects or control, and nobody has the ability to control what others do in (the simulation). All control comes through influence of beliefs.” Speed vs. Optimal Solutions What they found in the simulation was that while both teams solved the same problems presented to them, they achieved different results at different speeds. "We find that hierarchical teams don’t necessarily find the best solution, but they find the good enough solution in the shorter term. So if you are looking at the really long term, crowds do better. The crowds where individuals are all learning separately, they find the best solution in the long run, even though they are not learning from each other." Özgecan Koçak For example, teams of scientists looking for cures or innovative treatments for diseases work best with a flat structure. Each individual works on their own timeline, with their own search methodologies. The team only comes together for status updates or to discuss their projects without necessarily getting influence or direction from colleagues. The long-term success of the result is more important in some cases than the speed at which they arrive to their conclusion. That won’t work for an organization that answers to a board of directors or shareholders. Such parties want to see rapid results that will quickly impact the bottom line of the company. This is why the agile methodology is not beneficial to large-scale corporations. Koçak says, “When you try to think about an entire organization, not just teams, it gets more complicated. If you have many people in an organization, you can’t have everybody just be on the same team. And then you have to worry about how to coordinate the efforts of multiple teams. "That’s the big question for scaling up agile. We know that the agile methodology works pretty well at the team level. However, when firms try to scale it up applied to the entire organization, then you have more coordination problems." Özgecan Koçak Özgecan Koçak (pronounced as ohz-gay-john ko-chuck) is associate professor of Organization & Management at Emory University’s Goizueta Business School. If you're looking to know more about this topic or connect with Özgecan for an interview - simply click on her icon 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.

Small Changes Can Save Lives: How a Police Officer’s First Words Can Transform Communities

Britt Nestor knew something needed to change. Nestor is a police officer in North Carolina. Unlike many in her field, who recite interview-ready responses about wanting to be a police officer since childhood, Nestor admits that her arrival to the field of law enforcement was a serendipitous one. Told by teachers to start rehearsing the line “do you want fries with that?” while in high school, Nestor went to college to prove them wrong—and even graduated with a 3.9 GPA solely to prove those same people wrong—but she had absolutely no idea what to do next. When a local police department offered to put her through the police academy, her first thought was, “absolutely not.” “And here I am,” says Nestor, 12 years into her career, working in Special Victims Investigations as an Internet Crimes Against Children detective. A Calling to Serve Community Brittany Nestor, New Blue Co-Founder and President Though she’d initially joined on a whim, Nestor stuck around and endured many growing pains, tasting some of the problematic elements of police culture firsthand. As a woman, there was particular pressure to prove herself; she resisted calling for back-up on dangerous calls for fear of being regarded as weak, and tried out for and joined the SWAT team to demonstrate her mettle. "It took time to realize I didn’t need to make the most arrests or get the most drugs and guns to be a good cop. What was important was recognizing that I was uniquely positioned and given opportunities every single shift to make a difference in people’s lives—that is what I wanted to focus on." Britt Nestor Nestor found she took great pleasure in interacting with different kinds of people all day. She’s deeply fond of her community, where she is also a youth basketball coach. One of her greatest joys is being on call or working an event and hearing someone hail her from the crowd by yelling, “hey, coach!” When she landed in the Juvenile Investigations Unit, Nestor truly felt she’d found her calling. Still, what she’d witnessed in her profession and in the news weighed on her. And she’s not alone; while there is continued debate on the urgency and extent of changes needed, 89% percent of people are in favor of police reform, according to a CBS/YouGov poll. A few weeks after George Floyd’s murder in 2020, Nestor’s colleague Andy Saunders called her and told her they had to do something. It felt like the tipping point. “I knew he was right. I needed to stop wishing and hoping police would do better and start making it happen.” Andy Saunders, New Blue Co-Founder and CEO That conversation was the spark that grew into New Blue. Founded in 2020, New Blue strives to reform the U.S. Criminal Justice system by uniting reform-minded police officers and community allies. The organization focuses on incubating crowd-sourced solutions from officers themselves, encouraging those in the field to speak up about what they think could improve relations between officers and the communities they serve. “Over the years I’ve had so many ideas—often addressing problems brought to light by community members—that could have made us better. But my voice was lost. I didn’t have much support from the police force standing behind me. This is where New Blue makes the difference; it’s the network of fellows, alumni, partners, mentors, and instructors I’d needed in the past.” Nestor and Saunders had valuable pieces of the puzzle as experienced law enforcement professionals, yet they knew they needed additional tools. What are the ethical guidelines around experimenting with new policing tactics? What does success look like, and how could they measure it? The Research Lens Over 400 miles away, another spark found kindling; like Nestor, Assistant Professor of Organization & Management Andrea Dittmann’s passion for making the world a better place is palpable. Also, like Nestor, it was an avid conversation with a colleague—Kyle Dobson—that helped bring a profound interest in police reform into focus. Dittmann, whose academic career began in psychology and statistics, came to this field by way of a burgeoning interest in the