AI Governance Maturity Assessment Tool
Artificial intelligence (AI) is being rapidly adopted by enterprises across industries. However, implementing AI comes with significant risks and challenges around ethics, bias, transparency, and accountability. That is why organisations need a thoughtful approach to AI governance - the processes, policies, and structures implemented to ensure AI is developed and used responsibly.
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AI governance maturity can be mapped into three levels: Seeker, Steward, and Scaler. Understanding where your organisation falls on this spectrum can help create a roadmap to advance your AI governance capabilities.
Organisations at the Seeker level have limited or no formal AI governance in place. AI projects are ad hoc and experimental in nature rather than aligned to strategic goals. There are no guidelines on how to develop, test or deploy AI responsibly.
Seekers launch AI pilots to explore potential use cases but they happen in silos without coordination. Data scientists have free rein to experiment with minimal oversight. AI ethics is an afterthought and bias mitigation is not baked into the process.
With AI still in proof-of-concept mode, Seekers can get away with a lax governance approach in the short-term. But the lack of guardrails means they are likely to encounter issues as projects scale. Hurriedly retrofitting governance at that point causes delays and cost overruns.
Steward organisations understand the importance of AI governance and are working to build their capabilities. They have basic policies, processes and structures in place to align AI projects with business goals and ensure they create value.
Stewards establish cross-functional AI oversight teams comprising stakeholders from legal, compliance, IT, security, risk management, and the business. AI projects are reviewed to assess benefits and risks before being approved. Data collection, model development and deployment undergo audits for quality, bias, and ethical risks.
While not yet robust, Stewards' governance practices promote responsibility across the AI lifecycle. Project teams adhere to model cards, ethics checklists and documentation standards that increase transparency. Key metrics are defined to track AI risks and returns.
By instilling accountability early, Stewards lay the foundation for AI projects that are not only performant but aligned with organisational values. But reliance on manual governance processes limits their ability to scale AI across the enterprise.
Scaler organisations have mature AI governance hardwired into their structure and culture. They take a holistic approach spanning policy, process, technology and people. AI strategy is integral to corporate strategy.
Scalers establish centers of excellence to provide AI development teams with tools, best practices, training and oversight. Automated pipelines, model inventories and testing frameworks provide technological guardrails without impeding innovation.
Comprehensive policies cover data management, model risk, target setting, human oversight, ethics reviews and mitigation of bias and other harms. Risk assessment is standardised using frameworks like Model Cards for Model Reporting. Portfolio management provides visibility into all AI projects in the pipeline.
With AI governance woven into the fabric of the organisation, Scalers have the foundations to deploy AI responsibly at scale. Anticipating risks keeps them from "governance debt" which could necessitate costly fixes later. Independent audits provide assurance to both internal and external stakeholders.
Advancing Your AI Governance
Assessing where your organisation falls on the AI governance maturity spectrum can spotlight gaps as well as strengths to build upon. Seekers have the opportunity to lay the right foundations instead of having to retrofit governance. Stewards can level up their policies, processes and tools to scale responsibly.
And even Scalers have room for improvement - governance must evolve continually as technology advances. Prioritising the well-being of people and society should be the true mark of AI maturity.
Here are some specific steps that organisations can take to improve their AI maturity:
Develop a clear AI strategy. This strategy should define the organization's goals for AI, the resources that will be dedicated to AI initiatives, and the governance framework that will be used to manage AI projects.
Establish a mature AI governance framework. This framework should address issues such as the ethical use of AI, the security of AI systems, and the compliance of AI systems with regulations.
Scale AI initiatives across the organization. This means embedding AI into the organization's core processes and decision-making systems.
Measure the impact of AI initiatives. This will help organisations to understand the value that AI is delivering and to identify areas for improvement.
Tools For Advancing AI Governance
Here are some tools that we at aiethicsassessor.com have gathered for you to advance your AI Governance maturity.
We have created a simple maturity calculator to aid in understanding your current AI Governance maturity.
If you wish to learn about software tools available to streamline your AI Governance maturity, check out our blog post on the list of AI Governance software.
Try out our AI Verify playground to get hands on experience on tooling for AI Governance.
And finally if you wish assistance from experts in improving your AI Governance Maturity, feel free to contact us using our contact form.