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). While in popular discourse the ethical and social contexts that AI is associated with seem straightforward, the reality is that the research in the field is led by specific companies and institutions. For example, as Yudkowsky discusses, institutions like the Machine Intelligence Research Institute at Berkeley, the Future of Humanity Institute at Oxford, and the Centre for the Future of Intelligence play crucial roles in funding and developing research that aims to align AI capabilities with human values and safety requirements. However, the research produced by these institutions is often not accessible to a wider public due to its highly technical and specialized nature. As a result, much of the public discourse regarding AI and the alignment problem is plagued by fear, disinformation, and confusion.

Public discourse around AI ethics has escalated due to several high-profile incidents that illustrate the potential dangers of misaligned AI systems. For instance, biases in facial recognition technologies, discrimination in automated decision-making in hiring and law enforcement, and accidents involving autonomous vehicles have all prompted debates on the need for regulation and oversight in AI development. These concerns reflect broader anxieties about the loss of control over AI technologies and their ability to make decisions that profoundly affect individuals' lives without accountability or transparency.

Christian’s book works to dispel this confusion and provide context and depth to the public’s exploration of these issues. Christian’s analysis, which is written in accessible prose for a non-specialized public, also points to the gap between the rapid development of AI and the slower pace of policymaking. This lag poses significant challenges for ensuring that AI systems do not harm society. For example, the European Union has taken steps toward comprehensive AI regulation, while other regions are still in the nascent stages of addressing these issues. This disparity in regulatory approaches can lead to inconsistencies and loopholes that exacerbate the alignment problem.