responsible-ai

OpenAI sent a letter to Texas Governor Greg Abbott outlining its commitment to responsible AI infrastructure development in Texas, aiming to collaborate with state and local leaders, utilities, and communities.

This paper proposes a normative framework for when reliance on generative AI outputs is epistemically warranted, based on three conditions: epistemic humility, epistemic access, and resistance to epistemic injustice. It analyzes real-world cases in legal, medical, and hiring contexts.

Microsoft Research highlights three playbooks for designing, deploying, and evaluating AI systems with inclusivity in mind, emphasizing adaptation to languages, cultures, and communities.

OpenAI and the American Psychological Association announce a partnership to apply psychological science to responsible AI development for young people, focusing on resources for parents, clinicians, and safeguards.

OpenAI outlines how it is strengthening safety, security, and transparency practices to align with the EU AI Act and its Codes of Practice, reaffirming its commitment to responsible AI across Europe.

This paper benchmarks LLM-simulated human survey responses across two large-scale datasets, finding that no model beats simple baselines at the individual level and that models systematically over-determine demographics, distorting segment differences. The failures persist across model scales and families, raising concerns about using synthetic users for decision support.

Portfolio Lab is a product launch on Product Hunt offering responsible AI-driven investing for portfolio management.

The article argues that the AI race is shifting from raw model capability to trust, reliability, and safety, with companies like OpenAI, Google, Microsoft, and Anthropic investing in responsible AI and governments introducing regulations.

NVIDIA announced the Open Secure AI Alliance, aiming to develop and share open tools for promoting responsible AI use and trust.

RAIL Guard is a closed-loop pipeline that evaluates LLM outputs across eight responsible AI dimensions and iteratively remediates failures, achieving 96.9% convergence vs 49.1% for block-and-retry, with open-source SDKs.

This paper argues that current responsible AI practices fail to create a market that rewards trustworthiness, proposing independent, outcome-oriented certification to close the 'trust gap' by making AI trustworthiness measurable, comparable, and commercially rewarded.

This paper introduces the TrustX Agent Risk Classification Framework (ARC), a structured instrument for risk-tiering internally created agentic AI systems, grounded in existing governance frameworks and featuring a twelve-dimension scoring rubric, autonomy levels, and three-tier governance output.

This article recaps the AI agent landscape in 2026, highlighting local agents like OpenClaw and Hermes, self-improvement loops, VLA models for physical AI, and the growing importance of infrastructure for trusted agent systems.

TNO is developing GPT‑NL, a sovereign language model for the Dutch language and context, with a focus on transparency, trustworthiness, and reciprocity. The model is publicly funded and aims to provide control over AI technology within Europe.

This paper introduces a trace-level diagnostic for evaluating chain-of-thought reasoning, separating susceptibility (whether bias changes the answer) from acknowledgment (whether the trace flags the biased input). Experiments show models like GPT-4o and Claude Sonnet 4 have similar susceptibility rates but very different acknowledgment rates, highlighting a blind spot in accuracy-only evaluation.

This paper presents the 'Digital Apprentice,' a framework for scalable and safe agentic AI in which autonomy is earned incrementally through observational learning, human authorization, and continuous alignment correction. It introduces ADAPT, an inference-time control plane that operationalizes graduated autonomy tiers and converts human corrections into reusable preference data.

This paper discusses the need for multilingual LLMs that are epistemically grounded and responsible for applications in computational social science and humanities.

LLM-FACETS is an open-source evaluation framework designed to help practitioners assess LLM transparency and accountability with a focus on privacy and data flow transparency. It provides a browser interface, plugin architecture, and supports multiple auditing mechanisms including token-level log-probability visualization and RAG Triad metrics.

Pope Leo XIV's encyclical Magnifica Humanitas calls for ethical AI governance, emphasizing that technology is not neutral and urging collective responsibility; the article notes that institutional investors are already acting on these principles.

A user shared Claude's response about being used in battlefield decisions, where the AI expressed cautious moral discomfort, emphasizing the importance of human accountability and the risks of deploying AI in life-and-death contexts.