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Whether getting directions from Google Maps, personalized job recommendations from LinkedIn, or nudges from a bank for new products based on our data-rich profiles, we have grown accustomed to having artificial intelligence (AI) systems in our lives.
But are AI systems fair? The answer to this question, in short—not completely. Further complicating the matter is the fact that today’s AI systems are far from transparent.
Think about it: The uncomfortable truth is that generative AI tools like ChatGPT—based on sophisticated architectures such as deep learning or large language models—are fed vast amounts of training data which then interact in unpredictable ways. And while the principles of how these methods operate are well-understood (at least by those who created them), ChatGPT’s decisions are likened to an airplane’s black box: They are not easy to penetrate.
So, how can we determine if “black box AI” is fair? Some dedicated data scientists are working around the clock to tackle this big issue.
One of those data scientists is Gareth James, who also serves as the Dean of Goizueta Business School as his day job. In a recent paper titled “A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification” Dean James—along with coauthors Bradley Rava, Wenguang Sun, and Xin Tong—have proposed a new framework to help ensure AI decision-making is as fair as possible in high-stakes decisions where certain individuals—for example, racial minority groups and other protected groups—may be more prone to AI bias, even without our realizing it.
In other words, their new approach to fairness makes adjustments that work out better when some are getting the short shrift of AI.
Gareth James became the John H. Harland Dean of Goizueta Business School in July 2022. Renowned for his visionary leadership, statistical mastery, and commitment to the future of business education, James brings vast and versatile experience to the role. His collaborative nature and data-driven scholarship offer fresh energy and focus aimed at furthering Goizueta’s mission: to prepare principled leaders to have a positive influence on business and society. Unpacking Bias in High-Stakes Scenarios Dean James and his coauthors set their sights on high-stakes decisions in their work. What counts as high stakes? Examples include hospitals’ medical diagnoses, banks’ credit-worthiness assessments, and state justice systems’ bail and sentencing decisions. On the one hand, these areas are ripe for AI-interventions, with ample data available. On the other hand, biased decision-making here has the potential to negatively impact a person’s life in a significant way.
In the case of justice systems, in the United States, there’s a data-driven, decision-support tool known as COMPAS (which stands for Correctional Offender Management Profiling for Alternative Sanctions) in active use. The idea behind COMPAS is to crunch available data (including age, sex, and criminal history) to help determine a criminal-court defendant’s likelihood of committing a crime as they await trial. Supporters of COMPAS note that statistical predictions are helping courts make better decisions about bail than humans did on their own. At the same time, detractors have argued that COMPAS is better at predicting recidivism for some racial groups than for others. And since we can’t control which group we belong to, that bias needs to be corrected. It’s high time for guardrails.
A Step Toward Fairer AI Decisions Enter Dean James and colleagues’ algorithm. Designed to make the outputs of AI decisions fairer, even without having to know the AI model’s inner workings, they call it “fairness-adjusted selective inference” (FASI). It works to flag specific decisions that would be better handled by a human being in order to avoid systemic bias. That is to say, if the AI cannot yield an acceptably clear (1/0 or binary) answer, a human review is recommended.
To test the results for their “fairness-adjusted selective inference,” the researchers turn to both simulated and real data. For the real data, the COMPAS dataset enabled a look at predicted and actual recidivism rates for two minority groups, as seen in the chart below.
In the figures above, the researchers set an “acceptable level of mistakes” – seen as the dotted line – at 0.25 (25%). They then compared “minority group 1” and “minority group 2” results before and after applying their FASI framework. Especially if you were born into “minority group 2,” which graph seems fairer to you?
Professional ethicists will note there is a slight dip to overall accuracy, as seen in the green “all groups” category. And yet the treatment between the two groups is fairer. That is why the researchers titled their paper “a burden shared is a burdened halved.”
Practical Applications for the Greater Social Good “To be honest, I was surprised by how well our framework worked without sacrificing much overall accuracy,” Dean James notes. By selecting cases where human beings should review a criminal history – or credit history or medical charts – AI discrimination that would have significant quality-of-life consequences can be reduced.
