Adaptive AI Alignment: Established Resources for Aligning Machine Learning with Human Intentions and Values in Changing Environments - Stephen Fox (Machine Learning and Knowledge Extraction)
AI Alignment is a term used to summarize the aim of making artificial intelligence (AI) systems behave in line with human intentions and values. There has been little consideration in previous AI Alignment studies of the need for AI Alignment to be adaptive in order to contribute to the survival of human organizations in changing environments. This research gap is addressed here by defining human intentions and values in terms of survival biophysics: entropy, complexity, and adaptive behavior. Furthermore, although technology alignment has been a focus of studies for more than thirty years, there has been little consideration in AI Alignment studies of established resources for aligning technologies. Unlike the current focus of AI Alignment on addressing potential AI risks, technology alignment is generally focused on aligning with opportunities. Established resources include the critical realist philosophy of science, scientific theories, total quality management practices, technology alignment methods, engineering techniques, and technology standards. Here, these established resources are related to the alignment of different types of machine learning with different levels of human organizations. In addition, established resources are related to a well-known hypothetical extreme example of AI Misalignment, and to major constructs in the AI Alignment literature. Overall, it is argued that AI Alignment needs to be adaptive in order for human organizations to be able to survive in changing environments, and that established resources can facilitate Adaptive AI Alignment which addresses risks while focusing on opportunities.