need for research-informed policy. Much of her research explores the ways in which socioeconomic disparities play out in the work environment, and—more broadly—how discrepancies of power shape dynamics in organizations of all kinds. When people imagine research in the business sector, law enforcement is unlikely to crop up in their mind. Indeed, Dittmann cites the fields of criminal justice and social work as being the traditional patrons of police research, both of which are more likely to examine the police force from the top down. Andrea Dittmann Dittmann, however, is a micro-oriented researcher, which means she assesses organizations from the bottom up; she examines the small, lesser-studied everyday habits that come to represent an organization’s values. “We have a social psychology bent; we tend to focus on individual processes, or interpersonal interactions,” says Dittmann. She regards her work and that of her colleagues as a complementary perspective to help build upon the literature already available. Where Dittmann has eyes on the infantry level experience of the battleground, other researchers are observing from a bird’s eye view. Together, these angles can help complete the picture. And while the “office” of a police officer may look very different from what most of us see every day, the police force is—at the end of the day—an organization: “Like all organizations, they have a unique culture and specific goals or tasks that their employees need to engage in on a day-to-day basis to be effective at their jobs,” says Dittmann. Theory Meets Practice Kyle Dobson, Postdoctoral Researcher at The University of Texas at Austin What Dittmann and Dobson needed next was a police department willing to work with them, a feat easier said than done. Enter Britt Nestor and New Blue. "Kyle and I could instantly tell we had met people with the same goals and approach to reforming policing from within." Andrea Dittmann Dittmann was not surprised by the time it took to get permission to work with active officers. “Initially, many officers were distrustful of researchers. Often what they’re seeing in the news are researchers coming in, telling them all the problems that they have, and leaving. We had to reassure them that we weren’t going to leave them high and dry. If we find a problem, we’re going to tell you about it, and we’ll work on building a solution with you. And of course, we don’t assume that we have all the answers, which is why we emphasize developing research ideas through embedding ourselves in police organizations through ride-alongs and interviews.” After observing the same officers over years, they’re able to build rapport in ways that permit open conversations. Dittmann and Dobson now have research running in many pockets across the country, including Atlanta, Baltimore, Chicago, Washington, D.C. and parts of Texas. The Rise of Community-Oriented Policing For many police departments across the nation, there is a strong push to build closer and better relationships with the communities they serve. This often translates to police officers being encouraged to engage with citizens informally and outside the context of enforcing the law. If police spent more time chatting with people at a public park or at a café, they’d have a better chance to build rapport and foster a collective sense of community caretaking—or so the thinking goes. Such work is often assigned to a particular unit within the police force. This is the fundamental principle behind community-oriented policing: a cop is part of the community, not outside or above it. This approach is not without controversy, as many would argue that the public is better served by police officers interacting with citizens less, not more. In light of the many high-profile instances of police brutality leaving names like Breonna Taylor and George Floyd echoing in the public’s ears, their reticence to support increased police-to-citizen interaction is understandable. “Sometimes when I discuss this research, people say, ‘I just don’t think that officers should approach community members at all, because that’s how things escalate.’ Kyle and I acknowledge that’s a very important debate and has its merits.” As micro-oriented researchers, however, Dittmann and Dobson forgo advocating for or dismissing broad policy. They begin with the environment handed to them and work backward. “The present and immediate reality is that there are officers on the street, and they’re having these interactions every day. So what can we do now to make those interactions go more smoothly? What constitutes a positive interaction with a police officer, and what does it look like in the field?” Good Intentions Gone Awry To find out, they pulled data through a variety of experiments, including live interactions, video studies and online experiments, relying heavily on observation of such police-to-citizen interactions. "What we wanted to do is observe the heterogeneity of police interactions and see if there’s anything that officers are already doing that seems to be working out in the field, and if we can ‘bottle that up’ and turn that into a scalable finding." Andrea Dittmann Dittmann and her colleagues quickly discovered a significant discrepancy between some police officers’ perceived outcome of their interactions with citizens and what those citizens reported to researchers post-interaction. “An officer would come back to us and they’d say it went great. Like, ‘I did what I was supposed to do, I made that really positive connection.’ And then we’d go to the community members, and we’d hear a very different story: ‘Why the heck did that officer just come up to me, I’m just trying to have a picnic in the park with my family, did I do something wrong?’” Community members reported feeling confused, harassed, or—at the worst end of the spectrum—threatened. The vast majority—around 75% of citizens—reported being anxious from the very beginning of the interaction. It’s not hard to imagine how an officer approaching you apropos of nothing may stir anxious thoughts: have I done something wrong? Is there trouble in the area? The situation put the cognitive burden on the citizen to figure out why they were being approached. The Transformational Potential of the “Transparency Statement” And yet, they also observed officers (“super star” police officers, as Dittmann refers to them) who seemed to be especially gifted at cultivating better responses from community members. What made the difference? “They would explain themselves right from the start and say something like, ‘Hey, I’m officer so-and-so. The reason I’m out here today is because I’m part of this new community policing unit. We’re trying to get to know the community and to better understand the issues that you’re facing.’ And that was the lightbulb moment for me and Kyle: the difference here is that some of these officers are explaining themselves very clearly, making their benevolent intention for the interaction known right from the start of the conversation.” Dittmann and her colleagues have coined this phenomenon the “transparency statement.” Using a tool called the Linguistic Inquiry & Word Count software and natural language processing tools, the research team was able to analyze transcripts of the conversations and tease out subconscious cues about the civilians’ emotional state, in addition to collecting surveys from them after the encounter. Some results jumped out quickly, like the fact that those people whose conversation with an officer began with a transparency statement had significantly longer conversations with them. The team also employed ambulatory physiological sensors, or sensors worn on the wrist that measure skin conductivity and, by proxy, sympathetic nervous system arousal. From this data, a pattern quickly emerged: citizens’ skin conductance levels piqued early after a transparency statement (while this can be a sign of stress, in this context researchers determined it to reflect “active engagement” in the conversation) and then recovered to baseline levels faster than in the control group, a pattern indicative of positive social interaction. Timing, too, is of the essence: according to the study, “many patrol officers typically made transparency statements only after trust had been compromised.” Stated simply, the interest police officers showed in them was “perceived as harassment” if context wasn’t provided first. Overall, the effect was profound: citizens who were greeted with the transparency statement were “less than half as likely to report threatened emotions.” In fact, according to the study, “twice as many community members reported feeling inspired by the end of the interaction.” What’s more, they found that civilians of color and those from lower socioeconomic backgrounds —who may reasonably be expected to have a lower baseline level of trust of law enforcement—“may profit more from greater transparency.” Talk, it turns out, is not so cheap after all. Corporate Offices, Clinics, and Classrooms The implications of this research may also extend beyond the particulars of the police force. The sticky dynamics that form between power discrepancies are replicated in many environments: the classroom, between teachers and students; the office, between managers and employees; even the clinic, between medical doctors and patients. In any of these cases, a person with authority—perceived or enforceable—may try to build relationships and ask well-meaning questions that make people anxious if misunderstood. Is my boss checking in on me because she’s disappointed in my performance? Is the doctor being nice because they’re preparing me for bad news? “We believe that, with calibration to the specific dynamics of different work environments, transparency statements could have the potential to ease tense conversations across power disparities in contexts beyond policing,” says Dittmann. More Research, Action, and Optimism What could this mean for policing down the road? Imagine a future where most of the community has a positive relationship with law enforcement and there is mutual trust. "I often heard from family and friends that they’d trust the police more ‘if they were all like you.’ I can hear myself saying, ‘There are lots of police just like me!’ and I truly believe that. I believe that so many officers love people and want to serve their communities—and I believe a lot of them struggle with the same things I do. They want to see our profession do better!" Britt Nestor “When I get a new case and I meet the survivor, and they’re old enough to talk with me, I always explain to them, ‘I work for you. How cool is that?’ And I truly believe this: I work for these kids and their families.” The implications run deep; a citizen may be more likely to reach out to police officers about issues in their community before they become larger problems. An officer who is not on edge may be less likely to react with force. Dittmann is quick to acknowledge that while the results of the transparency statement are very promising, they are just one piece of a very large story with a long and loaded history. Too many communities are under supported and overpoliced; it would be denying the gravity and complexity of the issue to suggest that there is any silver bullet solution, especially one so simple. More must be done to prevent the dynamics that lead to police violence to begin with. “There’s a common narrative in the media these days that it’s too late, there’s nothing that officers can do,” says Dittmann. Yet Dittmann places value on continued research, action and optimism. When a simple act on the intervention side of affairs has such profound implications, and is not expensive or difficult to implement, one can’t help but see potential. “Our next step now is to develop training on transparency statements, potentially for entire agencies,” says Dittmann. “If all the officers in the agency are interacting with transparency statements, then we see this bottom-up approach, with strong potential to scale. If every interaction you have with an officer in your community starts out with that transparency statement, and then goes smoothly, now we’re kind of getting to a place where we can hopefully talk about better relations, more trust in the community, at a higher, more holistic, level.” While the road ahead is long and uncertain, Dittmann’s optimism is boosted by one aspect of her findings: those community members who reported feeling inspired after speaking with police officers who made their benevolent intentions clear. "That was really powerful for me and Kyle. That’s what gets me out of bed in the morning. It’s worth trying to move the needle, even just a little bit." Andrea Dittmann Looking to know more?  Andrea Dittman is available to speak with media about this important research. Simply click on her icon now to arrange an interview today.

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.