Building Trustworthy AI with Trust as a Service

Gartner predicts - By 2026, Organizations that will incorporate AI transparency, trust, and security will see a 50% efficiency increase in their AI Model in terms of adoption, business goals, and user acceptance.

The upcoming regulations globally and in the US are placing a high emphasis on Trustworthy, Responsible AI.

Yet, the challenge around what it is and how to design, build and create Trustworthy AI/Responsible AI is not well understood.

The advancement of AI technologies offers unparalleled opportunities for innovation and growth but also presents new challenges in governance and ethical considerations. To address this, "Trusted AI as a Service" merges the proven practices of traditional governance with the flexibility required for effective AI management.

Trusted AI as a Service is a?risk-based?approach?for Implementing Trustworthy AI responsibly.

Our approach ensures that organizations can harness the power of AI in a manner that is both ethical and sustainable, thereby maximizing potential benefits while mitigating associated risks.

Central to our service offering is the AI TIPS methodology (Artificial Intelligence Trust Integrated Pillars for Sustainability), developed from our extensive experience in strategic risk management for global corporations. AI TIPS is a comprehensive, lifecycle-oriented framework that provides actionable, operational governance for trustworthy AI implementations. This methodology emphasizes the importance of involving key stakeholders early and throughout the AI project lifecycle, aligning closely with the organization's strategic objectives and ethical standards.

Complementing our AI TIPS methodology, the "Trusted AI as a Service" offering is bolstered by our AI Center of Excellence (CoE). This CoE is a hub of on-demand AI expertise, offering access to leading AI specialists, best practices such as NIST AI RMF, ISO 42001, IEEE Ethics by design, and the latest strategies in AI governance. Through this innovative Consulting as a Service model, clients receive customized, scalable solutions tailored to their unique challenges and needs, ensuring success in their AI initiatives.

?I have been focused on the need for Trustworthy AI - assessing the criticality, complexity and risk of AI since last five years. Did I have a crystal ball and foresee the future? No but, having defined risk based holistic Cybersecurity & Privacy programs at large global Fortune 500 companies and looking at emerging risks through the lens of business impact I knew these are high impact systems unprecedented in risk, scale and impact (positive and negative).

That lead to designing the framework, AI TIPS.

Trust Integrated Pillars for Sustainability brings a modular approach to building Trusted AI for Intended outcomes. For humanity, for the planet, for society, business and our future. What is the AI TIPS Framework?

It is a lifecycle approach for Implementing/developing Trustworthy AI responsibly.

It is built on 8 essential pillars of trustworthy AI. They are:

Security

Privacy

Transparency

Explainability

Accountability

Audit

Ethics

Regulations

I created these five years ago. It aligns with the National Institute of Standards and Technology (NIST) AI Risk Management Framework that was published last year. NIST AI RMF published seven characteristics of Trustworthy AI. The AI RMF defines trustworthy AI as being “valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.”

In addition to a lifecycle approach that integrates the pillars, AI TIPS links to Sustainability. A holistic approach that integrates into the build cycle with the essential pillars will help decrease compute requirements by ensuring we dont build and rebuild each time there is a risk uncovered such as bias.

In addition we can cut down on compute intense frameworks, for example for Explainability unless it provides a clear value for the system

Problems with AI Governance Tools

Some of the key findings of our report is that there are some problems emerging already. We found that among the 18 AI governance tools that we reviewed specifically that reflect our definition of AI governance tools, 38% of those included faulty AI fixes, specific types of things that we look at in the report that are problematic when it comes to measuring AI fairness and explainability. And I can go into detail on what those are. It's a technical thing. But basically, put simply, we already saw some problems because we saw governments and NGOs putting out AI governance tools that included some methods that were problematic, and that could create that false sense of confidence when it comes to what a measure does. If some measurement method is intended to help rate or score the level of risk or the level of fairness or whatever, and the measurement itself doesn't work right, it's not achieving the policy goals that are intended there.

Conclusion: We are approaching right sizing the AI Strategy and approach based on people, process and technology to streamline efforts, reduce resources and risk. Given AI's dynamic nature and explosive growth, we are excited about bringing a dynamic and adaptive solution with a community of providers that is suitable for a context.

Please reach out for more details on Trust as a Service at https://www.trustedai.ai/ai-trust-as-a-service/ or join me at a LinkedIn Live event on March 11th https://www.dhirubhai.net/events/trustedaiasaservice7171145555354509312/