What are Test Harnesses?
Test Harnesses were created as a set of tools and conventions used for automated testing of software applications (https://bit.ly/3w7EX7D). It uses a combination of simulated stubs and drivers to assist with testing when we don’t have full visibility of the workflows or data that need to be tested. This enables comprehensive and quick testing of applications even when the environment/container isn’t ready/available.
Why do Talent AI Models need Test Harnesses?
As Talent AI models become increasingly complex (on account of “Black Box” frameworks like Large Language Models and Neural Networks) and data becomes increasingly difficult to access (on account of regulatory constraints), it is contingent on users of ML models to keep a close eye on:
1. How these models are performing.
2. Have clarity on what is expected from these models.
3. Most importantly, set performance thresholds beyond which clear corrective action needs to be triggered when these models are drifting/failing.
What are some examples of Talent AI models?
1) Attrition Prediction (eg: IBM Watson — https://cnb.cx/42shE4l)
2) Candidate Authentication (eg: Talview — https://bit.ly/42wwOph)
3) Online Course Completion (eg: NextThought — https://bit.ly/3OyhKBI)
4) LLM Enabled Onboarding Services (eg: VaultEdge — https://bit.ly/3usczwn)
A Test Harness for Talent AI is critical to continuously and comprehensively track the performance of these models. The key components required solutions are quite different from the original Test Harnesses since Machine Learning models are not heuristics-driven and data availability may also be limited.
The key components for a Talent AI Test Harness are:
1) Synthetic Data — A GenAI-created synthetic database that is anonymized, yet statistically representative of the organization. The actual values in this database will be compared with the predicted values generated by the model.
2) Measurement Framework — Key metrics for measuring the effectiveness of the model along with a functional context. Eg: The Specificity in a Classification engine that identifies a fake candidate in an interview will need to be explained differently than the Specificity in a Classification engine that identifies if a candidate is going to accept an offer or not.
3) Threshold Criteria — Acceptance criteria (contextual to the organization) that set the threshold for raising a red flag when a model does not meet the organization's requirements.
Who are the beneficiaries of a well-structured AI Test Harness?
1) AI Products / Service Providers
a. Objective / Reusable Measurement Frameworks — If the same Test Harness is going to be used across multiple AI Products, all these firms can expect a transparent and comprehensive comparison.
b. Industry Best Practices Alignment — By generating data/performance thresholds that are representative of the industry, these product owners can be assured that their models are aligned with industry expectations.
2) Enterprises consuming AI Services
a. Product Evaluation — If an organization is evaluating multiple products, having a standardized AI Test Harness will help objectively evaluate and compare them.
b. Privacy Preservation — Since organizations often use sensitive data like Compensation / Performance Data / PII (Personally Identifiable Information) to develop AI Models — it is often not possible to test the model periodically since the data will need to be released to the Model Assessment Team, which could be internal or external.
c. Bias / Fairness — The synthetically generated data can also be used to assess implicit Bias / Fairness by making sure the data is tweaked to compensate for any Bias that may exist in the actual data.
To conclude, AI Test Harnesses will help de-mystify the aura around AI Models which are still seen as “Black Boxes” by many Enterprise consumers, and increase adoption of Machine Learning frameworks. Additionally, this framework will also give AI Service providers the ability to objectively compare themselves with peers and incrementally improve their product offerings in alignment with industry standards.
