Costello Research Artificial Intelligence

Nonprofits are in trouble. Could more sensitive chatbots be the answer?

Jennifer Anzaldi — Tue, 18 Mar 2025 14:48:25 +0000

Nonprofits are in trouble. Could more sensitive chatbots be the answer? Jennifer Anzaldi Tue, 03/18/2025 - 10:48

In today’s attention economy, impact-driven organizations are arguably at a disadvantage. Since they have no tangible product to sell, the core of their appeal is emotional rather than practical—the “warm glow” of contributing to a cause you care about. But emotional appeals call for more delicacy and precision than standardized marketing tools, such as mass email campaigns, can sustain. Emotional states vary from person to person—even from moment to moment within the same person.

Photo by Getty Images

Siddharth Bhattacharya and Pallab Sanyal, professors of information systems and operations management at the Donald G. Costello College of Business at �鶹��Ƶ, believe that artificial intelligence (AI) can help solve this problem. A well-designed chatbot could be programmed to calibrate persuasive appeals in real time, delivering messaging more likely to motivate someone to take a desired next step, whether that’s donating money, volunteering time or simply pledging support. Automated solutions, such as chatbots, can be especially rewarding for nonprofits, which tend to be cash-conscious and resource-constrained.

“We completed a project in Minneapolis and are working with other organizations, in Boston, New Jersey and elsewhere, but the focus is always the same,” Sanyal says. “How can we leverage AI to enhance efficiency, reduce costs, and improve service quality in nonprofit organizations?”

Siddarth Bhattacharya. Photo provided

Sanyal and Bhattacharya’s working paper (coauthored by Scott Schanke of University of Wisconsin Milwaukee) describes their recent randomized field experiment with a Minneapolis-based women’s health organization. The researchers designed a custom chatbot to interact with prospective patrons through the organization’s Facebook Messenger app. The bot was programmed to adjust, at random, its responses to be more or less emotional, as well as more or less anthropomorphic (human-like).

“For the anthropomorphic condition, we introduced visual cues such as typing bubbles and slightly delayed response to mimic the experience of messaging with another human,” Sanyal says.

The chatbot’s “emotional” mode featured more subjective, generalizing statements with liberal use of provocative words such as “unfair,” “discrimination” and “unjust.” The “informational” modes leaned more heavily on facts and statistics.

Over the course of hundreds of real Facebook interactions, the moderately emotional chatbot achieved deepest user engagement, as defined by a completed conversation. (Completion rate was critical because after the last interaction, users were redirected to a contact/donation form.) But when the emotional level went from moderate to extreme, more users bailed out on the interaction.

The takeaway may be that “there is a sweet spot where some emotion is important, but beyond that emotions can be bad,” as Bhattacharya explains.

Pallab Sanyal. Photo provided

When human-like features were layered on top of emotionalism, that sweet spot got even smaller. Anthropomorphism lowered completion rates and reduced the organization’s ability to use emotional engagement as a motivational tool.

“In the retail space, studies have shown anthropomorphism to be useful,” Bhattacharya says. “But in a nonprofit context, it’s totally empathy-driven and less transactional. If that is the case, maybe these human cues coming from a bot make people feel creepy, and they back off.”

Sanyal and Bhattacharya say that more customized-chatbot experiments with other nonprofits are in the works. They are taking into careful consideration the success metrics and unique needs of each partner organization.

“Most of the time, we researchers sit in our offices and work on these problems,” Sanyal says. “But one aspect of these projects that I really like is that we are learning so much from talking to these people.”

In collaboration with the organizations concerned, they are designing chatbots that can cater their persuasive appeals more closely to each context and individual interlocutor. If successful, this method would prove that chatbots could become more than a second-best substitute for a salaried human being. They could serve as interactive workshops for crafting and refining an organization’s messaging to a much more granular level than previously possible.

And this would improve the effectiveness of organizational outreach across the board—a consummate example of AI enhancing, rather than displacing, human labor. “This AI is augmenting human functions,” says Sanyal. “It’s not replacing. Sometimes it’s complementing, sometimes it’s supplementing. But at the end of the day, it is just augmenting.”

In This Story

Siddharth Bhattacharya

Pallab Sanyal

Topics

A.I. & Innovation - Costello

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Costello Research Machine Learning

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Research

Artificial Intelligence

Building a 'digital twin' of the real world

Jennifer Anzaldi — Mon, 26 Sep 2022 14:02:39 +0000

Building a 'digital twin' of the real world Jennifer Anzaldi Mon, 09/26/2022 - 10:02

In This Story

Jingyuan Yang

As artificial intelligence (AI) has become more adept at anticipating people’s wants and needs, predictive algorithms have been mingled with almost everything we do digitally. When we are done watching a Netflix show, they take the liberty of queueing up the next one—they finish our sentences in Outlook and Gmail.

