Graduate or upper division courses

I have a number of courses that I can teach as elective, upper division courses, or at the graduate level with little or no preparation.

Artificial morality and Machine Ethics

In this course, we explore the ethical and moral aspects of AI. We are interested in moral cognition for artificial agents. We discuss whether artificial agents are able to make moral decisions. Can morality be learned through a machine learning algorithm? Do we need to hard-code ‘rules of moral behavior’ in machines?

Textbook: Allen, Colin, and Wendell Wallach. 2009. Moral Machines: Teaching Robots Right from Wrong. Oxford University Press.

Artificial agency: Trust, confidence and reliability

We start with the concepts of ‘trust’ and ‘confidence’ that we humans develop in others and extend them to scientific knowledge, technologies, artifacts, and to social or political structure. Can we trust a technology such as AI or driverless cars? We discuss the epistemology and ethics of trust.

Textbook: Simon, Judith, (ed). 2020. The Routledge Handbook of Trust and Philosophy. Routledge.

Scientific discovery and justification in the era of AI, Machine learning and Big Data

This course is centered on the impact of artificial intelligence (AI) and machine learning (ML) on scientific progress and scientific discovery. Is ML the right tool to discover new laws of nature, new symmetries, new invariants, or new patterns in nature? How do we use AI or ML to justify scientific hypotheses? Is Big Data a new type of scientific evidence? Is the deluge of data helping us in finding new physics/chemistry and new biology? Are the new algorithms powerful enough to eliminate the inherent prejudice or bias all scientists have, or they just extend the known ‘human’ problems of scientific knowledge? Most of the materials will be papers published in the philosophy of science and computer science literature.

“Magnet concepts” in natural sciences

This course is based on a set of concepts in natural sciences, devised as sometimes as polar categories or opposite antipodes of a spectrum, and sometimes as complementing eachother. Sometimes the middle position between two extremes is the hardest to comprehend, therefore pure cases are didactically more useful (they are most of them idealized and abstracted). But ‘magnet concepts’ bring together these perspectives and gather known information from physics, biology, chemistry onto a common philosophical ground. Here is a short list of possible magnet concepts commonly used in natural sciences:

• Determinism versus indeterminism; Reversibility and chaos in natural world

• Predictability and manipulability of systems. Models, theories, conjectures in natural science

• Non-locality versus locality: the holistic view versus mechanistic view
• Linearity versus entanglement and extreme non-linearity
• Reductionism versus emergence (esp. in biology, but in physics, and chemistry)
• Laws, mechanisms (or mere generalizations) in natural sciences (compare and contrast physics and chemistry with biology)
• Natural kinds (“carving nature at its joints”)
• Discrete versus continuous quantities or features of systems
• Interdisciplinarity, cross-disciplinarity versus “fragmented sciences”
• The quantum/classical divide, the gravity/other forces divide and other cases of strong (inseparable) divisions in science.

They can be easily shaped by looking at concrete cases of subdisciplines in physics, chemistry, biology etc.

This course is designed to explore some of these magnet concepts and it is suitable for students with a major or minor degree in: most STEM disciplines, and students in philosophy, psychology, cognitive sciences. One important feature of this course is its inherent inter-disciplinarity, and cross-disciplinarity and its natural place in a liberal arts education. Its main outcome is to show that a dialogue among the multiple cultures: humanities, sciences, mathematics, and technology (STEM) is not only possible, but desirable and achievable.

The overall gain of our class is to show to all students, both in STEM areas and in the humanities, how the gap between the “two cultures” (the natural science versus social science and humanities), can be understood and, ultimately, surpassed. This course is the natural place to reflect upon the two cultures, and their relationship. We postulate a weak “unity of knowledge,” both geographically and across disciplinary boundaries, as well as the significance of great questions, and foundational thinking that permeate philosophy and natural sciences. This course argues for (a) the central role of science in a liberal arts college and (b) the importance of humanities (philosophy, history) in any STEM education.

