November 1, 2024
In a recent episode of The Chief AI Officer Podcast, Dr. Stephen Drew, the Chief Operating Officer (COO) of RNL, delved into the intricate relationship between high capability and explainability in neural networks. Neural networks, a type of machine learning model, have revolutionized various industries with their ability to process vast amounts of data and make accurate predictions. However, according to Drew, their power comes with a trade-off: they can be very hard to understand and see how they make their decisions.
Why Transparency Matters
Here are a few reasons:
- Building trust: When we understand how a model works, we’re more likely to trust its outputs and recommendations.
- Improving model quality: By peeking under the hood of a model, developers can spot biases, errors, and areas for improvement, leading to better performance over time.
- Taking ownership: With transparent models, organizations can take responsibility for their AI-powered decisions and be accountable for the consequences.
What’s Being Done to Address the Issue
To tackle this challenge, solution providers like RNL are adopting a collaborative approach. They’re working closely with clients to:
- Set realistic expectations: Educating clients on what AI can and can’t do, so they know what to expect.
- Develop more explainable models: Creating models that provide insight into their decision-making processes.
- Foster a culture of transparency: Prioritizing clear communication and accountability within the organization.
A Bright Future for AI
Industry leaders agree that addressing these uncertainties is crucial for successful AI adoption. By prioritizing transparency, we can unlock the full potential of neural networks while maintaining trust and accountability. As Dr. Drew emphasized, finding a balance between capability and explainability is key to successful AI implementation.
Listen to the full episode here.