Focus on the Output
Posted: Tue Feb 11, 2025 7:02 am
The solution is straightforward: continuous output monitoring. By focusing on the quality of the outputs rather than the complexities of the models themselves, businesses can safely utilize these powerful tools.
As an example, let’s consider hiring a new employee. There are many AI and other algorithmic tools available that claim to assist in this process and improve the quality of the decision. But given the risk of bias belgium whatsapp number data in AI tools, should an organization rely on the vendor’s marketing claims that their tool is low in bias or even “bias free”? Clearly, the answer is no. But is it reasonable to expect a deploying organization to fully and completely understand how the AI operates when even the engineers who built it likely don’t?
When making decisions about people, it is imperative to understand generally which factors are being weighted, but it is likely too onerous to expect a tool user to completely grasp the complexities of how an AI algorithm functions. It is practical to focus on outputs – what information is produced from the tool and what does it mean? Does it predict new hire success on the job? Is it low in observable bias between protected classes of individuals? These are the key outcomes that need to be monitored in an ongoing (and continual if possible) fashion.
As an example, let’s consider hiring a new employee. There are many AI and other algorithmic tools available that claim to assist in this process and improve the quality of the decision. But given the risk of bias belgium whatsapp number data in AI tools, should an organization rely on the vendor’s marketing claims that their tool is low in bias or even “bias free”? Clearly, the answer is no. But is it reasonable to expect a deploying organization to fully and completely understand how the AI operates when even the engineers who built it likely don’t?
When making decisions about people, it is imperative to understand generally which factors are being weighted, but it is likely too onerous to expect a tool user to completely grasp the complexities of how an AI algorithm functions. It is practical to focus on outputs – what information is produced from the tool and what does it mean? Does it predict new hire success on the job? Is it low in observable bias between protected classes of individuals? These are the key outcomes that need to be monitored in an ongoing (and continual if possible) fashion.