After a romp through philosophy of science (unit 0), we approach the conceptual foundation of classical mechanics, mostly as it was established during and after the first Scientific Revolution. We discuss here concepts such as determinism, causality, mechanistic philosophy (and compare and contrast it with two forms of vitalism), as well as locality, separability, etc. We tackle the formalism of classical mechanics and we refer to classical electromagnetism and the chemistry of the 19^th century. In unit II we discuss the question of reductionism and emergence in the context of physics and then biology. We still aim to discuss the classical cases, without a direct reference to quantum mechanics. The paradigmatic case of reduction is the relation between thermodynamics and statistical mechanics, which had major implications for biology and chemistry. We evaluate critically the empirical base of the reductive claims as well as different criticism of it.

In unit II we start with questioning the classical picture of the world—according to which the world is local (there is no action-at-distance) and deterministic (future states are fully determined by present and past states) and separable in smaller units. Albeit it is true that in investigating some classical systems some have questioned even locality and determinism, it is precisely the quantum mechanics that forces us to radically rethink determinism, separability and locality. Is the quantum physics a conceptual revolution? Is Kuhn right in arguing for the fragmentation of physics (and other sciences) after each revolution? After presenting the concepts and the structure of quantum mechanics in unit II, we discuss Schrödinger’s cat paradox and succinctly entanglement. The main focus of this unit is the problem of measurement and some notorious interpretations of quantum mechanics that allegedly solve it. We focus here on the Bohmian interpretation and the many-worlds interpretation. In the context of these interpretations, we address some questions such as: Does QM describe the world or the process of measuring it? Do quantum particles have properties related to the measurement process? What is superposition? Are quantum objects as real as classical objects? Is consciousness involved in the process of measurement? These will be the topic of the first written assignment.

In dealing with quantum mechanics we emphasize (a) the current stage of the theory and its philosophical implications and (b) the relation with classical physics, mainly classical mechanics and classical electromagnetism and (c) the relevance of these foundational issues to the practice of other disciplines such as chemistry and biology. The last meetings of unit III are dedicated to some more general aspects of the philosophy of biology related to reductionism, explanation, prediction and unification. We discuss reductionism in biology, and the different worldviews at work in biology. We insist on the relation between biology and chemistry, and biology and physics. The “science communication” aspect of this course emphasizes the role of conceptual analysis and the analysis of different biases present in the practice of science

As our approach to philosophy of biology is much more restricted, unit III explores only the two themes related to the role of function and randomness, and chance in biology. We draw some important conclusions about the role functions and chance plays in biology by comparing and contrasting them with physics. We discuss the status of biological laws, natural kinds and the discrete/continuous metaphysical perspectives in biology, by comparing and contrasting when possible with physics or chemistry.

Unit IV is dedicated to concrete cases from physics biology and chemistry. Students research and present applications of the discussion from units 0-III.

Learning Outcomes

The ultimate aim of this course is to draw “conceptual bridges” between different STEM disciplines and humanities (mainly philosophy and history). We focus on foundations of different areas in physics, biology and chemistry, by approaching some issues in these disciplines from a philosophical perspective. We are interested in inter-disciplinary connections, in the way sciences communicate and in concepts that travel beyond the boundaries of one discipline into other sciences, or even in philosophy, or humanities. For example, concepts such as time, space, causation, energy, information, complexity, parthood, etc. are all used virtually in all STEM disciplines as well as in philosophy or humanities.

Certainty, uncertainty, and decision making in science

It is a widespread opinion that science is one of the most successful path to knowing and representing the world. Scientists are able to predict, explain, and help all of us make the world a better place. Science tracks truth and builds model of the world. Last but not least, we understand better the world through science. Most of us think that science is one of the few sources of truth and knowledge and for some of us science is the only source of real knowledge. This perspective is sometimes called ‘scientism’ and originated the 18^th century Enlightenment and has been refined by the positivism of the 19^th century. In the 20^th century scientism was promoted by philosophers and scientists under the name of logical positivism. The trust in science and technology has been eroded in the first decades of the 21^st century. This course will put scientism in a broader perspective and reconstruct it to match the reality of the 21^st century. We will ground our analysis on the idea that whereas some scientific results offer some degree of certainty and provide us with true statements about the world, most of our scientific results are less certain, further away from truth, but closer to what we can call ‘probabilistic claims.’ One line of thought is to accept induction as the main method in science. The other is to admit that uncertainty is always present in science. What means to think probabilistically about the world? We explore science as a continuum between a repository of truth on one hand, and on the other hand a tool which helps us navigate a complex, hard to understand and hard to control world, meshed with uncertainty.

