Machine Learning Ethics for Engineers
Machine Learning Ethics for Engineers Machine learning offers powerful tools, but with power comes responsibility. Engineers shape systems that touch jobs, health, finance, and daily life. Ethics is not a side task; it guides data choices, deployment, and how we explain results to teammates and users. This article shares practical habits to reduce harm and build trust in ML projects. Bias and fairness are central concerns. Models learn from data that can reflect society’s gaps. Mitigate with diverse data, simple fairness checks, and clear explanations for decisions. Privacy matters too: minimize data collection, anonymize where possible, and protect access with solid security. Transparency helps people trust systems when data sources, model limits, and decision rules are easy to understand. Accountability means clear roles, audit trails, and sign-off before release. Finally, safety and robustness demand testing for edge cases, monitoring drift, and a ready rollback plan. ...