Teaching

Description. We will consider ethical, social, and professional issues arising from the use and misuse of computers and technology. The course is oriented to the perspective of the computer professional, aiming at developing ethical reasoning skills and sensitivities to the myriad of issues that could arise for the professional, and to the user, aiming at understanding the nature of our rights and responsibilities in an increasingly online world. A key feature will be to explore and understand the unique ethical issues which arise from the use of computers. There are three main classes of things which fall under computer use: computer hardware/infrastructure; programs; and data. As we will see, these are not always distinct categories, and computers have some properties which no other artifacts do.

Syllabus forthcoming.

Description. Artificial intelligence research is progressing quickly, and along with it the capacities of AI systems. As these systems become more sophisticated and more deeply embedded in society, it will become increasingly essential to ensure that we are able to maintain control of these systems, and that the decisions and actions they take are aligned with the values of humanity writ large. These are known, in the field of machine ethics, as the control problem and the value alignment problem.

In the first part of this course, we will examine the concepts of control and value alignment to see how they are connected and what practical, scientific, ethical, and philosophical questions arise when trying to solve these problems. We will focus on both the normative and technical components of value-aligned artificial intelligence—namely, how to achieve moral agency in an artificial system. The normative component of the value alignment problem asks what values or principles (if any) we ought to encode in an artificial system; whereas, the technical component asks how we can encode these values. In the final part of the course, we will examine the social, ethical, and philosophical consequences that might arise (indeed, have arisen) from misaligned AI systems.

Syllabus forthcoming.

Description. This course explores formal approaches (evolutionary game theory, network models, and agent-based models) and their applications in understanding and explaining social phenomena. The course will critically examine the philosophical foundations of these formal approaches and their relevance in addressing various questions in social science. Each week will focus on specific game structures and models that illustrate their applications in explaining real-world social phenomena. By the end of the course, students will gain a deep understanding of the conceptual and methodological aspects of formal approaches in the philosophy of social science.

Syllabus available here.

Description. Perhaps all knowledge of the natural world is arrived at by inductive inference. The predictions of our best scientific theories, medical diagnoses of pressing illnesses, and judgments about matters of political and economic importance are all varieties of inductive inference. As should be plain, not all inductive inferences are equally good. Inductive logic is the study of rules of good inductive inference.

This course introduces students to the fundamentals of inductive logic, historical and philosophical puzzles of induction, as well as modern developments. In order to develop skills in formulating and analyzing arguments, students will first be introduced to (or reminded of) the basics of deductive logic. We will then look at inductive arguments and various pitfalls humans fall into when reasoning inductively. This will lead us to seek out ways we might avoid such pitfalls and to investigate probability theory as a model for how to reason inductively. Classical philosophical problems (e.g., the Problem of Induction, the interpretation of probability, various probabilistic puzzles) will be explored along the way while students are equipped with tools they can use to improve their critical thinking and probabilistic reasoning in everyday contexts.

nb. This course was adapted, with permission, from Dr Aydin Mohseni’s course design for LPS 31: Introduction to Inductive Logic, taught at the University of California, Irvine, Summer 2021.

Description. We will consider ethical, social, and professional issues arising from the use and misuse of computers and technology. The course is oriented to the perspective of the computer professional, aiming at developing ethical reasoning skills and sensitivities to the myriad of issues that could arise for the professional, and to the user, aiming at understanding the nature of our rights and responsibilities in an increasingly online world. A key feature will be to explore and understand the unique ethical issues which arise from the use of computers. There are three main classes of things which fall under computer use: computer hardware/infrastructure; programs; and data. As we will see, these are not always distinct categories, and computers have some properties which no other artifacts do.

Winter 2023 syllabus forthcoming.

Winter 2022 syllabus (out of date) available here.

Description. Artificial intelligence research is progressing quickly, and along with it the capacities of AI systems. As these systems become more sophisticated and more deeply embedded in society, it will become increasingly essential to ensure that we are able to maintain control of these systems, and that the decisions and actions they take are aligned with the values of humanity writ large. These are known, in the field of machine ethics, as the control problem and the value alignment problem.

In the first part of this course, we will examine the concepts of control and value alignment to see how they are connected and what practical, scientific, ethical, and philosophical questions arise when trying to solve these problems. We will focus on both the normative and technical components of value-aligned artificial intelligence—namely, how to achieve moral agency in an artificial system. The normative component of the value alignment problem asks what values or principles (if any) we ought to encode in an artificial system; whereas, the technical component asks how we can encode these values. In the final part of the course, we will examine the social, ethical, and philosophical consequences that might arise (indeed, have arisen) from misaligned AI systems.

Winter 2023 syllabus forthcoming.

Winter 2022 syllabus (out of date) available here.

Description. According to Stuart Murray (2012), ‘The very idea of an autistic person is a philosophical one’. This course is about autism and philosophy. According to Kenneth Richman, the philosophy of autism is not (yet) a subfield of philosophy. Instead, philosophical work on autism falls mostly under ethics, philosophy of mind, philosophy of psychology, or philosophy of medicine. However, as we shall see, the autism spectrum provides a philosophically rich vehicle for considering philosophical perspectives in a much wider array of subfields and topics.

