Richard G. Epstein
In our paper, "Ethical Guidelines for AI in Education: Starting a Conversation," we tried to present a framework for evaluating the societal impact of new educational technologies that employ artificial intelligence. The paper presents ten fundamental principles for the design of such systems. These principles, in turn, were derived from two meta-principles that are described here.
The Negative Meta-Principle for AIED
AIED (AI in Education) technology should not diminish the student along any of the fundamental dimensions of human being.
The Positive Meta-Principle for AIED
AIED (AI in Education) technology should augment the student along at least one of the fundamental dimensions of human being.
We proposed the following six dimensions of human being as being fundamental:
This dimension refers to actions and behaviors insofar as they might have an impact upon other human beings, creatures, and the environment. This dimension relates to an understanding of basic ethical principles and a willingness to act in accordance with that understanding.
This dimension refers to having an appreciation for beauty in all of its manifestations. This includes beauty in nature, the arts, mathematics, science, and technology.
This dimension refers to an individual's concept of self and his/her relation to others. This dimension has to do with the values of community, family, and friendship.
This dimension refers to the human intellect and its manifest and manifold powers. These include the ability to understand existing knowledge and to create new knowledge.
This dimension refers to basic phsyical health, including all aspects of physical well-being, such as exercise, the avoidance of harmful substances and habits.
This dimension refers to the individual's ability to lead a happy and fulfilling life. This dimension is also related to the social, intellectual, aesthetic, and ethical dimensions.
FUNDAMENTAL PRINCIPLES FOR
Using the meta-principles and the fundamental dimensions of being, we formulated ten principles for AIED systems. Many of these principles can be generalized for other types of AI systems as well: