Michael Zahorec

Philosopher & Computer Scientist. AI Ethics, Explainability, and Evaluation. Philosophy of Science.

Teaching Portfolio

My teaching philosophy, course evaluations, and sample syllabi are available here: my teaching portfolio.


Sample Course Descriptions (see Teaching Portfolio for syllabi)

Theory and Ethics of AI

This course covers central topics in the theory and ethics of artificial intelligence. After providing background on philosophy and AI, we dig into non-generative AI, followed by generative AI. About both of these, we focus on persistent issues and difficult dilemmas. We approach these issues and dilemmas with an interdisciplinary perspective, highlighting especially the connections between philosophy, politics, law, computer science, and data science. Main topics include fairness and bias in algorithmic systems, epistemic opacity and its practical and ethical upshots, and the appropriate scope for the use of AI in our lives and work.

Environmental Ethics

This course covers some central topics in environmental ethics and related fields: pollution, environmental destruction, and related human behavior and policies; food production, distribution, and related issues; and poverty and its relation to the aforementioned topics. In addition to covering some concrete issues under each of these headings, we will cover some introductory material in value theory, philosophy of science, and logic. On these fronts, we aim to develop the skills and gain the concepts required to read, evaluate, and produce philosophical arguments about environmental issues and policies. By the end of the semester, students should be able to read, discuss, and write about issues in environmental ethics in a respectful, clear, and productive manner. This involves having (1) a strong understanding of and ability to work with some central concepts and conceptual distinctions (e.g., intrinsic versus instrumental value and normative versus non-normative claims); (2) a general awareness of some central issues in environmental ethics; and (3) a general philosophical skillset which includes the ability to understand, evaluate, discuss, and create abstract arguments in a clear and respectful manner.

Reasoning and Critical Thinking

Note: I teach this course with materials developed by Mike Bishop (Professor, FSU Philosophy).

The goal of this class is to give you the tools to be a better critical thinker and decision-maker. Topics include: opportunity costs, sampling arguments, testimony, echo chambers, politeness, sharpening and leveling, causal arguments, policy evaluation, controlled experiments, randomized studies, pre-post studies, status quo bias, sunk costs, confirmation bias, statistical independence, performance curves, regression to the mean, herd reasoning, gambler’s fallacy, clustering illusion, explanatory power, and inference to the best explanation (IBE).

Symbolic Logic

Note: I teach this course with materials developed by Piers Rawling (Professor, FSU Philosophy), including the following course description.

Why are some arguments good (valid) and others bad (invalid)? How can you tell the difference? In this course you will address these questions by:
(i) explaining the nature of validity;
(ii) symbolizing arguments and using rigorous methods to determine whether they are valid; and
(iii) producing good arguments of your own.

The arguments analyzed (both weak and strong) will be drawn from a variety of sources, including ethics, science, and mathematics. The first half of the course covers sentential logic; the second half covers predicate logic, which expands upon sentential logic. One of the persistent questions about (good) arguments, dating at least as far back as Aristotle, is whether they all share certain forms. Sentential logic endeavors to capture the form of arguments involving ‘sentential connectives’, such as ‘or’ and ‘if … then…’; predicate logic adds to this the logic of the ‘quantifiers’: ‘there exists’ and ‘for all’. Using sentential logic alone we can, for example, analyze the hypothetico-deductive method employed in scientific investigation. Using the increased power of predicate logic we can explore issues ranging from whether there need be (as many people seem to think) exceptionless moral principles, to the grammatical distinction between ‘which’ and ‘that’. Predicate logic is also the language of mathematics, in the sense that all of mathematics can be expressed within it.

Those who acquire the knowledge and skills taught in this course will be able to:
(i) distinguish between good and bad reasoning, whatever the topic, and explain the difference; and
(ii) think critically about any subject matter, and produce cogent arguments concerning it.


Teaching Areas

I am would be glad to teach in the following areas, or adjacent ones.

Philosophy

  • Introduction to Philosophy
  • Philosophy of Science
  • Philosophy of AI
  • Data Ethics
  • AI Ethics
  • Technology Ethics
  • Applied Ethics
  • Environmental Ethics
  • Engineering Ethics
  • Ethical Theory (especially virtue ethics and Kantian ethics)
  • Logic
  • Reasoning and Critical Thinking
  • Kant (his theoretical or practical philosophy)
  • Metaphysics (esp. as it relates to phil sci and to realism/anti-realism debates)
  • Epistemology (esp. of AI)
  • Action Theory (intentional action, causal action theory, etc.)
  • Philosophy of Action (free will, moral responsibility, etc.)
  • Philosophy of Language
  • Ordinary Language Philosophy
  • Wittgenstein
  • History of Analytic Philosophy
  • Pragmatism
  • Experimental Philosophy


Computer/Data Science

  • Data Science
  • Data Ethics
  • Explainable/Interpretable AI
  • AI Evaluation
  • History/Theory of AI
  • Theory of Computation
  • Logic for Computer Science
  • Digital Humanities