How can we make AI trustworthy

Covid-19, and the rapid increase in remote working has accelerated the development and use of artificial intelligence (AI) across organizations and in consumer interactions.

This has highlighted both the benefits and the potential risks of AI — notably the issue of trust in technology. While trust has long been a defining factor in an organization’s success or failure, the risk of AI now goes beyond reputation and customer satisfaction’ — it is playing a critical role in shaping the well-being and future of individuals and communities around us — even as few fully understand how it works.

A new study by KPMG, The Shape of AI Governance to Come, finds that the majority of jurisdictions globally have yet to fully grasp the full implications of how AI will shape their economies and societies. Furthermore, the pace of AI innovation is happening so quickly that even the most technologically sophisticated governments are struggling to keep up.


87% IT decision makers believe that technologies powered by AI should be subject to regulation.

Of this group, 32% believe that regulation should come from a combination of both government and industry, 25% believe that regulation should be the responsibility of an independent industry consortium.

94% IT decision makers feel that firms need to focus more on corporate responsibility and ethics.

Current challenges to regulation:

1. The precision and accuracy of the technology.
2. Discrimination and bias in decisioning.
3. The appropriate use of consumer data and data privacy to inform AI.

Till there is clarity around regulation, businesses can:

1. Develop AI principles, policies and design criteria and establish controls in an environment that fosters innovation, flexibility, and trust while identifying the unique risks associated with AI.

2. Design, implement, and operationalize an end-to-end AI governance and operating model across the entire AI development life cycle, including strategy, building, training, evaluating deploying, operating and monitoring AI.

3. Assess the current governance and risk framework and perform a gap analysis to identify opportunities and areas that need to be addressed.

4. Design a cross-functional governance committee and framework that deliver AI solutions and innovation through guidelines, templates, tooling and accelerators to quickly yet responsibly deliver AI solutions.

5. Integrate a risk management framework to identify and prioritize business-critical algorithms and incorporate an agile risk mitigation strategy.

6. Design and set up criteria to maintain continuous control over algorithms without stifling innovation and flexibility.