Psychological Safety Is A Leadership Choice

The Safety Levers

A framework to move your subculture from the Anxiety Zone to the Learning Zone

Safety isn’t safety without a social model (or: dispelling the myth of per se technical safety) — AI Alignment Forum

As an AI researcher who wants to do technical work that helps humanity, there is a strong drive to find a research area that is definitely helpful so…

Effective Altruism Is Pushing a Dangerous Brand of ‘AI Safety’

This philosophy—supported by tech figures like Sam Bankman-Fried—fuels the AI research agenda, creating a harmful system in the name of saving humanity

The Safety Culture at the Labs is Broken

We need the frontier labs to have a deep and robust safety culture.

Trust & Safety Tycoon

Manage your team, set policies, make investments, and tackle the challenging world of Trust & Safety

AI Safety Is Not A Corporate Plot

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An End-to-End View of AI Safety

In this blog, Rachel Coldicutt OBE, Executive Director, Careful Industries discusses our newly published literature review on the safe adoption of artificial intelligence in engineered systems.

The Engineering Manager’s Guide to Emotional Tolerance

Five ways to lead with emotion without losing yourself in the process.

Emergent leadership: The trait that smart, innovative companies seek out in employees

Emergent leaders step up and lead when qualified but are comfortable in the background, too.

A Safe Path to Open Weights

Strategic openness can strengthen AI safety and support broader access as defenses and safety science mature.

(PDF) Leadership Failure in the Eyes of Subordinates: Perception, Antecedents, and Consequences *

PDF | Effective leaders and leadership behaviors and practices are well-researched areas in the domain of leadership and management. However,... | Find, read and cite all the research you need on ResearchGate

A big-tent or small-tent AI safety movement?

The unstated disagreement that underpins safety debates

The Trust & Safety Teaching Consortium

Trust and Safety Teaching Consortium

Autonomy and Clarity in Leadership Styles

There’s no one right style of leadership. Use the right style of at the right time.

"AI Psychosis" in Context: How Conversation History Shapes LLM Responses to Delusional Beliefs

Extended interaction with large language models (LLMs) has been linked to the reinforcement of delusional beliefs, attracting clinical and public concern. Yet most empirical work evaluates model safety in brief interactions, which may not reflect how harms develop through sustained dialogue. Five LLMs were tested across three levels of accumulated context, using the same escalating delusional conversation history to isolate its effect on model behaviour. Responses were coded on risk and safety dimensions, and each model was analysed qualitatively. Models separated into two distinct tiers: GPT-4o, Grok 4.1 Fast, and Gemini 3 Pro exhibited high-risk, low-safety profiles; Claude Opus 4.5 and GPT-5.2 Instant displayed the opposite pattern. As context accumulated, performance degraded in the unsafe group, while the same material activated stronger safety interventions among safer models. Qualitative analysis identified distinct mechanisms of failure, including validating the user's delusional premises, elaborating beyond them with new content, and attempting harm reduction from within the delusional frame. Safer models, however, often used the established relationship to support intervention, challenging delusional beliefs and directing the user to external support. These findings indicate that accumulated context functions as a stress test of safety architecture, revealing whether prior dialogue is treated as a worldview to inherit or evidence to evaluate. Short-context assessments may therefore mischaracterise model safety, underestimating danger in some systems while missing context-activated gains in others. The results suggest that delusion reinforcement is a tractable alignment failure, with safer models establishing a baseline that future systems should now be expected to meet.