Warden AI provides independent, tech-led AI bias auditing, designed for both HR Tech platforms and enterprises deploying AI solutions in HR. As the adoption of AI in recruitment and HR processes grows, concerns around fairness have intensified. With the advent of regulations such as NYC Local Law 144 and the EU AI Act, organisations are under increasing pressure to demonstrate compliance and fairness.

This document lists examples of and defines categories of use cases for machine learning in medicine for clinical practice.

The impact of artificial intelligence or autonomous and intelligent systems (A/IS) on humans is measured by this standard.

This document provides a high-level overview of AI ethical and societal concerns.

This standard identifies the core requirements and baseline for AI solutions in health care to be deemed as trustworthy.

A toolkit for technology-makers interested in improving their technologies by applying wellbeing psychology to design.

Diagnose bias in LLMs (Large Language Models) from various points of views, allowing users to choose the most appropriate LLM.

Approaches to disability-centered data, models, systems oversight

Ethical Problem Solving (EPS) is a framework to promote the development of safe and ethical artificial intelligence. EPS is divided into an evaluation stage (performed via Algorithmic Impact Assessment tools) and a recommendation stage (the WHY-SHOULD-HOW method).

This document provides risk-management practices or controls for identifying, analyzing, and mitigating risks of large language models or other general-purpose AI systems (GPAIS) and foundation models. This document facilitates conformity with or use of leading AI risk management-related standards, adapting and building on the generic voluntary guidance in the NIST AI Risk Management Framework and ISO/IEC 23894, with a focus on the unique issues faced by developers of GPAIS.

Our work enables developers and policymakers to anticipate, measure, and address discrimination as language model capabilities and applications continue to expand.

In undertaking this voluntary commitment, developers and managers of advanced generative systems commit to working to achieve outcomes related to the OECD AI principles.

The Playbook provides suggested actions for achieving the outcomes laid out in the AI Risk Management Framework (AI RMF) Core (Tables 1 – 4 in AI RMF 1.0). Suggestions are aligned to each sub-category within the four AI RMF functions (Govern, Map, Measure, Manage).

The AIRC supports all AI actors in the development and deployment of trustworthy and responsible AI technologies. AIRC supports and operationalizes the NIST AI Risk Management Framework (AI RMF 1.0) and accompanying Playbook and will grow with enhancements to enable an interactive, role-based experience providing access to a wide-range of relevant AI resources.

The goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems.

This report proposes a model for Equality Impact Assessment of AI tools. This builds on the paper research which finds that existing fairness and bias auditing solutions are inadequate to ensuring compliance with UK Equalities legislation.

A toolkit for employers and workers seeking to understand the challenges and opportunities of using algorithmic systems that make or inform decisions about workers.

Guidance aimed at encouraging employee led design and development of algorithmic systems in the workplace.

Provides the regulatory framework for incorporating rights, freedoms, and obligations relevant to work and people's experience of it, in particular technology specific guidance.

FAIRLY provides an AI governance platform focussed on accelerating the broad use of fair and responsible AI by helping organisations bring safer AI models to market.