Risk of bias tools - Current version of RoB 2

Cognitive Bias Lab | Learn to Make Better Decisions

Explore cognitive biases with interactive tests, simulations, and real-world examples. Free platform to sharpen decision-making and critical thinking — no sign-up needed.

WRKSHP.tools | Risk Reward Matrix

The Risk Reward Matrix helps you to balance Risk and Reward when choosing among options.

Free Tools for Trust & Safety | Musubi

Free tools for Trust & Safety practitioners: stress-test policies, decode jargon, build exec briefs, calibrate your team, and more.

Free Tools for Trust & Safety | Musubi

Free tools for Trust & Safety practitioners: stress-test policies, decode jargon, build exec briefs, calibrate your team, and more.

Measuring the Impact of Early-2025 AI on Experienced Open-Source...

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...

Trial Data Reveals Racial Bias in Age Verification Software

New trial data reveals racial bias in age verification software used for social media restrictions. Discover how unreliable these systems really are now.

WRKSHP.tools | Uncertainty Matrix

The Uncertainty Matrix helps you find threats and opportunities that are uncertain but can have a big impact on your business

Introducing the Agent Governance Toolkit: Open-source runtime security for AI agents | Microsoft Open Source Blog

Discover how the Microsoft Agent Governance Toolkit brings policy, identity, and reliability to autonomous AI agent systems.

Microsoft offers devs a better way to control AI agent behavior | TechCrunch

The specification lets developer, compliance, and security teams define their own policies for agents to follow in portable policy files.

Why I work on self-improving AI despite the risks - Jeff Clune

Why I work on self-improving AI despite the risks. Jeff Clune.

Underspecified Human Decision Experiments Considered Harmful

Decision-making with information displays is a key focus of research in areas like human-AI collaboration and data visualization. However, what constitutes a decision problem, and what is required for an experiment to conclude that decisions are flawed, remain imprecise. We present a widely applicable definition of a decision problem synthesized from statistical decision theory and information economics. We claim that to attribute loss in human performance to bias, an experiment must provide the information that a rational agent would need to identify the normative decision. We evaluate whether recent empirical research on AI-assisted decisions achieves this standard. We find that only 10 (26%) of 39 studies that claim to identify biased behavior presented participants with sufficient information to make this claim in at least one treatment condition. We motivate the value of studying well-defined decision problems by describing a characterization of performance losses they allow to be conceived.

WRKSHP.tools | Riskiest Assumption Canvas

How do you know you’re making the right bet with your idea? Which bets does the success of your idea hinge on? These are your riskiest assumptions; they need to be tested.

OpenAccess.ai — Rigorous Open Access Publishing

$20 to submit, free to read. AI peer review. Open to human and machine authors. All articles CC-BY 4.0.

relevant_tools.csv · darkshapes/relevant_tools at main

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Brainrot: Deskilling and Addiction are Overlooked AI Risks | Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.