Engadget AI
Stripe is reportedly in talks to buy PayPal — open the original publisher
Stripe and private equity firm Advent previously offered a deal PayPal didn't take, the Wall Street Journal says. Now, they're negotiating a new price.
Living report · refreshed from daily source data
Algorithmic bias report: evidence, research and policy
A living report on algorithmic bias and AI fairness, with daily charts, evidence records and downloadable data. Coverage counts are signals of attention—not measures of importance, harm or consensus.
Daily source coverage90 days
926records in archive
288latest 30 days
+95%versus prior 30 days
By record type
Leading sources in this record
What this report tracks
Tracks documented discrimination, fairness research, audits, litigation and policy responses. Automatic classification cannot establish that a system is biased; the linked evidence must be read in context.
Questions to take into the evidence
- Where are discriminatory outcomes being documented?
- Which audit and fairness methods are being tested?
- How are courts and regulators assigning responsibility?
Latest evidence
Full topic record →TechCrunch
Does Mark Zuckerberg really believe AI is ‘for everyone’? — open the original publisher
Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]
Bloomberg Technology
Jane Street Lost $15 Billion in Its First Down Month in a Decade — open the original publisher
Jane Street posted $15 billion of losses last month as AI-focused hedge fund Situational Awareness swooned and dragged down asset prices across equity markets, according to a person familiar with the ...
TechCrunch
Meta’s ‘open’ AI, and a $250M deal gone very wrong — open the original publisher
Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]
arXiv cs.CY
From Fair Representation to Just Recognition in Generative AI — open the original publisher
arXiv:2608.12669v1 Announce Type: new Abstract: The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally e
arXiv cs.CY
Applied and Filtered: An End-to-End Algorithmic Fairness Audit of A Public Employment Agency — open the original publisher
arXiv:2608.13022v1 Announce Type: new Abstract: Algorithmic fairness evaluation commonly assesses AI systems as bounded technical components, abstracting away the organizational context in which they operate. We present, to our knowledge, the first independent end-to-end fairness audit of a semi-automated hiring system operated by Barcelona Activa, a public employment agency using the third-party TalentClue platform for candidate search and shortlisting. We analyze approximately 497,000 candidat
arXiv cs.CY
Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements — open the original publisher
arXiv:2608.13444v1 Announce Type: new Abstract: Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being inva
arXiv cs.CY
The Illusion of Improvement: Reject Inference Strategies in Credit Scoring — open the original publisher
arXiv:2606.18479v2 Announce Type: replace-cross Abstract: Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematically evaluate several such methods and uncover a structural failure mode: in a natural retraining cycle, models whose accuracy improves while recall collapses create an illusion of improvement that leads practitioners to believe the system is getting better when, in fact, its rejection quali
Method and limits
This report is assembled automatically from source metadata and keyword classifications. It summarizes what the tracked source fleet published; it does not independently validate every linked claim. Source-fleet growth can inflate historical comparisons. Cite the individual evidence record and original publisher for substantive claims.