A dying man squares off against a machine of his own creation, a system that deploys a formidable arsenal of persuasive weaponry in an attempt to convince the patient to end his own life. The central conflict of the story points to an increasingly important debate in the design of systems that use machine intelligence. What are the types of persuasive methods we should permit designers of machines to use, and under what contexts are certain methods inappropriate?
As control has become distributed across multiple actors, our social and legal conceptions of responsibility remain generally about an individual. If there’s an accident, we intuitively — and our laws, in practice — want someone to take the blame. The result of this ambiguity is that humans may emerge as “moral crumple zones.” Just as the crumple zone in a car is designed to absorb the force of impact in a crash, the human in a robotic system may become simply a component — accidentally or intentionally — that is intended to bear the brunt of the moral and legal penalties when the overall system fails.
Preliminary observations of rideshare drivers and their changing working conditions reveals the significant role of worker motivations and regional political environments on the social and economic outcomes of automation. Technology’s capacity for social change is always combined with non-technological structures of power—legislation, economics, and cultural norms.
How are practitioners grappling with the social impacts of AI systems? An AI Pattern Language presents a taxonomy of social challenges that emerged from interviews with a range practitioners working in the intelligent systems and AI industry. The book describes these challenges and articulates an array of patterns that practitioners have developed in response.