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In this thesis I discuss issues involving artificial intelligence and personhood. "Personhood" is a term we often attribute to human beings. My goal in this thesis is to define personhood, show how personhood can be present in varying degrees, and finally show why artificial intelligence may be candidates for personhood in the future. More specifically, in this thesis I will discuss five concepts of personhood. These are the metaphysical concept of personhood, the moral concept of personhood, the moral agent concept of personhood, the legal concept of personhood, and the religious concept of personhood. After discussing these concepts, a feasible definition will be given for what it means to be a person. Following this, the practical and philosophical problems that currently prevent artificial intelligence from being considered persons will be discussed along with some proposed solutions to these problems. Once these problems are dealt with we may have serious candidates for personhood. If artificial intelligence qualifies as having personhood then we will have some ethical issues to address. This thesis will discuss these ethical issues in the final chapter by giving reasons for treating artificial intelligence ethically if they should qualify as persons. This point will be defended from a Kantian standpoint using his categorical imperative requiring respect for persons.

The results of empirical comparisons of existing learning algorithms illustrate that each algorithm has a selective superiority; each is best for some but not all tasks. Selective superiority arises because each learning algorithm searches within a restricted generalization space, defined by its representation language, and employs a search bias for selecting a generalization in that space. Given a data set, it is often not clear beforehand which algorithm will yield the best performance. The problem is complicated further because for some learning tasks, different subtasks are learned best using different algorithms. In such cases, the ability to form a hybrid classifier that combines different representation languages will produce a more accurate classifier than employing a single representation language and search bias. This dissertation presents an approach to overcoming this problem by applying knowledge about the biases of a set of learning algorithms to conduct a recursive automatic algorithm search. The approach permits classifiers learned by the available algorithms to be mixed in a recursive tree-structured hybrid, thereby allowing different subproblems of the learning task to be learned by different algorithms. The Model Class Selection System (MCS), an implementation of the approach, combines decision trees, linear discriminant functions and instance-based classifiers in a tree-structured hybrid classifier. Heuristic knowledge about the characteristics that indicate one bias is better than another is encoded in the rule base that guides MCS's search for the best classifier. An empirical evaluation illustrates that MCS achieves classification accuracies equal to or higher than the best of its primitive learning components for a variety of data sets, demonstrating that domain-independent knowledge about the biases of machine learning algorithms can guide an automatic algorithm selection search.