Principle 4 – follow ethical, responsible, and well-governed ML practices
Ethical and responsible ML practices become increasingly important as data-centricity allows us to tackle more high-stakes challenges. This requires you to consider factors such as transparency, fairness, and accountability when designing algorithms so that they do not discriminate against certain groups or individuals. Additionally, those responsible for implementing these systems must be aware of how they work and understand their limitations so that they can make informed decisions about their use.
Unfortunately, ethical and responsible ML practices are generally not as developed as they should be. In 2021, the IBM Institute for Business Value and Oxford Economics conducted a study1 where 75% of executives ranked AI ethics as important; however, fewer than 20% of executives strongly agreed that their organizations’ practices aligned with their declared principles and values.
As practitioners...
