Hannah Overbye-Thompson
@overbye.bsky.social
PhD candidate @ UC Santa Barbara Comm | I study how people detect, perceive & respond to AI/algorithmic bias
https://www.hannahoverbye.com/
https://www.hannahoverbye.com/
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.
August 27, 2025 at 3:14 PM
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.
👩🦱 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.
August 27, 2025 at 3:14 PM
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.
👩🦱 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)
⚠️ Risks clustered around mental health, privacy, fairness, and polarization.
💡 Efficacy beliefs were split into: Powerlessness, Strategic consumption (user tactics) & Collective responsibility (policy, regulation, audits)
August 27, 2025 at 3:14 PM
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)
⚠️ 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.
📊 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.
August 27, 2025 at 3:14 PM
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.
📊 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)
- Organizational algorithms (e.g., hiring, healthcare, policing)
- Individual-use algorithms (e.g., search engines, facial filters)
August 27, 2025 at 3:14 PM
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)
- Organizational algorithms (e.g., hiring, healthcare, policing)
- Individual-use algorithms (e.g., search engines, facial filters)
APA PsycNet
doi.org
August 21, 2025 at 1:40 AM
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
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
The Oracle of Bacon
Kevin Bacon numbers, link any actor to any other, the Center of the Hollywood Universe, and more
oracleofbacon.org
August 4, 2025 at 5:33 PM
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
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/
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/
Beer Viz | Discover beers, & say cheers!
seekshreyas.github.io
August 4, 2025 at 5:33 PM
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/
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/
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...
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...
How trees secretly talk to each other - BBC World Service
YouTube video by BBC World Service
www.youtube.com
August 4, 2025 at 5:33 PM
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...
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...