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4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src
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#decision-making

5 curated events
papersSEP 11 04:00 UTC

arXiv Paper Proposes Data-Driven Framework for Supply Chain Decision-Making

A new arXiv preprint describes an approach that combines statistical learning from data with robust decision-making for supply chain networks. The work targets structural uncertainty, market volatility, and operational disruptions that modern supply chains face, and aims to connect data-driven methods with practical decision support. It is an announcement of a cross-listed machine learning paper rather than a released product.

arxiv/supply-chain/research/decision-making
papersSEP 10 04:00 UTC

Physically Grounded Proactive Modeling for Retail Agents from Sparse Third-Person Video

Researchers present a study on proactive agents that must both select actions and decide whether available evidence justifies acting. Using sparse third-person video in retail service scenarios, the approach grounds decisions in human-object interactions so agents can anticipate customer needs before an explicit request is made.

agents/decision-making/video/embodied-ai
papersSEP 10 04:00 UTC

Study examines when decision-makers have incentives to offer algorithmic recourse

A research paper investigates whether organizations that rely on automated decision systems, such as banks and employers, have any motivation to tell rejected applicants how they could change an unfavorable outcome. The work analyzes the strategic incentives behind offering recourse, a mechanism meant to help people overturned by algorithmic decisions. Its findings are relevant to ongoing debates about transparency and fairness in automated decision-making.

fairness/explainability/accountability/decision-making
papersSEP 12 04:00 UTC

arXiv Paper Proposes Pragmatic Information Theory Linking Communication and Control

A new arXiv preprint outlines a framework called pragmatic information theory that aims to connect communication, control, and decision-making under one account. Its central concept, the isoteleia mapping, treats different semantic paths as equivalent when they lead to the same optimal action, a property the author describes as equifinality. The work is a cross-listed submission and is presented as a mathematical treatment rather than an applied system.

information-theory/research/arxiv/decision-making
papersSEP 10 04:00 UTC

New Framework Distinguishes Cognitive Amplification From Over-Dependence in Human-AI Systems

An arXiv paper in cs.AI introduces a metric framework for telling apart AI systems that genuinely boost human reasoning from those that encourage users to offload their thinking. The authors note that the line between cognitive amplification and cognitive delegation has been poorly defined, and they propose measurable criteria to close that gap. The work may help researchers and product teams evaluate how AI-assisted decision tools affect human cognition.

human-ai-interaction/metrics/cognition/decision-making