The Alignment Problem: Machine Learning and Human Values

Brian Christian’s The Alignment Problem is set against a backdrop of rapid technological advancements, particularly in the field of artificial intelligence, and the societal implications that these technologies entail. As AI systems become increasingly integrated into various aspects of daily life—from decision-making systems in healthcare, finance, and criminal justice to autonomous vehicles and personal assistants—the need to ensure these systems’ decisions align with human values and ethics has never been a pressing concern for many specialists in technical and social science fields.

The alignment problem refers to the challenges and risks posed when AI systems behave in ways that are unforeseen or contrary to the intentions of their creators, often due to mismatches between the goals programmed into AI and the broader values of society. These issues are not just technical but are embedded in societal norms, ethics, and the complexities of human behavior. As AI technologies advance, their potential to impact society on a structural level grows, raising questions about privacy, security, fairness, and the potential perpetuation of existing inequalities.

In the 2016 paper “The AI Alignment Problem: Why It’s Hard, and Where to Start,” Eliezer Yudkowsky starts with the following question: “If we can build sufficiently advanced machine intelligences, what goals should we point them at?” (1).