The Alignment Problem: Machine Learning and Human Values

Brian Christian’s The Alignment Problem focuses not only on the development of AI technologies but, most importantly—as the title of the book suggests—on how to align machine learning and AI use in general with human values, ethics, and behaviors. The thematic significance of the intersection of human and machine learning remains central throughout the book, highlighting both the potential benefits and pitfalls of these technologies as they become more integrated into daily human life.

Christian explores the intersection of human and machine learning through various lenses, including the need for representative training data, the risks of AI systems perpetuating existing biases, and the challenges of designing AI that can interpret and respond to complex human behaviors and intentions.

One of the critical aspects Christian discusses is the concept of training data. He illustrates how AI systems are only as good as the data from which they learn. For instance, face recognition technologies have improved significantly as datasets have become more inclusive. However, these advancements also raise concerns about surveillance, especially as the legislation for regulating the use of AI is missing in many countries around the world. This discussion underscores the importance of careful consideration in data collection to ensure AI systems do not reinforce existing inequalities.