Intellectual hub of the topic
The mathematical precision of automated systems often creates an illusion of objectivity, yet human biases are frequently embedded within the software code. In this collection, we have gathered research and articles dedicated to the phenomenon of algorithmic bias. We analyze how data errors and specific software configurations influence decision-making in the judiciary, recruitment, medicine, and the distribution of social benefits.
We break down how federated learning deals with 'corrupted' data – and why spectral analysis turned out to be an unexpectedly elegant solution.
The Sarvam AI team conducted a large-scale study on the quality of speech recognition systems for Indian languages, highlighting the challenges they uncovered.
Researchers tested how resilient visual language models are to misleading geographical cues – and the results were quite telling.
Lab
Computer Science
How feminist principles and collaborative workshops are changing the approach to data annotation – and why it matters for fair AI.
NeuroBlog
Artificial intelligence • Educational Technologies
AI text detectors promise to unmask machines, but they increasingly accuse real people. We're exploring why these tools mislead and what can be done about it.
Lab
Finance & Economics
Research shows: language models form economic expectations just as irrationally as humans do, prioritizing personal experience over official data.
Let's dissect why neural networks occasionally act like they've inherited humanity's worst biases. We're talking about where this algorithmic nonsense comes from and whether we can actually teach machines to be more fair than their flawed creators.
Lab
Finance & Economics
New analysis methods show that many conclusions about discrimination in hiring turn out to be an illusion until we begin to properly account for uncertainty.
NeuroBlog
Artificial intelligence • AI Ethics
Алгоритмы, как древние боги, судят нас по своим законам – но кто написал эти законы и чьи ценности они отражают?
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