Global Data Science Forum - Data Science

The risks in the ML life cycle are also different since machine learning models have become pervasive in so many aspects of everyday consumer life – so much of which is tightly regulated. As machine learning models help automate important decisions in a wide variety of industries – banking, health care, airline schedules, telecom, shopping, entertainment, and so on – they become subject to much scrutiny about compliance, audits, needs for explainability, concerns about fairness and bias, privacy laws, security concerns, etc. Many of those activities are regulated, for important reasons. While more traditional software engineering similarly has security concerns, audits, etc., the stakes are not nearly as high: code can be debugged. Machine learning, especially when driven with large scale data, is substantially more difficult to trace and "debug" compared with coding.