Building Trust in AI Products

As artificial intelligence continues to reshape industries, brands face the challenge of instilling trust in their AI products and services.

Consumers are seeking reassurance in the AI they engage with. They want transparency on why and how it’s used.

This trust-building process is crucial, and brands must seek to align their AI integration with their customer’s values and expectations.

1. Explainability = Trust

Transparency is key when it comes to AI.

Users should have a clear understanding of how the technology works, what data it used, and how decisions are made. 

PathAI, a healthcare technology company that leverages AI to improve the accuracy and efficiency of diagnosing diseases, places a strong emphasis on explainability.

By providing detailed visualisations and insights that help clinicians understand how the AI arrived at its conclusions, it has become a trusted tool for clinicians to validate and refine diagnoses.

2. Design for ease of use

A positive user experience is crucial for establishing trust in AI functions.

Brands must prioritise designing AI interfaces that are user-friendly, intuitive, and provide clear feedback.

Google’s Smart Compose feature in Gmail is an excellent example of an AI-powered function that enhances the user experience. It uses AI to suggest complete sentences while composing emails.

It provides real-time suggestions that users can accept, enhancing productivity and creating a sense of reliability and trust in the AI function.

3. Show how you are protecting user data.

Ensuring the security of AI functions is essential for building trust in your product or service.

Brands must prioritise data protection and embed robust security measures into the user experience.

Payment processing platform Stripe leverages AI to enhance fraud detection and prevention.

They place a strong emphasis on privacy and data protection by implementing robust security measures and maintaining a transparent privacy policy.

4. Demonstrate testing for bias

Trust in AI functions is strengthened when brands visibly test and validate their technology.

This process includes comprehensive testing for bias, accuracy, and other potential issues.

For example, Google has developed specific tools and frameworks, such as the Google Cloud Fairness Indicators, to measure and detect potential biases in their models.

Fitbit have also implemented robust testing to ensure accuracy and reliability of their AI-powered fitness tracking algorithms. They employ a combination of lab testing and field studies to assess the accuracy of their devices’ measurements.