Belief in AI extends beyond ethical concerns

The economic potential of artificial intelligence (AI) is vast, but many organizations are failing to realize it, with a staggering 87% of AI projects ending in failure. The root cause of this failure is not a technology problem, a business problem, or a culture problem, but rather a trust problem.

Recent research has shown that trust in AI is crucial for driving revenue, competitiveness, and customer success. However, building trust in AI systems has proven to be a complex challenge, with many recommendations being criticized as too abstract or impractical.

To address this issue, a new “AI Trust Equation” has been proposed, focusing on practical application. The equation, Trust = Security + Ethics + Accuracy, divided by Control, aims to provide a framework for building trust between humans and machines.

Security, ethics, accuracy, and control are identified as key components of trust in AI systems. Security ensures the protection of data shared with AI systems, while ethics addresses moral considerations in the development and use of AI. Accuracy measures the reliability of AI systems in providing accurate answers, and control determines the level of autonomy and oversight in AI decision-making.

By following the five steps outlined in the AI Trust Equation, organizations can evaluate AI systems based on their usefulness, security, ethical standards, accuracy targets, and control requirements. Building trust in AI systems is not only essential for economic success but also for shaping the future of technology and its impact on society.

As the potential of AI continues to grow, establishing trust between humans and machines will be crucial for unlocking its full benefits. The future of AI hinges on the ability to build and maintain trust within organizations and society.