The future of responsible AI: Balancing innovation with ethics

By Shrish Ashtaputre, Senior Technical Director Engineering, Calsoft

Teams today are using generative AI to write code, convert logic across languages, draft documentation, design tests, and even identify vulnerabilities in massive repositories. Machine-learning systems are analyzing code changes, predicting which test cases matter most, and helping teams ship faster than ever before. But here’s the uncomfortable truth: without responsible AI practices in place, every one of these accelerators can also become a multiplier for mistakes.

This is not a theoretical concern. A computer-vision system that is trained primarily on one skin color may give acceptable test results, but nevertheless may not be able to consistently recognize someone’s face when exposed to actual conditions in the real world. This happens not because of a deficiency in the algorithm itself, but because the dataset used to train that model was not diverse enough, thereby resulting in the ...