There’s a reason why a baby car seat needs to receive a stamp of approval from the Consumer Product Safety Commission before it can take to the road. A baby is precious cargo and you cannot take the manufacturer’s word that the restraints will function as they should. Equally important, you know the Commission will apply the same set of rigorous standards to all car seats irrespective of the manufacturer. This rigorous process of testing, and the use of an external body to validate those tests, brings peace of mind when purchasing and using the product.
This peace of mind creates the trust that the product will perform as it should. This trust is brought about by the third party who tests the equipment—a third party who is independent and uses a rigorous set of standardized tests and scenarios across the board.
It is how you build trust that the end product will work as it was designed.
A similar logic applies to AI/ML models, especially because AI has a trust issue. Nearly half of enterprise leaders in a 2021 survey said they trusted AI-derived decisions only some of the time. Therefore, developing this trust is down to building a rigorous–and independent–approach to validation and testing of AI/ML models ahead of live deployment. This consistency of repeatable testing and validation, over time, will build trust and confidence in AI-led decisions.
Consider the ways we use and deploy AI/ML models today. If you work to deploy third-party models and algorithms, how can you ensure that the model works for your use cases and understands your ecosystem? How do you know if the model you are buying is not just another off-the-shelf construct not quite suited to your needs?
Vendor assurance is not enough, especially when so much is on the line. You can be stuck with a black box, without any knowledge of the dataset’s origin or its potential for bias. Such opacity can be problematic when it is your organization’s compliance on the line. While there is a growing effort for more transparency from vendors, we still need a trust-but-verify approach to ensure that all information about model development is available and that your AI/ML models can be safely and effectively deployed.
If you are building models in-house, the same argument applies. Enterprises might say they commit to testing models internally, but the process simply does not work consistently. For one thing, when AI talent is at such a premium, more enterprises want the focus on shiny development tasks instead of test-driving what they are creating. Besides, having internal teams test their own models is like having a student grade their own test. An A+ grade is often the outcome, whether it is earned or not. Testing standards become inconsistent and conditions are not always reproducible. In-house AI/ML models too need an independent third party to test before deployment. Only then can real trust be formed.
There’s a lot at stake. When an ML model is used to detect fighter ships at sea or deliver on-ground intelligence, it cannot afford mistakes. Models need to pass testing by an independent third-party agent who understands the parameters involved. Such an entity will test for accuracy, reliability, robustness, and resilience of AI/ML models, all the parameters laid out in the AI Risk Management Framework set out by the National Institute of Standards and Technology (NIST).
When you make independent AI/ML model testing before deployment a habit, you can rest assured that your model is ready to hit the road without encountering speed bumps along the way. You build that most valuable of currencies: trust.
About CalypsoAI
CalypsoAI’s mission is to build trust in AI through independent testing and validation. We solve one of the biggest issues facing AI: machine learning models not getting deployed into production. Through CalypsoAI’s automated testing and validation solution, decision-makers gain the performance visibility needed to confidently deploy their models into production. This ensures the success of AI strategy while drastically reducing the amount of risk, time, and money spent to manually test and validate models. CalypsoAI was founded in Silicon Valley by DARPA, NASA, and DoD veterans.
