Ramnath K Chellappa

It's going to be a busy week in America when it comes to politics. And if you're covering we have experts who can help with any of your questions or stories. Tom Smith Professor in the Practice of Finance Professor Smith is an expert in labor economics, entertainment and healthcare economics, as well as real estate and urban economies. David Schweidel Professor of Marketing Professor Schweidel has been closely researching the impact of AI in society, especially elections. He can speak on the impact AI is expected to have in this year’s elections. Professor Schweidel also has extensive work in election marketing. He researched negative campaign advertising and if a negative tone has a positive impact on election results. Ramnath Chellappa Professor of Information Systems & Operations Management Professor Chellappa is available to discuss the economics of information security and privacy. He can also discuss the economics and impact of AI. Raymond Hill  Professor Emeritus Hill is available to discuss any issues on the economy related to energy. If you are looking to arrange an interview simply click any of the listed expert's icons to set up a time today or email Kim Speece for assistance.

Artificial intelligence has been hogging headlines around the world in recent months. In late March 2023, an unprecedented coalition of tech CEOs signed an open letter calling for a moratorium on AI training. The race to empower powerful artificial minds should be paused, argued signatories (including Elon Musk) to give humanity time to review and reassess the potential risks of developing “human-competitive intelligence”–intelligence that “no one–not even their creators–can understand, predict, or reliably control.” Concerns about the unchecked rise of AI are not new, and global media is increasingly sounding the alarm, citing concerns that range from invasion of privacy to an existential threat to human existence. Weighing in on this with compelling new evidence around the “unintended consequences” of AI is research by Goizueta’s Ramnath Chellappa and Information Systems PhD candidate, Jonathan Gomez Martinez. Uncovering the Threat Their paper, Content Moderation and AI: Impact on Minority Communities, takes a hard look at how the use of AI in social media could disadvantage LGBTQ+ users. And what they find is worrying. Chellappa, who is Goizueta Foundation Term Professor of Information Systems & Operations Management, explains that he and Gomez Martinez homed in on Twitter to explore how unchecked artificial language moderation might (mistakenly) censor the use of “otherwise toxic” language by failing to understand the context or nuanced use of the LGBTQ+ lexicon. Examples of this include “reclaimed language”—verbiage that would be a slur in other contexts—but is reclaimed and prosocial if used by the originally targeted community. Their paper, Content Moderation and AI: Impact on Minority Communities, takes a hard look at how the use of AI in social media could disadvantage LGBTQ+ users. And what they find is worrying. Chellappa, who is Goizueta Foundation Term Professor of Information Systems & Operations Management, explains that he and Gomez Martinez homed in on Twitter to explore how unchecked artificial language moderation might (mistakenly) censor the use of “otherwise toxic” language by failing to understand the context or nuanced use of the LGBTQ+ lexicon. Examples of this include “reclaimed language”—verbiage that would be a slur in other contexts—but is reclaimed and prosocial if used by the originally targeted community. “This is a community that has ‘reclaimed’ certain words and expressions that might be considered offensive in other contexts. Terms like ‘queer’ are used within the community both in jest and as a marker of identity and belonging. But if used by those outside the community, this kind of language could be deemed inflammatory or offensive.” Gomez Martinez adds: “We wanted to measure the extent to which AI’s lack of a nuanced understanding of what is ‘acceptable’ affects minority users’ online interactions. As humans, we understand that marginalized communities have long used ‘reclaimed words’ both in jest and as a kind of rallying cry. Our intuition was that the machine simply wouldn’t understand this without context—context that is more immediately