Final answer:
The approach to addressing machine learning/AI in healthcare focuses on ensuring legal transparency, considering ethical implications, and incorporating a multidisciplinary perspective to enhance safety and responsibility. It also involves preparing for the potential impact of AI on labor and privacy and making efforts to align AI with human values through adjustments in policy and design strategies.
Step-by-step explanation:
Our plan with respect to addressing machine learning/AI in healthcare involves a multifaceted approach. Foremost, we aim to ensure legal transparency for artificial intelligence, recognizing the need to evaluate whether AI is more harmful or helpful to society. This focus extends to the unpredictability and difficulty in controlling AI, with particular attention to the biases present within algorithms. The intention is to create a framework that can increase transparency effectively.
To address the ethical considerations of AI in healthcare, we must also be aware of the industries where AI has already become embedded, like self-driving cars and virtual butlers. Understanding the existing spectrum of thought on AI ethics and governance is crucial. Informed by perspectives such as those expressed by Genevieve Bell and Nick Bostrom, we consider the importance of using artificial intelligence safely, sustainably, and responsibly. Bell advocates for a human-scale technology approach by integrating an anthropological perspective into technological development.
In response to concerns highlighted about AI's impact on labor, privacy, and human values, solid strategies must be devised. These include diversifying the engineering core to incorporate insights from social and cognitive sciences, developing ethics certifications for AI designers, and introducing mechanisms to reduce the automatic nature of AI interactions. Awareness of the potential for AI to both alleviate and exacerbate labor issues will guide the formulation of guidelines and regulations aimed at ensuring a smooth transition during the integration of AI into healthcare systems.