You don't need to understand how a neural network weighs parameters to understand that your AI shouldn't discriminate against your customers. AI Ethics isn't a technical challenge; it's a leadership challenge.
For founders, the responsibility stops with you. If you deploy an AI hiring tool that rejects qualified candidates based on biased historical data, "the algorithm did it" is not a valid defense. It's your company, your brand, and your responsibility.
Audit Your Inputs
The output of any AI system is a reflection of its inputs. If you train a model on data that contains historical biases, the model will not only learn them but amplify them. Ask your technical team or vendors: "What data was this trained on? How was it vetted for bias?"
Explainability
Avoid "black box" systems where decisions cannot be explained. If an AI denies a loan application or flags a transaction as fraudulent, you need to be able to explain *why* that decision was made. If you can't explain it, you can't trust it.