apparent to people.” Determining the Impact of AI-Based Moderation To test this, he and Chellappa looked at data from social media behemoth, Twitter. During the pandemic in 2020, the platform made a significant shift to AI-based content moderation to accommodate stay-at-home measures. Data from Twitter’s proprietary Academic Research API afforded Gomez Martinez and Chellappa access to a complete listing of historical tweets and replies before, during and after this period. Together they analyzed a total of 3.8 million interactions (1.8 million tweets and 2.0 million replies) from a panel of 2,751 users, of which 1,224 self-identified as LGBTQ+ in their Twitter bios. Their study ran over four months, from January to May 2020, before, during and after the switch to machine-based moderation. Using the same tools that Twitter moderators deploy to moderate interactions, Gomez Martinez and Chellappa were able to measure any increase or decrease in pro-social, in-group teasing and toxic language among LGBTQ+ users: terms such as “bitch” or “queer,” which research shows to be a form of ritualized insults—dubbed “reading” by the community—which can appear inappropriate or incoherent to outsiders, says Chellappa. “Analyzing the language, we find a notable reduction in the use of terms that could be considered toxic. When the AI moderation is in effect, you see these users’ language become more vanilla,” he adds. Quantifiably so, in fact. Chellappa and Martinez find a 27 percent reduction in the use of reclaimed language among LGBTQ+ users. And while that doesn’t sound like much, it’s significant for the community, says Gomez Martinez. Using in-language and reading each other is one way for this marginalized group to create a sense of community and social status. Not just that, we know from research that LGBTQ+ people use slurs and insults as a way of preparing themselves emotionally and psychologically for hostile interaction with heterosexual individuals. This kind of teasing and playing helps build resilience, so any reduction in it is significant.” Jonathan Gomez Martinez Good Intentions May Breed Unexpected Consequences So what does this mean for social media, for the LGBTQ+ community or any marginalized group for that matter, that might be prone to automated censorship? And how does any of this play out in the context of broader concerns around AI? For Chellappa and Gomez Martinez, there is a major hazard in granting technology any degree of control over how human beings interact. And it’s rooted in the mismatch between good intentions and unexpected consequences. Their paper, one of the first to dig into the impact of AI on actual business and society, lays bare some of the real-world impact AI has already had on marginalized people. While this study looks at the LGBTQ+ community, it could equally apply to any group that is prone to bias or exclusion—racial minorities or any other underrepresented demographic. “Wherever you have user-generated content, you are likely to find communities with their own, unique way of interacting. We looked at LGBTQ+ Twitter users, but you could also look at the African American community, for instance.” Ramnath K. Chellapa At a time when social media platforms have become almost newslike in their influence, this is a concern. On the one hand, censoring certain demographics might earn Twitter et al an unwanted reputation for being anti-LGBTQ+ or racist, he adds. But there are even bigger stakes here than bad publicity. “Twitter has long aspired to be a kind of global town square,” says Gomez Martinez. “But you end up pretty far from that scenario if only some voices are truly heard, or if you start reinforcing biases because you are using a time-saving technology that is not equipped yet to understand the complexity and nuance of human interaction.” AI isn’t there yet, say Chellappa and Gomez Martinez. And they caution against using AI indiscriminately to expedite or streamline processes that impact human communication and interchange. If we don’t keep track of it, their research shows that AI has the potential to start dictating and moving people into normative behavior—effectively homogenizing us. And that’s a problem. Looking to know more? Ramnath Chellappa is available to speak with media. Simply click on his icon now to arrange an interview today.

