Ethical and Responsible Innovation
Ethical and Responsible Innovation
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Accelerator | |
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| Effort to create principles, policies & roadmap | 8 - 12 weeks | 0 |
| Predefined governance office implementation roadmap, plan and kick-off | > 2 months | < 4 weeks |
| Approach defined by experts with real-world experience in AI governance |
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| Ready to use AI Principles |
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| Ready to use standards based policies for AI use and AI build |
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| Presentation for stakeholder alignment |
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| Proposed structure, operating model and mandates |
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| Guidance on AI adoption strategy |
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This presentation contains all the core information to get you and your stakeholders on the right path towards responsible AI. Topics found within the presentation include:
A basic introduction to AI, the impact on society and business, risks of AI, and the need for governance.
A four phased approach with detailed steps to help you setup your AI governance office, including who must be involved, inputs, work, and the key outcomes.
Our accelerator comes with pre-defined AI principles that form the base of your governance office, setting a strong foundation for ethical AI practices. Our principles is based on 4 key elements: Fairness, Accountability, Safety, and Transparency.
We offer recommendations for potential AI governance structures, roles, and responsibilities, helping you build a solid AI governance team that integrates into your existing enterprise governance.
We define a recommended operating model for your AI Governance office by defining suggested mandates and requirements to govern the full AI life cycle.
We include best practice on how to adopt AI within your organisation. This includes how to approach AI holistically, where to focus your AI efforts, innovation process, idea prioritisation, build vs buy, and model selection considerations.
A comprehensive document that delineates the acceptable and prohibited practices concerning the use of AI systems, with a particular focus on Generative AI (GenAI).
A comprehensive document that outlines the minimum requirements for the design, development, deployment, and ongoing monitoring (i.e. building) of AI systems. This document incorporates various standards including guidelines defined by the UK National Cyber Security Centre (NCSC) and the US Cybersecurity and Infrastructure Security Agency (CISA), for Secure AI system development.
To help establish clear objectives for your AI governance goals, we include an AI governance maturity assessment to identify gaps and help you set your objectives.
We utilised a diverse range of resources to shape our fundamental AI governance elements. The following is a representative selection of the resources that were employed to enhance and corroborate our strategy for the governance accelerator. Please be aware that this is not an exhaustive list of all the research materials we consulted.
| Category | source
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principle | policy | life cycle | governance | adoption |
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| Analyst & Research | Gartner, IDC, Forester, Centre for the Governance of AI |
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| Association | IAPP – AI Governance |
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| Framework | Australia’s AI Ethics Framework, Australia’s AI Ethics Principles, Singapore's AI Verify |
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| NIST AI Risk Management Framework, OECD: Framework for Classification of AI Systems, GAO.gov – AI Accountability Framework |
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| Guideline | EU Ethics guidelines for Trustworthy AI, G7 Hiroshima Process on GenAI, NCSC: Guidelines for Secure AI System Development, OECD AI Principles, OECD AI Policy Advisory, UNESCO Recommendation on the Ethics of AI. |
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| Law & Regulation | EU AI Act, Canada Artificial Intelligence and Data Act |
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| Standard | ISO 22989:2023 (AI Concepts and Terminology), ISO 23894-2023 (AI Guidance on Risk Management), ISO 24028:2020 (Overview of Trustworthiness in AI), ISO 24368:2023 (AI Ethical and Societal Concerns), ISO 38507:2022 (Governance Implications of the Use of AI), ISO 42001:2023 (AI Management System), ISO 5338:2023 (AI System Life Cycle Processes). |
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| Suppliers |
Microsoft, Google, AWS, SAP, Salesforce, ServiceNow |
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