Owing to the nature of the subject, the presentation of this course will not be structured like a ‘typical’ philosophy seminar. Because of stigma, misinformation, and confusion surrounding autism, we will begin our seminar (Weeks 2-5) with an historical overview of autism as a diagnosis in the early-mid twentieth century. The first part of the course will be primarily lecture-based, with some in-class activities. To gain a better understanding of autism from within autism, the ‘readings’ for these weeks will consists in videos, essays, stories, blog posts, etc. told from an autistic perspective. The rest of the semester (Weeks 6-14) we will continue in a seminar format. Our discussions will span a variety of philosophically rich topics in the philosophy of autism and autistic approaches to philosophy.

Syllabus available here.

Description. Perhaps all knowledge of the natural world is arrived at by inductive inference. The predictions of our best scientific theories, medical diagnoses of pressing illnesses, and judgments about matters of political and economic importance are all varieties of inductive inference. As should be plain, not all inductive inferences are equally good. Inductive logic is the study of rules of good inductive inference.

This course introduces students to the fundamentals of inductive logic, historical and philosophical puzzles of induction, as well as modern developments. In order to develop skills in formulating and analyzing arguments, students will first be introduced to (or reminded of) the basics of deductive logic. We will then look at inductive arguments and various pitfalls humans fall into when reasoning inductively. This will lead us to seek out ways we might avoid such pitfalls and to investigate probability theory as a model for how to reason inductively. Classical philosophical problems (e.g., the Problem of Induction, the interpretation of probability, various probabilistic puzzles) will be explored along the way while students are equipped with tools they can use to improve their critical thinking and probabilistic reasoning in everyday contexts.

nb. This course was adapted, with permission, from Dr Aydin Mohseni’s course design for LPS 31: Introduction to Inductive Logic, taught at the University of California, Irvine, Summer 2021.

Description. We will consider ethical, social, and professional issues arising from the use and misuse of computers and technology. The course is oriented to the perspective of the computer professional, aiming at developing ethical reasoning skills and sensitivities to the myriad of issues that could arise for the professional, and to the user, aiming at understanding the nature of our rights and responsibilities in an increasingly online world. A key feature will be to explore and understand the unique ethical issues which arise from the use of computers. There are three main classes of things which fall under computer use: computer hardware/infrastructure; programs; and data. As we will see, these are not always distinct categories, and computers have some properties which no other artifacts do.

Syllabus available here.

Description. Artificial intelligence research is progressing quickly, and along with it the capacities of AI systems. As these systems become more sophisticated and more deeply embedded in society, it will become increasingly essential to ensure that we are able to maintain control of these systems, and that the decisions and actions they take are aligned with the values of humanity writ large. These are known, in the field of machine ethics, as the control problem and the value alignment problem.

In the first part of this course, we will examine the concepts of control and value alignment to see how they are connected and what practical, scientific, ethical, and philosophical questions arise when trying to solve these problems. We will focus on both the normative and technical components of value-aligned artificial intelligence—namely, how to achieve moral agency in an artificial system. The normative component of the value alignment problem asks what values or principles (if any) we ought to encode in an artificial system; whereas, the technical component asks how we can encode these values. In the final part of the course, we will examine the social, ethical, and philosophical consequences that might arise (indeed, have arisen) from misaligned AI systems.

Syllabus available here.

Description. In a 1973 article, evolutionary biologist Theodosius Dobzhansky proclaimed that ‘nothing in biology makes sense except in the light of evolution’. One way to understand this provocative statement is that biology (in terms of the observed diversity of life and its distribution on the earth’s surface) only makes sense in the light of evolution because it only makes sense if life on earth has a shared history. If we take this sentiment seriously, then in order to (truly) understand (nearly) any facet of human life, we ought to understand it in the context of the evolutionary history of humanity (as a species). Rather than assuming that ethics is the result of divine revelation or the application of our rational faculties, evolutionary ethics examines the possibility that morality is a social phenomenon, borne out of the biological and cultural evolution of intelligent, social creatures.

In this course, we will examine proto-morality and pro-social behaviour, as it appears in non-human animals (especially primates). We will also examine possible mechanisms of evolution that may apply to the evolution of moral behaviour in humans, including natural selection, sexual selection, kin selection, and group selection, in addition to social adaptations that may aid, foster, or give rise to moral behaviour, including altruism, signalling, cooperation, and conventions. On the philosophical side of things, we will discuss how a biological point of view allows and disallows certain normative concepts; we will also discuss criticisms of such a biological approach and contemporary challenges for evolutionary ethics.

In a paraphrase of a question posed by the linguist and cognitive scientist Massimo Piattelli-Palmarini, this course will essentially centre upon two converse questions:

1. What is ethics, that it may have evolved? And,
2. What is evolution, that it may apply to ethics?

Syllabus available here.

Description. Artificial intelligence research is progressing quickly, and along with it the capacities of AI systems. As these systems become more sophisticated and more deeply embedded in society, it will become increasingly essential to ensure that we are able to maintain control of these systems, and that the decisions and actions they take are aligned with the values of humanity writ large. These are known, in the field of machine ethics, as the control problem and the value alignment problem.

In the first part of this course, we will examine the concepts of control and value alignment to see how they are connected and what practical, scientific, ethical, and philosophical questions arise when trying to solve these problems. We will focus on both the normative and technical components of value-aligned artificial intelligence—namely, how to achieve moral agency in an artificial system. The normative component of the value alignment problem asks what values or principles (if any) we ought to encode in an artificial system; whereas, the technical component asks how we can encode these values. In the final part of the course, we will examine the social, ethical, and philosophical consequences that might arise (indeed, have arisen) from misaligned AI systems.

Syllabus available here.