Our approach to ethical Ai
Building trust in ethical Ai through transparency
When you’re a leader in ethical Ai, you must make choices about the design, data, and science behind your products. At Sapia, we choose to maximize transparency, explainability, and fairness.
This resource explains the strategic choices that make up our Ai technology, and why you can trust it as a tool for recruitment and people decision-making.
This document will help to answer:
- Why Ai providers and technologies are different, and how the approach to transparency causes the divide
- Why ethical frameworks are crucial for Ai tools, particularly those used for human decision-making
- How and why Sapia approaches continuous and constant bias-testing
- Why Sapia does not use video data for any Ai component, and why it is unethical to do so
- Why the use of resume, meta, or any other kind of third-party data is unethical and results in suboptimal outcomes
- Why the use of explainable rule-based models is superior to classical machine learning models
- Why untimed assessments result in better, fairer outcomes for candidates
- How and why Sapia is the leader in fairness for Ai
Our bias testing is broad and continuous
While a model may pass all bias tests in its build process, when the same model is used with new candidates (data it has not seen before), it can generate biased scores due to data drift. We have bias constraints in the model build process, which prevents any models showing biased outcomes from going live. We don’t stop there, though. Our Discover Insights dashboards – to which customers have constant access – feature continuous bias reporting on live models. This is critical in building and maintaining customer trust in our Ai.
Ethical Ai is the key to unlocking better hiring outcomes
As a candidate screening technology, we believe that Inclusion starts with your candidate experience and your technology choices. It can end there too.
Download the document here
Learn why the right approach to ethical Ai really matters for your business.