Explaining and Exploring Ethical and Trustworthy AI in the Context of Reinforcement Learning

An interdisciplinary approach to Artificial Intelligence (AI) and Machine Learning (ML) is necessary to address issues arising from the overlap in the areas of Reinforcement Learning (RL), ethics, and the law. Some types of RL, due to their use of evaluative feedback in combination with function approximation, give rise to new strategies for problem-solving that are not easily foreseen or anticipated, and embody the monkey paw problem. This is the problem related to RL that grants what one asked for, and not what one should have asked for or in terms of what was intended. Sometimes these new strategies can be characterized as promoting a social good, but there is the possibility that they could give rise to outcomes that are not aligned with social goods. Control applications in the form of supervised learning (SL)-based solutions may be used to control for unaligned new strategies. These control applications, however, may introduce bias such that ethical and legal regimes may need to be put into place to solve for such biases. These ethical and legal regimes may be based upon generally agreed to social conventions as traditional ethical regimes in the form of utilitarianism and deontological ethics may provide an incomplete solution. Further, these social conventions may need to be implemented by people and ultimately the corporations instructing these people on how to perform their jobs.