https://www.hannahoverbye.com/
A big thank you to everyone who has been extra kind during my job market year & Go Green! ππ€
The registration issue has been resolved β everything should now work smoothly.
π Direct link: www.icahdq.org/event/Hackat...
π Registration is open until April 5, 2026
Looking forward to seeing you at the ICA Hackathon 2026 @SU School for Data Science and Computational Thinking! ππ‘
The registration issue has been resolved β everything should now work smoothly.
π Direct link: www.icahdq.org/event/Hackat...
π Registration is open until April 5, 2026
Looking forward to seeing you at the ICA Hackathon 2026 @SU School for Data Science and Computational Thinking! ππ‘
A big thank you to everyone who has been extra kind during my job market year & Go Green! ππ€
A big thank you to everyone who has been extra kind during my job market year & Go Green! ππ€
doi.org/10.1080/0883...
doi.org/10.1080/0883...
Great work Sovannie πππ #commsky
Great work Sovannie πππ #commsky
π©β𦱠Women & people of color often described avoidant attitudesβseeing risks but feeling powerless. Which makes sense, as they are often the target of algorithmic bias
π¨ White men sometimes saw systemic risks but reported higher efficacy.
π©β𦱠Women & people of color often described avoidant attitudesβseeing risks but feeling powerless. Which makes sense, as they are often the target of algorithmic bias
π¨ White men sometimes saw systemic risks but reported higher efficacy.
β οΈ Risks clustered around mental health, privacy, fairness, and polarization.
π‘ Efficacy beliefs were split into: Powerlessness, Strategic consumption (user tactics) & Collective responsibility (policy, regulation, audits)
β οΈ Risks clustered around mental health, privacy, fairness, and polarization.
π‘ Efficacy beliefs were split into: Powerlessness, Strategic consumption (user tactics) & Collective responsibility (policy, regulation, audits)
π People saw organizational algorithms as riskier than personal ones.
π But they also felt less able to mitigate bias in those systems.
In other words, the higher the stakes, the less control people feel.
π People saw organizational algorithms as riskier than personal ones.
π But they also felt less able to mitigate bias in those systems.
In other words, the higher the stakes, the less control people feel.
- 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)
We ask: Do people see algorithmic bias as a riskβand do they feel capable of addressing it? Answer... It depends! More below ππ§ͺ #commsky
doi.org/10.1080/1044...
We ask: Do people see algorithmic bias as a riskβand do they feel capable of addressing it? Answer... It depends! More below ππ§ͺ #commsky
doi.org/10.1080/1044...
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