Hannah Overbye-Thompson

New study (2025) examines how AI autonomy affects user agency and attitudes. Key finding: AI autonomy triggers psychological reactance through threats to freedom, BUT personalization benefits cancel this out + users with higher agency feel more threatened by autonomous AI 🧪

doi.org/10.1080/0883...

Racial Bias in AI Training Data: Do Laypersons Notice? Given that the nature of training data is the primary cause of algorithmic bias, do laypersons realize that systematic misrepresentation and under-representation of certain races in the training da...

doi.org/10.1080/1521...

New study (2025) examines if people can detect bias in AI training data. Key finding: Training data cues were largely ineffective; users relied on AI performance instead to judge bias + consistent with prior work on AI bias, the majority of participants failed to notice any bias in training data 🧪

New paper (2025) by @len-s.bsky.social proposes the PMSIS model: parents can use racially diverse entertainment media + "foreground co-viewing" + active mediation to improve children's intergroup socialization 🧪 doi.org/10.1093/annc...

Great work Sovannie 👏👏👏 #commsky

What Is an Algorithm? A Cross-Cultural Examination of Social Media Users’ Mental Associations on Algorithms Volume 6, Issue 3. DOI: 10.1037/tmb0000170

tmb.apaopen.org/pub/7w7ecobk

Fabulous work 👏👏👏

New study by @janadreston.bsky.social @anneo.bsky.social & @germanneubaum.bsky.social reveals how users understand algorithms. Key findings: 71% have a basic understanding of algorithms but only 33% can explain how they work; users see themselves as passive actors when interacting with algorithms 🧪

Personally, I had a lot of fun on this project. It was my first time leading a mixed-methods study and an all-student team. I hope this research is useful for informing design, policy, and education efforts that help people feel more empowered in the algorithmic age.

Demographics mattered too:

👩‍🦱 Women & people of color often described avoidant attitudes—seeing risks but feeling powerless. Which makes sense, as they are often the target of algorithmic bias

👨 White men sometimes saw systemic risks but reported higher efficacy.

Qual findings:

⚠️ Risks clustered around mental health, privacy, fairness, and polarization.

💡 Efficacy beliefs were split into: Powerlessness, Strategic consumption (user tactics) & Collective responsibility (policy, regulation, audits)

Quant findings:

📊 People saw organizational algorithms as riskier than personal ones.

📊 But they also felt less able to mitigate bias in those systems.

In other words, the higher the stakes, the less control people feel.

Drawing from the Risk Perception Attitude framework, we studied how people think about algorithmic bias in both:

- Organizational algorithms (e.g., hiring, healthcare, policing)
- Individual-use algorithms (e.g., search engines, facial filters)

Excited to share my new paper with @garciaerick.bsky.social Xinyi Zhang & @laurentwang.bsky.social.

We ask: Do people see algorithmic bias as a risk—and do they feel capable of addressing it? Answer... It depends! More below 👇🧪 #commsky

doi.org/10.1080/1044...

Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives Background There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race...

jme.bmj.com/content/51/6...

New study by Aquino et al. provides a fabulous look at differing opinions about algorithmic bias held by healthcare professionals. 72 experts had 3 key disagreements: whether bias exists (most say yes, some no), who's responsible for fixing it & whether to include race/ethnicity data in AI systems 🧪

doi.org/10.1037/xge0...

New study by @drjt.bsky.social examines if attention control explains the 🔗 between inspection time tasks and intelligence. Key finding: attention control fully mediated the inspection time-intelligence relationship + people with better sustained attention showed less performance decline over time 🧪

🎉 Huge congrats to our team @overbye.bsky.social, Kristy Hamilton and @jacobtfisher.online for receiving a Top Student Paper award in the Communication & Social Cognition Division at #NCA25! 🏆

4. The Oracle of Bacon 🥓🎬
A classic: plug in any actor and see how many steps it takes to reach Kevin Bacon (or any other actor).
Based on co-appearances in films. oracleofbacon.org

3. The Beer Graph 🍺
Curious how Lagers relate to Stouts?
This interactive network lets you explore how beers are connected by taste, aroma, and appearance.
Fun use of similarity graphs!
seekshreyas.github.io/beerviz/

YouTube video by BBC World Service How trees secretly talk to each other - BBC World Service

2. The Hidden Network of Trees 🌳
Trees communicate underground using fungal networks; sharing nutrients, warning of threats, and shaping forest life.
A lovely example of why we study two-mode networks

🍄📡 www.youtube.com/watch?v=DUqE...

In the Network of the Conclave - Bocconi University How network science can help us understand who will be the next Pope. The study by Soda, Iorio, and Rizzo reveals how status, information and alliances influence the papal election.

1. Who Will Be the Next Pope?
Network science can help us predict it.
The Network Conclave project mapped connections between cardinals; before Robert Prevost was selected, we could see he had the highest eigenvector centrality (i.e., he knew important others).
👑🌐 www.unibocconi.it/en/news/netw...

After a great summer teaching Social Networks at UCSB, I wanted to share 4 of my favorite network examples we explored in class 🧪🧵

1. Who Will Be the Next Pope?
2. The Hidden Network of Trees 🌳
3. The Beer Graph 🍺
4. The Oracle of Bacon 🥓🎬

Psychopathology and Gaming Disorder in Adolescents This cohort study uses data from the Adolescent Brain Cognitive Development Study to examine directional longitudinal associations between psychopathology and gaming disorder among adolescents.

jamanetwork.com/journals/jam...

New from @kyliewoodman.bsky.social "baseline levels of psychopathology were significantly associated with an increased risk of developing gaming disorder 1 year later. However, there was no significant association between gaming disorder and the development or worsening of psychopathology." 🧪

Amazing job @kyliewoodman.bsky.social. This is such important research.

Social (media) psychology of the “news-finds-me” perception: habits, mindsets, and beliefs Abstract. Unintended encounters with news on social media can foster a reliance on incidental news exposure and the perception that “news finds me.” Becaus

doi.org/10.1093/jcmc...

New study (Campbell & Hawkins, 2025) examines why people expect news to find them on social media (vs seeking it out). Key finding: when people habitually scroll social media w/out thinking + believe algorithms/lack of control drive SM usage, they're more likely to rely on incidental news exposure 🧪

Taken together, I hope these contributions make it easier to:
• Frame algorithmic bias more clearly
• Talk about how people respond to it in everyday life
• Study that behavior with more precision

Thanks for reading!

5/5 🧵

Finally, the paper offers an example for thinking about when and how to study workarounds to algorithmic bias, depending on whether algorithmic bias is real, perceived, or both, which I hope can serve as a starting point for examining bias in various sociotechnical contexts.

4/5 🧵

The paper proposes the concept of “workarounds” to describe how individuals respond to biased systems. Algorithmic bias workarounds are goal-driven attempts by users to overcome, bypass, or reduce perceived or existing algorithmic bias, which is something we lack consistent language for.

3/5 🧵