When it comes to ensuring ethical practices in artificial intelligence (AI) development and deployment, having a comprehensive understanding of the available tools and standards is crucial for organisations. By categorising these resources based on region and industry relevance, companies can effectively navigate the complex landscape of AI ethics.
In North America, for instance, organizations may leverage tools such as the AI Ethics Guidelines developed by the IEEE or the AI Principles outlined by the Partnership on AI. These resources provide a framework for ethical AI development and can assist companies in meeting the necessary criteria for each tier of ethical AI. Similarly, in Europe, the General Data Protection Regulation (GDPR) serves as a cornerstone for AI ethics compliance, while industry-specific standards like the European AI Alliance's Ethics Guidelines for Trustworthy AI offer additional guidance.
Below are some of the tools and standard Natural Velocity references when assessing AI products, services and service providers.
Tier Level | AI Tools | AI Standards and Regulations |
 | AI Fairness 360 (IBM): An open-source toolkit to help detect and mitigate bias in AI models. LIME (Local Interpretable Model-Agnostic Explanations): A tool for improving the transparency of machine learning models. | North America Algorithmic Accountability Act (USA): Proposed legislation aimed at requiring companies to assess the impact of their AI systems. Asia Singapore's Model AI Governance Framework: Provides guidelines for self-assessment of AI systems in terms of fairness, transparency, and accountability. |
 | DataRobot AI Catalog: Provides governance features to track AI models’ compliance with standards. ModelOp Center: Ensures AI models comply with regulatory and ethical standards across their lifecycle. AI Explainability 360 (IBM): Focuses on improving AI transparency and compliance with ethical guidelines. | Europe GDPR (General Data Protection Regulation):Enforces strict data privacy standards in AI. North America California Consumer Privacy Act (CCPA):Focuses on data privacy and the ethical use of consumer data in AI. Asia Japan’s AI R&D Guidelines: Provides guidelines for responsible AI development and deployment. |
| Ethical ML (Google Cloud): A toolset for monitoring and evaluating the ethical impact of AI models. Microsoft Fairlearn: Helps identify and mitigate biases in AI models and ensure ethical AI use. | Europe Ethical Guidelines for Trustworthy AI (EU): Developed by the European Commission to guide the development of trustworthy AI systems. North America IEEE 7000-2021: A standard for addressing ethical concerns during AI systems design. Asia China’s AI Ethics Guidelines: These guidelines provide a framework for the ethical development and deployment of AI technologies. |
 | IBM Watson OpenScale: Monitors AI models in real-time for fairness, explainability, and compliance with ethical standards. Google Fairness Indicators: Helps monitor and ensure fairness in large-scale machine learning models. Europe Algorithm Watch: A non-profit that helps in auditing AI systems for ethical compliance. | North America NIST AI Risk Management Framework (USA): Provides guidelines for managing AI risks to ensure trustworthy AI. Europe AI Act (EU): A proposed regulation aiming to ensure the safety and trustworthiness of AI systems across Europe. Asia Singapore’s AI Governance Testing Framework:Provides the highest level of AI governance and ethical compliance, focusing on transparency, fairness, and accountability. |
For industries like healthcare, where AI technologies are increasingly being deployed, organisations can benefit from tools such as the American Medical Association's AI policy recommendations or the Health Ethics and Policy Lab's Ethical AI Framework for Health. These resources address the unique ethical considerations within the healthcare sector and help companies align their AI practices with industry best practices. In the financial services sector, tools like the AI Principles for Financial Services developed by the World Economic Forum can aid organisations in developing responsible AI solutions that meet regulatory requirements and promote transparency.
By leveraging these region-specific and industry-relevant AI tools and standards, organisations can not only qualify for each tier of ethical AI but also demonstrate a commitment to responsible and ethical AI practices that prioritize transparency, fairness, and accountability.
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