May 20th Speakers:
Yulin Yu, Postdoctoral Fellow, Kellogg School of Management
Feihong Xu, PhD Candidate, McCormick School of Engineering
Maalvika Bhat, PhD Student, School of Communication
Rochana Chaturvedi, Postdoctoral Fellow, Kellogg School of Management

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Talk Titles and Abstracts:

Yulin Yu
Postdoctoral Fellow
Kellogg School of Management &
Northwestern Institute on Complex Systems

Human–AI Creative Pathways: How People and Machines Differ in Creative Strategy
Generative AI offers the promise of amplifying creativity by recombining knowledge at a scale far beyond human capacity, yet humans still hold key advantages in flexibility and contextual reasoning. To understand how each achieves novelty, we analyzed more than 5,000 responses to the Divergent Association Task from both humans and AI systems using network-based methods. We find that while individual humans use fewer and simpler conceptual categories than machines, the collective diversity of human ideas is substantially higher. Human creative pathways tend to follow a one-directional but highly unpredictable trajectory, whereas AI systems rely on repetitive, back-and-forth exploration patterns. Finally, both humans and machines show anchoring effects—early ideas shape later responses—but in opposite ways: humans anchor low, while machines anchor high.

Feihong Xu
PhD Candidate
Engineering Sciences & Applied Mathematics
McCormick School of Engineering

A Well-Calibrated Model Similarity Measure for Arbitrary Neural Networks
Deep learning approaches have transformed biological and biomedical image analysis, but model opacity and fragility remain major obstacles to trustworthy use. One barrier is the lack of a well-calibrated measure of similarity across arbitrary neural networks trained with different architectures, checkpoints, random initializations, and training strategies. Existing notions of model similarity span functional and representational domains, often rely on heuristic assumptions, and are susceptible to spurious signals introduced by probing samples, making principled cross-model meta-analysis difficult. Here, we clarify prevailing notions of deep neural network similarity and benchmark their robustness under extensive out-of-distribution perturbations. We then introduce the Ahmad RV coefficient on chain weight matrices (wARV), a theoretically grounded weight-space similarity measure that combines chain-normalized weights with the RV coefficient. wARV is sample-agnostic, symmetric, computationally efficient, and better calibrated than current measures. Across benchmarks varying random initialization, training checkpoint, architecture, and training strategy, wARV more faithfully tracks functional similarity while avoiding confounding effects from probing data. Applying wARV to deep neural network models on both generic and medical image classification tasks, we uncover substantial learning heterogeneity and instability even among models with similar predictive performance.

Maalvika Bhat
PhD Student
Technology and Social Behavior
School of Communication &
McCormick School of Engineering

Scholars See Clickbait as a Greater Threat to Science Than to Their Own Work
As scientific research competes for attention in a media landscape driven by sensationalism, the risks of misrepresentation grow. This study examines whether academics, while widely recognizing clickbait as a threat to science broadly, tend to downplay its relevance to their own work. Surveying 5,603 U.S.-based researchers, we find a consistent perception gap between systemic and personal risk, one that varies by career stage and disciplinary context. Early-career scholars show a pronounced version of this asymmetry: they express heightened concern about clickbait’s harms to science while rating its relevance to their own work as comparatively lower, a pattern that leaves them most exposed at a stage when reputational stakes are highest.

Rochana Chaturvedi
Postdoctoral Fellow
Kellogg School of Management &
Northwestern Institute on Complex Systems

Who Gets the Callback? Generative Artificial Intelligence and Gender Bias
Large language models are increasingly embedded in hiring workflows, raising concerns about their potential to amplify societal biases — yet how these biases manifest within and across occupations, and the role of model 'personality' in shaping these biases, remains unexplored. We introduce a three-part attribution framework applied to 332,044 real-world job ads, measuring gender-based callback bias, associations of skills and traits with gendered stereotypes in LLMs, and the effect of simulated recruiter personas. We find that LLMs systematically favor men, especially in higher-wage roles, with their decisions tracking traditional gendered language cues in job postings. Notably, assigning a low-agreeableness persona reduces model bias, implicating sycophancy as a mechanism reinforcing societal stereotypes; at the same time, controversial personas trigger internal guardrails leading to more cautious and less-biased outputs. These findings highlight how alignment choices in AI-driven hiring systems shape bias, with important implications for fairness and diversity.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/98031689779

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, networks, and artificial intelligence. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.

Related Info

MAY MEETING: Thursday, May 28, 2026 at 5:30pm (US Central)

LOCATION:
ESAM Conference Room, Tech M416
2145 Sheridan Road, Evanston, IL 60208

AGENDA:
5:30pm - Meet and greet with refreshments
6:00pm - Talk with Xudong Tang, PhD Student, Computer Science, NICO, and the Human-AI Collaboration Lab, Northwestern University

TALK TITLE:
Human and Machine Perception of Voice Similarity

ABSTRACT:
Modern voice cloning systems generate synthetic speech that listeners frequently cannot identify as being synthetic. But a voice can sound natural without sounding like the intended person, and what determines whether a clone is heard as a particular person is an open question. Here we report a large-scale preregistered experiment in which we collected 92,239 responses from 175 participants on their perception of pairs of real recordings, voice clones, and continuously morphed voices drawn from 100 contemporary celebrities across 20 speaker groups. We find that voice clones do not reliably preserve perceived speaker identity, reducing same-speaker judgments by 12.7 percentage points even though the clones are produced by a state-of-the-art text-to-speech model, while leaving different-speaker judgments unchanged. Using continuously morphed stimuli, we find that speakers vary substantially in how much variation their perceived identity tolerates, and that this variation is not predicted by speaker demographics. Speaker embeddings account for 58.9\% (95\% CI = [55.7, 61.9]) of variance in identity judgments, which is more than acoustic features, social attributes, and clone status combined. Once all these observed features are accounted for, clone status adds no additional predictive power. These results shows that the perceptual impact of voice cloning is positional rather than categorical: we can model how listeners judge a voice by how close it falls to the perceptual boundary that defines each speaker's recognizable voice, applying the same criterion to real and synthetic speech alike.

DATA SCIENCE NIGHTS are monthly meetings featuring presentations and discussions about data-driven science and complex systems, organized by Northwestern University graduate students and scholars. Students and researchers of all levels are welcome! For more information: http://bit.ly/nico-dsn

FUTURE DATES:
Data Science Nights will return in September!

Related Info