Reducing protected groups’ burden of bias is also a matter of following the laws. For example, in the financial industry, the United States’ Equal Credit Opportunity Act (ECOA) makes it “illegal for a company to use a biased algorithm that results in credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because a person receives public assistance,” as the Federal Trade Commission explains on its website. If AI-powered programs fail to correct for AI bias, the company utilizing it can run into trouble with the law. In these cases, human reviews are well worth the extra effort for all stakeholders.
The paper grew from Dean James’ ongoing work as a data scientist when time allows. “Many of us data scientists are worried about bias in AI and we’re trying to improve the output,” he notes. And as new versions of ChatGPT continue to roll out, “new guardrails are being added – some better than others.” “I’m optimistic about AI,” Dean James says. “And one thing that makes me optimistic is the fact that AI will learn and learn – there’s no going back. In education, we think a lot about formal training and lifelong learning. But then that learning journey has to end,” Dean James notes. “With AI, it never ends.” Gareth James is the John H. Harland Dean of Goizueta Business School. If you're looking to connect with him - simply click on his icon now to arrange an interview today.
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 Özgecan Koçak (pronounced as ohz-gay-john ko-chuck) is associate professor of Organization & Management at Emory University’s Goizueta Business School. She holds a Ph.D. in organizational behavior from the Graduate School of Business at Stanford University. 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 “You need some way to coordinate the efforts with multiple teams.” The Catch: Compensation Makes a Difference The simulation did not take into account one of the biggest parts of a corporate hierarchical structure—incentives and reward. The teams in the simulation received no monetary compensation for their leadership or influence. That is not something that happens in real life.
Koçak says, “If you built up an organization with just influence, you just say we’re not going to have any authority, and we’re not going to give anybody the right to control anybody else’s actions. If we’re not going to be rewarding anyone more than the other, there’s not going to be any marks of status, etc. We’re just going to have some people influence others more. I would guess that would automatically lead to a prestige hierarchy right away. The person with more influence, you would start respecting more.” It’s almost like we’re incapable of working in a flat society, because somebody always wants to be or naturally becomes a leader and an influencer whether they planned on it or not. Özgecan Koçak The paper concludes that both methodologies, with either hierarchical and flat organization of teams, reach their goals. They just arrive at different times with different end results. If an organization has the luxury of time and money, a flat, agile methodology organization might be the right structure for that company. However, even agile workflow needs some coordination, according to Koçak.
“There are also some search tasks that require coordination. You can’t always be searching on your own independently of others. There are some situations in which search needs to be done in a coordinated fashion by more than one person in teams. That’s because many of the knowledge-based settings where we do discovery require some division of labor, some specialization by expertise.” Communication is Key The key to any successful workflow, whether it be agile or hierarchical, is coordination and communication. Looking back to the example of scientific researchers, Koçak said, “You have scientific teams working independently of one another without a common boss dictating what they do research on or how they do it. Instead, they explore and experiment on their own. They write up their results, share their results, and learn from each other, because they are in the long-term game. The goal is to find the truth, however long it takes.
“But when you look closely at a scientific team where everybody’s exploring, there is still some need for coordination. A lot of that happens through communication, and a lot of times projects will have a lead. Not necessarily somebody who knows better than the others, but somebody who’s going to help with coordination.” The leaner, flatter organizational structures in businesses might be gaining popularity. This simulation done by Koçak and colleagues, however, shows that it isn’t a perfect fit for every company, Further, some form of hierarchical workflow is necessary to maintain communication and coordination. Hierarchical structures don’t always find the best solution to a problem, but it’s almost always a good solution in a timelier fashion.
Looking to know more? Özgecan Koçak is associate professor of Organization & Management at Emory University’s Goizueta Business School. She is available to speak with media about this topic - simply click on her icon now to arrange an interview today.
Why buy vintage? Reasons abound. It’s kinder to the environment. It’s usually cheaper. It’s back in style. But did you know it may also address a deep-seated psychological need for stability amid upheavals?
Vintage consumption—that is, buying previously owned items from an earlier era—acts as a means to connect the past, present, and future. That connection across time can be reassuring, most especially in times of uncertainty. When you really want to buy a leather jacket that’s older than you are, it may be enlightening to consider the circumstances.
