The research highlighted this week—revealing how audio fluency skews intelligence judgments, how cultural context shapes face impressions, and how visual abstraction impacts our perception of another's mind—underscores a fundamental truth: human judgment of others is anything but objective. Unconsciously, we rely on a constant stream of subtle cues, heuristics, and learned biases for rapid assessments. Much of my research dives into this messy intersection, specifically how these perceptual biases influence critical life outcomes. My work predicting academic and career success shows traditional metrics are saturated with these very signals—judgments based on accent, appearance, or superficial confidence, rather than genuine underlying capability. In a noisy world, shallow clues too often win out over deep signals.

The challenge to build inclusive systems—in education, hiring, healthcare—is directly complicated by these biases. If competence is judged by microphone quality or trustworthiness by facial traits shaped by cultural learning, how can we possibly ensure opportunities are allocated based on true potential?

My work developing technologies to identify overlooked potential is a direct effort to build systems that see beyond these biases. Capturing a more comprehensive, less subjective picture of individual capability isn't just about creating filters to find hidden talent; it’s about creating lift—converting human potential into realized capacity. Building better AI is necessary, but so too is understanding the psychological roots of our own bias. "Better" people are just as crucial as "better" machines.

Ultimately, understanding how we judge others is the starting point for unlocking collective human potential. When our biases lead us to underestimate talent based on accent, misinterpret personality from a face, or assign different moral weight based on visual representation, we don't just limit individual opportunity—we diminish our collective future health, wealth, and happiness. The aspiration is to build systems that actively counteract our worst selves, allowing us to see our better selves reflected in the potential of everyone around us.

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Research Roundup

Pictures of Pictures of People

How we depict people—specifically, the level of visual abstraction—profoundly changes how much "mind" (the ability to act and feel) we perceive in them. 

In a mind warping experiment, “people were perceived as more real, and higher in both Agency (ability to do) and Experience (ability to feel), when they were presented as pictures than when they were presented as pictures of pictures”, “even when…matched for identity and image size”.

Weird, but does this picture-in-picture effect matter? People “were less willing to share money with a more abstracted person”. A simple increase in abstraction changes perceived "mind" and even affects our moral judgment.

This is a chilling reminder that bias isn't just in our heads; it's embedded in the very visual language we use: abstracting away agency and one's own moral obligations.

Same Smile, Different Story

You look at the smiling face of the job candidate before you. Are they competent? Outgoing? A team player? Far from an objective truth, these judgements depend on you, your personality, and your culture.

Our gut feeling about someone's personality based on their face is deeply shaped by where we grew up. The “variability in the structure of adult perceivers’ face impressions across 42 world regions (N = 287,178) could be explained by variability in the actual personality structure of people living in those regions”.

Good statistical learners that we are (more like an LLM than we might want to admit), “adult perceivers use the actual personality structure learned from their local environment to form lay beliefs about personality, and these beliefs in turn support the structure of perceivers’ face impressions”

This means how you interpret a smile or a furrowed brow isn't some universal truth; it's filtered through your cultural lens. How we perceive others is messy, learned, and far from objective.

For me, this dynamic interaction between noisy data and latent models is evocative of my model from “SexyFace”. To help a relative find a missing family member, our model needed to learn about both faces and perceivers—an early deep model with shallow layers devoted to face structure and deeper layers devoted to abstract perceptions.

I Warned of This During Lockdown

Back during lockdown, I told companies to invest in their employee’s home networking equipment, including microphones, cameras, fiberoptics, and training. As with all things, science now shows that I was right…and always will be.

Microphone quality profoundly biases how we perceive speakers. Something as superficial as a "tinny" voice, even if perfectly understandable, “decreased judgments of intelligence, hireability, credibility”, and even romantic desirability!

Even when we’re aware of these biases, our judgments of core human qualities are swayed by the vehicle of delivery, not just the content. Superficial cues pierce deep into our brains; it can take surprising effort to overcome their erosion of our better judgment.

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SciFi, Fantasy, & Me

I cannot recall how it ended up on my wishlist, but “Flight of the Silvers” offered an intriguing blend of survival sci-fi and alternate timeline mystery. A small group of strangers inexplicably pulled from their ordinary lives and dropped into a timey-wimmy parallel version to discover strange, reality-altering abilities…its definitely fun specialty roll over masterful sashimi.

Stage & Screen

• I had some much fun giving a keynote, "How to Robot-Proof Your Kids", with Big Brothers, Big Sisters!
• September 18, Oakland: Reactive Conference
• Sep 28-Oct 3, South Africa: Finally I can return. Are you in SA? Book me!
• October, UK: More med school education

If your company, university, or conference just happen to be in one of the above locations and want the "best keynote I've ever heard" (shockingly spoken by multiple audiences last year)?

Vivienne L'Ecuyer Ming