Title: Building Trust in AI: The New AI Trust Equation
Introduction:
The potential of AI is vast, but many organizations fail to realize its benefits, with a staggering 87% of AI projects falling short. The root cause of this failure, according to recent research, is a lack of trust in AI systems. Trust in AI has become a complex issue, akin to trust between humans. This article introduces a new “AI Trust Equation” that focuses on practical application, addressing key factors such as security, ethics, accuracy, and control.
The AI Trust Equation:
The traditional trust equation, Trust = Credibility + Reliability + Intimacy divided by Self-Orientation, is not directly applicable to building trust between humans and machines. Instead, a new AI Trust Equation is proposed: Trust = Security + Ethics + Accuracy divided by Control. This equation takes into account the unique considerations required for trusting AI systems.
Security:
Security forms the foundation of trust in AI systems. Organizations must ensure that their data remains secure when shared with AI systems. This requires evaluating the platform’s security measures and considering factors such as data protection and potential breaches.
Ethics:
Ethics in AI is a moral question rather than a technical one. Leaders must consider how people were treated in the making of AI models and whether they align with their organization’s values. Additionally, leaders should assess model explainability, potential biases, and the business model behind the AI system.
Accuracy:
Accurate answers are crucial for building trust in AI systems. Organizations should evaluate how reliably an AI system provides accurate answers based on the sophistication of the model and the quality of the training data. Accuracy directly affects the usefulness of the AI system in the context of the organization’s workflow.
Control:
Control is at the core of trusting AI systems. Organizations need to assess whether an AI system will do what they want it to do and whether they will retain control over intelligent systems. This ranges from tactical questions about system performance to broader concerns about losing control over AI decision-making.
Using the AI Trust Equation:
To effectively apply the AI Trust Equation, organizations should follow these steps:
1. Determine usefulness: Assess whether an AI platform adds value to the organization’s goals before investing resources in evaluating its trustworthiness.
2. Ensure security: Collaborate with security teams or hire advisors to verify the platform’s security measures and ensure data protection.
3. Set ethical thresholds: Define explicit definitions of explainability and other ethical principles that align with the organization’s values. Measure proposed AI systems against these limits.
4. Define accuracy targets: Establish accuracy targets that align with the organization’s workflow requirements. Avoid adopting systems with low accuracy, as this can lead to poor quality work output.
5. Determine control requirements: Decide the level of control desired over AI systems and explore whether existing systems meet those requirements or if a higher level of control is necessary.
Conclusion:
Building trust in AI systems is crucial for unlocking their immense potential. Organizations must embrace the new AI Trust Equation, tailored to their specific needs. By evaluating AI systems based on security, ethics, accuracy, and control, organizations can pave the way for a more trustworthy future of technology. Trust in AI is not just an economic necessity but also fundamental to shaping society’s relationship with technology.