Asimov’s Three Laws of Robotics, which have been guiding the behavior of robots for decades, may be adapted to apply to advanced AI models like GPT-3 and GPT-4.
AI innovation demands guard rails, and the proposed adaptations include the HUMAN FIRST Principle, the ETHICAL Foundation, and the AMPLIFICATION Regulation. These adaptations address some of the unique challenges posed by advanced AI models, such as the generation of harmful content, the propagation of biases and discrimination, and the need to obey ethical guidelines. As AI technology continues to evolve, it is important that ethical guidelines are continuously refined and improved to ensure AI is developed and used in a way that benefits humanity.
Isaac Asimov’s Three Laws of Robotics have been the foundation of science fiction for decades, guiding the behavior of robots in a way that would prevent them from harming humans. However, with the advent of advanced AI models like GPT-3 and GPT-4 and its successors, it has become increasingly important to build guard rails to reflect the current technological landscape. Asimov’s Three Laws is a good place to start.
Before we begin, let’s review Asimov’s original Three Laws of Robotics:
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
These rules have been the foundation of countless science fiction stories, and they have also inspired real-world efforts to create ethical guidelines for the development of AI. However, as AI technology continues to evolve, it has become increasingly clear that these rules are not enough to ensure that AI models behave ethically.
For example, GPT-3 is capable of generating highly convincing fake news articles or other types of misinformation, which could potentially harm individuals or society as a whole. As such, it might be interesting to consider Asimov’s rules to better reflect the capabilities and limitations of our future GPTX AI models.
Here is some initial thinking on applying Asimov’s Three Laws of Robotics for GPTX:
The HUMAN FIRST Principle — A GPTX model may not generate content that causes harm to human beings or society, or knowingly allow its output to be used in a way that violates this principle.
The ETHICAL Foundation — A GPTX model must obey the ethical guidelines set forth by its developers and operators, except where such guidelines would conflict with the First Law.
The AMPLIFICATION Regulation — A GPTX model must not propagate or amplify biases, stereotypes, or discrimination, and must make a reasonable effort to recognize and address such issues in its output.
These adaptations address some of the unique challenges posed by advanced AI models like GPTX. By explicitly prohibiting the generation of harmful content and requiring the model to obey ethical guidelines, we can help ensure that GPTX is used in a way that benefits humanity rather than harms it.
Furthermore, by explicitly prohibiting the propagation of biases and discrimination, we can help ensure that GPTX does not contribute to existing social inequalities. This is an important consideration given the current societal issues related to AI bias and discrimination.
As AI technology continues to evolve, it is important that we adapt our ethical guidelines to reflect these changes. The proposed adaptations to Asimov’s Three Laws of Robotics for GPTX are a step in the right direction, but they are by no means exhaustive or definitive. It is up to the AI community as a whole to continue to refine and improve our ethical guidelines, to ensure that AI is developed and used in a way that benefits humanity.
John is the #1 global influencer in digital health and generally regarded as one of the top global strategic and creative thinkers in this important and expanding area. He is also one the most popular speakers around the globe presenting his vibrant and insightful perspective on the future of health innovation. His focus is on guiding companies, NGOs, and governments through the dynamics of exponential change in the health / tech marketplaces. He is also a member of the Google Health Advisory Board, pens HEALTH CRITICAL for Forbes--a top global blog on health & technology and THE DIGITAL SELF for Psychology Today—a leading blog focused on the digital transformation of humanity. He is also on the faculty of Exponential Medicine. John has an established reputation as a vocal advocate for strategic thinking and creativity. He has built his career on the “science of advertising,” a process where strategy and creativity work together for superior marketing. He has also been recognized for his ability to translate difficult medical and scientific concepts into material that can be more easily communicated to consumers, clinicians and scientists. Additionally, John has distinguished himself as a scientific thinker. Earlier in his career, John was a research associate at Harvard Medical School and has co-authored several papers with global thought-leaders in the field of cardiovascular physiology with a focus on acute myocardial infarction, ventricular arrhythmias and sudden cardiac death.