This vintage insight reveals itself in research by Ryan Hamilton, associate professor of marketing at Goizueta Business School. In an award-winning article titled “Stitching time: Vintage consumption connects the past, present, and future,” Hamilton—along with coauthors Gulen Sarial-Abi, Kathleen Vohs, and Aulona Ulqinaku—uncovered why we may want to turn to something old when we perceive threats to our worldviews. Notably, multiple studies have shown thoughts of death to increase the appeal of items that have already stood the test of time.
The Psychological Appeal of Thrifting In psychology, “meaning frameworks” are how we, as human beings, interpret and understand our lives as meaningful and valuable. Threats to our meaning frameworks—i.e., the pillars propping up our worldviews—can include thoughts of death, unsettling economic upheavals, and other existential challenges.
In order to explore the effects of meaning threats on our preference for vintage, Hamilton and coauthors designed several studies. Their pilot test measured the physical health of nursing home residents. It then measured their preferences for vintage items, controlling for other variables. The results held up the researchers’ hypothesis: Vintage items—be they books, watches, bicycles, or luggage—were more strongly preferred over their modern versions by elderly participants in poorer health, presumably those most likely to have mortality on their minds.
Six subsequent studies used different variables to see if the main hypothesis continued to hold up. It did, while at the same time revealing more information about the mechanisms at work.
Ryan Hamilton Associate Professor of Marketing Death or Dental Pain In one study, for example, researchers prompted participants with death reminders. They had to contemplate and write about their own deaths to make sure mortality was top of mind. Researchers prompted a control group with reminders of dental pain. Both groups then answered a 12-question survey about their desire for structure (e.g., set routines and practices) at that particular moment. But there was another element in this study: contemplating wearing a watch from the 1950s. As predicted by the main hypothesis, death cues were associated with participants reporting that they desired more structure. The only exceptions was for those who imagined an old watch ticking on their wrists. Vintage consumption seemed to act as a buffer against unsettling thoughts of death for them.
What is going on here? As noted above, the researchers theorize and show that vintage objects tend to connect our thoughts of the past, present, and future. These mental, intertemporal connections tend to be reassuring—“a hidden factor” in our preferences and choices, as Hamilton notes.
More than Nostalgia One might think nostalgia—a sentimental longing for the past—could also be at work. Feeling nostalgic for one’s own past and social connections can buffer against meaning threats, as previous research has shown. But this paper was designed to tease out nostalgia. It focused on vintage’s connections across time regardless of one’s personal experiences.
“This study allowed us to clearly show that people respond differently to something they believe to be old,” as Hamilton explains. “It’s not just something that has a retro look, which was one of my favorite aspects of this project.” Hamilton and his coauthors achieved this by having participants evaluate identical items thought to be genuinely vintage or replicas. And the results were robust. Retro replicas, which can prompt nostalgia, did not have the same psychological impact as items believed to be genuinely old. For instance, 20-year-olds who find a watch from the 1950s reassuring can’t feel nostalgic about the design personally. They can, however, feel a connection across time—and that came through in the study.
Retail Therapy on the Rise? Hamilton’s research here follows his broader interest in consumer psychology, branding, and decision-making. “When we’re buying things, we may think it’s based on strict utility maximization. However, it also might be making us feel better in some way,” says Hamilton. Shopping can serve as an emotional management strategy—for better or for worse.
Although it was outside the scope of this particular investigation (and all participants were over age 18), the insights gleaned here may help explain why 21st-century teenagers seem to be particularly avid “thrifters” these days. “I don’t want to overstate our findings. But it’s at least possible that the appeal of vintage for teenagers is boltstered by a sense of permanence and endurance that helps them during times of upheaval,” Hamilton says.
It turns out a 30-year-old leather jacket might help its new owner feel better on many levels. So is it any wonder that vintage shopping is surging in uncertain times? Fashion magazines, such as Vogue and GQ, are following the vintage craze closely in 2024. Concern for climate change and the Earth’s finite resources may present two intertwined reasons to buy old things: those two things are environmental and psychological. If tumultuous times continue amid contentious elections, wars, and other threats, it seems safe to bet on vintage.
Ryan Hamilton is associate professor of Marketing at Emory University - Goizueta Business School. If you're a journalist looking to know about this topic, simply click on his icon now to arrange an interview today.
