Omri Ben-Dov
@omribendov.bsky.social
Reposted by Omri Ben-Dov
We (w/ Moritz Hardt, Olawale Salaudeen and
@joavanschoren.bsky.social) are organizing the Workshop on the Science of Benchmarking & Evaluating AI @euripsconf.bsky.social 2025 in Copenhagen!
📢 Call for Posters: rb.gy/kyid4f
📅 Deadline: Oct 10, 2025 (AoE)
🔗 More info: rebrand.ly/bg931sf
@joavanschoren.bsky.social) are organizing the Workshop on the Science of Benchmarking & Evaluating AI @euripsconf.bsky.social 2025 in Copenhagen!
📢 Call for Posters: rb.gy/kyid4f
📅 Deadline: Oct 10, 2025 (AoE)
🔗 More info: rebrand.ly/bg931sf
September 22, 2025 at 1:45 PM
We (w/ Moritz Hardt, Olawale Salaudeen and
@joavanschoren.bsky.social) are organizing the Workshop on the Science of Benchmarking & Evaluating AI @euripsconf.bsky.social 2025 in Copenhagen!
📢 Call for Posters: rb.gy/kyid4f
📅 Deadline: Oct 10, 2025 (AoE)
🔗 More info: rebrand.ly/bg931sf
@joavanschoren.bsky.social) are organizing the Workshop on the Science of Benchmarking & Evaluating AI @euripsconf.bsky.social 2025 in Copenhagen!
📢 Call for Posters: rb.gy/kyid4f
📅 Deadline: Oct 10, 2025 (AoE)
🔗 More info: rebrand.ly/bg931sf
In our new work we ask: Can end-users make a platform’s ML models fairer?
Firm-side fair learning often reduces accuracy, discouraging firms from using it. But if a platform relies on user data, can minority users collectively change the data to induce fairness?
(1/4)
Firm-side fair learning often reduces accuracy, discouraging firms from using it. But if a platform relies on user data, can minority users collectively change the data to induce fairness?
(1/4)
August 22, 2025 at 6:45 AM
In our new work we ask: Can end-users make a platform’s ML models fairer?
Firm-side fair learning often reduces accuracy, discouraging firms from using it. But if a platform relies on user data, can minority users collectively change the data to induce fairness?
(1/4)
Firm-side fair learning often reduces accuracy, discouraging firms from using it. But if a platform relies on user data, can minority users collectively change the data to induce fairness?
(1/4)