Jingyuan Yang

In the offline world, however, the complex and multidimensional nature of many of our most pivotal decisions defies algorithmic analysis. That is, unless AI can learn to detect how real-world contingencies, such as specifics of time and place, govern our choices.

Jingyuan Yang, an assistant professor of information systems and operations management at �鶹��Ƶ School of Business, is at the forefront of AI research that aims to crack the codes of the physical world. Her results so far point toward innovative solutions for some of the biggest societal, governmental, and business challenges we face.

Several of her papers to date investigate urban bike sharing, a “last mile” extension of public transport systems designed to coax commuters out of their cars. Some early adopters of bike sharing, such as New York City and Taiwan, have seen long-term success with the model. But elsewhere, including major Chinese cities, oversaturation has led to bicycle-flooded sidewalks or, even worse, rivers and vacant lots turning into bicycle graveyards—an environmental disaster that produced friction with local politicians.

Yang discovered that in Shanghai, part of the problem was that the distribution of bikes across the city did not match demand. Some areas had far more available bikes than riders, while in others the opposite was the case. Additionally, Shanghai’s system allowed commuters to dismount wherever they chose. This dockless model made predicting rider demand even more challenging, as bikes could be located virtually anywhere.

With a team of six other researchers[1], Yang developed a data-driven model for predicting traffic flows within dockless bike-sharing systems, based on a dataset provided by leading provider Mobike. Spanning the period February 2017-March 2018, the Mobike data contained more than 957 million riding records from nearly 315,000 shared bikes.

The research team extracted flow patterns from the data by partitioning the city and “smoothing out” areas with the lowest levels of activity. The resulting grid-like “flow matrix” carved Shanghai’s bike traffic into spatio-temporal snapshots that could be studied and compared. After clustering these based on their similarities, the researchers could construct “base matrices” that provided broader, deeper points of reference for prediction than temporal or geographic cues alone. Using the base matrix, the algorithm could identify emergent patterns in a certain area as those associated with central business districts on a rainy holiday morning and forecast bike traffic in that area over the next few hours accordingly.

Yang says she was surprised by what this technique of algorithmic mapping revealed. “There are a number of surprising factors we can discover that cannot be covered by traditional model analysis. As an example, we find that there is a slight increase in bike traffic near subway stations during rain, because people want a shorter commute,” she says.

The team tested their model against six other algorithms designed for the same purpose, to see which was the most accurate at predicting actual bike traffic flow in Shanghai. Yang’s team’s solution consistently outperformed the rest on sample datasets for regular working days, rainy working days and holidays–meaning it achieved the lowest degree of prediction error. Perfect predictions are impossible, because all sorts of irregular real-world occurrences, from auto accidents to one-off public events, can cause traffic on a given day to break with the pattern. “Really odd events, we cannot capture,” Yang says. “But the base matrix lets us capture basic trends.”

For Yang, optimizing bike sharing is part of a necessary push toward environmentally sustainable options for urban living, including fewer polluting modes of transport. “All these papers are intended to help companies go a more sustainable way and help the user to tackle the last mile in an eco-friendly manner-without waste and damage to the environment.”

With some tweaks to the logic, however, the same AI-based methods can apply to a range of pressing business issues. The territory mapped by algorithms need not be geographical; researchers can also “map” a network of individuals or companies. Yang’s experiment in the field of B2B marketing is a case in point. She helped build[2] an automatic recommendation engine for marketing campaigns based on customer profiles (similar in concept to the bike-sharing base matrices) reflecting corporate affiliation as well as individual employee status. Customers from the same “region” on the grid, i.e. the same company, are treated holistically to improve recommendation quality.

“When you’re selling to a company, you’re usually dealing with a group of decision-makers who are at different buying stages," Yang explains. "To estimate buying propensity, you need to consider that they may share information. Their behavior should be considered together. Therefore, we use matrix representation to extract their shared knowledge.”

One of Yang’s current research projects focuses on predicting employee flow within networks of companies, again borrowing spatio-temporal techniques. Similar to Shanghai’s urban environment, the job-hopping professional grid has its own version of “weather”—favorable or gloomy economic conditions – that may alter the pattern.

“Based on different job positions, you can group the companies. You can aggregate company profiles and predict, collectively, how many people will leave based on the stock price,” she says.

Yang’s research suggests that by building a chessboard-like “digital twin” of the real world, spatio-temporal AI solutions can help business and society predict–and thus prevent–harmful losses such as human capital flight and damage to the natural environment.

[1] Jingjing Gu, Qiang Zhou and Yanchao Zhao (of Nanjing University of Aeronautics and Astronautics), Yanchi Lui and Hui Xiong (of Rutgers University), Fuzhen Zhuang (of Chinese Academy of Sciences)

[2] In collaboration with Chuanren Liu (of Drexel University), Mingfai Teng and Hui Xiong (of Rutgers University) and Ji Chen (Google).