What companies actually adopt internally.
21 resources
Google's AI Principles outline the company's commitment to developing AI responsibly. The principles cover being socially beneficial, avoiding unfair bias, safety, accountability, privacy, scientific excellence, and appropriate use cases. Google also lists AI applications they will not pursue.
IBM's Principles for Trust and Transparency establish the company's approach to AI ethics. Key principles include that AI should augment human intelligence, data and insights belong to their creator, and AI systems should be transparent and explainable.
Anthropic's Responsible Scaling Policy defines AI Safety Levels (ASL) based on model capabilities and potential risks. It establishes commitments for capability evaluations, security measures, and deployment decisions as AI systems become more capable.
OpenAI's Usage Policies define prohibited uses of their AI systems, including illegal activities, harmful content generation, and attempts to circumvent safety measures. The policies establish requirements for developers building on OpenAI's platforms.
A sample policy template for organizations to govern the use of third-party generative AI tools like ChatGPT, Google Bard, DALL-E, and Midjourney in the workplace. The policy provides guidance on acceptable use practices and establishes governance frameworks for employees using publicly available AI applications that generate content or perform tasks.
A free downloadable template for organizations to create their own AI acceptable use policy. The template provides a framework for establishing guidelines around appropriate AI usage within companies.
A comprehensive template for organizations to create their own AI usage policies. The template covers governance structures, ethical guidelines including fairness and transparency, and practical usage guidelines for handling data and sensitive information.
A practical guide outlining ten essential steps for organizations to develop comprehensive AI policies. The resource focuses on defining stakeholder roles and responsibilities, establishing governance mechanisms, and ensuring ethical decision-making throughout AI development and deployment processes.
A comprehensive guide explaining AI governance principles and implementation strategies for organizations. Focuses on security, privacy, and risk management approaches that chief officers and boards must develop to protect data and prevent cybersecurity threats from AI systems.
This resource provides guidance on determining when AI applications require formal governance policies versus when they may not. It focuses on helping organizations identify and mitigate ethical concerns through responsible AI use standards, bias prevention, and transparency in decision-making processes.
Microsoft's responsible AI framework outlining ethical policies and practices for AI development and deployment. The resource covers key ethical considerations including bias and fairness, privacy and security, transparency and accountability, inclusiveness, and reliability and safety for generative AI tools.
This Gartner report examines the evolution of AI ethics, governance and compliance frameworks. It emphasizes the need for adaptive ethics approaches that build trust through transparent AI decision making, particularly in highly regulated industries.
This research article examines Unilever's approach to implementing AI ethics, outlining a five-stage process from evangelism through policy development to systematic review and action. The authors identify key stages including evangelism, policy development, data recording, systematic review, and decision-making actions for AI use cases.
Columbia University's institutional policy governing the use of generative AI tools by faculty, staff, students, and researchers in their academic and professional functions. The policy establishes guidelines for responsible use of generative AI technologies within the university community.
Google's policy outlining prohibited uses of generative AI services, including restrictions on misrepresenting AI-generated content as human-created with intent to deceive. The policy includes exceptions for educational, documentary, scientific, or artistic purposes where benefits outweigh potential harms.
A policy guidance document designed to help local governments develop frameworks for the use of generative AI within municipal operations. The document addresses data management requirements, public records compliance, and governance structures for AI implementation at the local government level.
This resource provides guidance on how artificial intelligence can transform vendor management processes in procurement operations. It focuses on leveraging AI for improved efficiency, risk mitigation, and building strategic supplier relationships through data-driven decision making.
This resource provides guidance on how organizations can hold their vendors accountable for AI use through specific questions and requirements. It emphasizes the importance of understanding vendor AI practices, data flow diagrams, and ensuring vendor compliance with organizational AI policies.
A practical guide for organizations on developing AI policies, covering the establishment of AI governance committees and vendor management programs. The resource provides guidance on structuring governance based on organizational size and complexity, with considerations for involving key departments like IT, security, compliance, and legal teams.
A collection of 16 AI governance policy examples designed to help government teams develop effective AI strategies and policies. The resource includes communication frameworks for AI developments and features examples like the City of San Jose's Generative AI Guidelines to demonstrate practical implementation approaches.
A comprehensive policy template launched by the Responsible AI Institute to help organizations build foundational responsible AI policies and governance structures. The template covers common policy and governance functions including data management, risk management, and procurement, and enables enterprises to integrate AI-specific guidance from the NIST AI Risk Management Framework.