When consumers choose where to shop, they often consider a store’s price image —does the store have a reputation for having lower or higher prices than its competitors? A store’s reputation for lower prices doesn’t happen by chance. Choosing a pricing strategy is one of the biggest pricing decisions a retailer makes. In “When is HILO Low? Price Image Formation Based on Frequency versus Depth Pricing Strategies,” a recently published paper in the Journal of Consumer Research, co-authors Ryan Hamilton, associate professor of marketing, Ramnath Chellappa, associate dean and Goizueta term professor of information systems and operations management, and Daniel Sheehan, associate professor of marketing and supply chain at the University of Kentucky’s Gatton College of Business and Economics, explore a gap in existing pricing strategy research. “Our research doesn’t threaten the validity of the previous research,” said Hamilton, “but what it does do is point to the limited generalizability of the previous research.” This is because previous pricing strategy research used the same research paradigm: It emphasized consumers’ perspectives as they compared prices simultaneously across multiple stores. Hamilton, Chellappa, and Sheehan wondered what would happen if they studied consumers as they compared prices of products within a store, instead of across stores. When they did so, the authors found that “many of the prominent findings of previous research are reversed,” they wrote. “We propose that when stores’ prices are evaluated one at a time, or in isolation, consumers will rely on the most salient contextual clues available—within-category price information—when forming a price image.” For example, rather than research the price of peanut butter across multiple grocery stores, shoppers often evaluate the price of peanut butter by comparing the prices of the brands on the shelf in front of them. To illustrate their point, the authors explore two basic pricing strategies: a frequency pricing strategy and a depth pricing strategy. Every Day Low Pricing (EDLP) is a frequency strategy where stores offer small price advantages over their competitors on many items. Walmart employs an EDLP strategy. A common depth strategy is a high-low (HILO) pricing strategy. HILO offers infrequent, but deep, price advantages over competitors. Macy’s utilizes this strategy. “The conventional wisdom is that EDLP equals low price,” explained Hamilton. But he and his co-authors argue that in a non-theoretical environment, the effectiveness of EDLP strategies is less clear. The trio hypothesized that the context in which consumers encounter prices has important implications. Specifically, that the frequency advantage of EDLP identified in earlier research was limited to those scenarios where customers were able to simultaneously compare prices across multiple stores. In contrast, they argue that a depth advantage, one resulting from HILO pricing, will be more likely when consumers evaluate store prices separately. “Without simultaneous comparisons across stores, consumers shift from using across-store prices as reference points to using within-category reference prices. As a result of this shift, deep price advantages are easier to evaluate than frequent price advantages and therefore more influential on customers’ formation of price image,” they write. “Because our theoretical account is based on within-category external reference prices, we predict that a depth store is likely to be evaluated as having a lower price image than a frequency store even when consumers are exposed to the prices of just one store,” they write. The authors tested their hypothesis using six separate experiments. All but one of the experiments studied national brands commonly found in grocery stores. (The other experiment used televisions.) In the experiments where participants saw store prices simultaneously, the experiment replicated the frequency advantage noted in previous research. But when participants did not have simultaneous price information across stores, the previous findings didn’t hold “What we found is that if you distance those prices comparisons even a little bit -showing a price on one webpage and then seeing a price on another webpage that’s enough to completely reverse the findings,” explained Hamilton. In an isolated setting, “a couple of really low prices” will better communicate a store’s low-price image, said Hamilton. “That’s the big story.” While excited about the findings of their research, Hamilton is quick to point out the limits of their hypothesis, such as when pricing information isn’t readily available or when the consumer isn’t familiar with the brands of the product they wish to buy. “People want a simple answer that works everywhere, but it’s more nuanced than that,” said Hamilton. “This [hypothesis] is going to work better under certain set of circumstances than others because people process price information differently.” The insights aren’t only useful for retailers. While using a store’s price image to shop can be efficient from a consumer standpoint, assuming that the prices are low solely because the store has a reputation for low prices isn’t always the case. A retailer’s price image has vulnerabilities. Not everything at Costco is cheaper than it is at Whole Foods. Southwest Airlines may not always be cheaper than Delta Air Lines. “If you’re shopping for things you really care about,” advised Hamilton, “it might be worth doing more across-store price comparisons.” Chellappa is excited about how the paper addresses gaps in traditional economic models of pricing. “While much research in economics and information systems focuses on the availability of information for price comparison, the cognitive aspect of ‘how’ consumers compare and process such information is only explicated by studies such as ours. Looking at pricing through a behavioral lens, capturing consumers’ real shopping behavior reveals great insights that will be useful for firms,” he said. Interested in learning more about consumer behavior and Price Image Formation Based on Frequency versus Depth Pricing Strategies? Then let us help with your coverage and questions. Ryan Hamilton and Ramnath Chellappa are both available to speak regarding this important topic simply click on either expert's icon now to arrange an interview today.