We start by assessing our limited cognitive capacity to deal with uncertainty: probability and statistics, unlike numbers or geometrical shapes, are less intuitive. We are prone to statistical fallacies more than fallacies in arithmetic or geometry. In scientific inquiry the skill for good reasoning and decision making is needed more than anywhere. Flawed reasoning and bad decisions in science have dramatic consequences. This course equips master students with interest in liberal arts with a core of concepts needed to avoid fallacies and to better understand and communicate science. Even for those who are not scientists, many decisions depend upon results of scientific inquiry. This requires that we understand how scientific reasoning works and be able to assess whether a given result is trustworthy. We continue with an attempt to delineate science from other forms of knowledge, especially from pseudo-scientific activities. We explain what makes science a different endeavor and how to avoid scientific fallacies. Then we focus on three central cognitive processes in science: reasoning about causal hypotheses, solving problems based on statistical results, and assessing risks related to policies based on statistics.

This course focuses on methods of reasoning and inquiry commonly used by scientific communities and addresses these questions: (1) What makes for a good piece of reasoning in science? (2) Can you ever be certain of the truth or falsity of a scientific hypothesis? (3) How objective is scientific observation? (4) What can we learn from discovering correlations between variables and how can we avoid being misled by illusory correlations? (5) What does it take to establish a causal relationship? (7) How do we decide in science when values, society, risk, and other non-epistemic factors are involved in science? (8) Is statistical reasoning an alternative to deductive reasoning? (9) What are probabilities good for in science?

Units of the course:

• Unit 0: Science and pseudoscience, falsity, and fallacies in science

• Unit I: Certainty and truth in science
• Unit II: Causal models and causal hypotheses
• Unit III: Statistical models and statistical hypotheses
• Unit IV: Risk, values, and uncertainty. How do we evaluate them?
• Unit V: Case studies of scientific reasoning and decision making (examples: climate science, medical science, emergent technologies and their impact)

Objectives

Upon completion of this course students will be:

• adept at systematic argumentation

• in a position to accept or reject critically pieces of scientific reasoning in virtually any discipline
• able to distinguish good from poor design of scientific experiments or methodologies
• understand better statistical reasoning and how to assess the risks of reasoning under uncertainty.
• developing a critical appreciation for the methods by which knowledge is acquired in science.
• differentiating good from poor reasoning and decision making in science and other domains.
• assessing decisions in science made when values, society, risk, and other non-epistemic factors
• This course emphasizes active engagement in kinds of reasoning and decision making which scientists use in developing and testing hypotheses.

Resources

• Giere, Ronald N., John Bickle, and Robert Mauldin. 2006. Understanding Scientific Reasoning. 5th ed. Wadsworth Publishing.

• Cummins, Denise D. 2012. Good Thinking: Seven Powerful Ideas That Influence the Way We Think. 1st edition. Cambridge: Cambridge University Press.
• Sytsma, Justin, and Jonathan Livengood. 2015. The Theory and Practice of Experimental Philosophy. Peterborough, Ontario: Broadview Press.

Several other resources, multimedia files, articles, and excerpts from other books will be distributed to the class.

Neuroethics: Neuroscience, Ethics & Society

In the last three decades, we have witnessed tremendous progress in our ability to measure and understand the functioning of the human brain. As any scientific or technological emerging discipline, neuroscience has several ethical consequences. As many experts would agree, neuroscience is not a mature discipline yet (when compared to physics, or even psychology) so one would expect to encounter lots of philosophical and methodological issues specific to a “science in the making.”

One such issue is the relationship between neuroscience and ethics and the bipartite nature of ethical investigation into neuroscience. One aspect, dominant in the literature is the ethical implications of the practice and advancement of neuroscience. Some people identify neuroethics with the ‘ethics of neuroscience’ and take it as an area of applied ethics to the practice of science: it studies ethical issues in the sciences of the mind, particularly neuroscience, which is the research of the brain.

Some researchers (Adina Roskies i.a.) prefer to extend the scope of neuroethics from the ‘ethics of neuroscience’ to encompass ‘the neuroscience of ethics’, thus suggesting an extension of the scope of neuroethics to help us understanding the biological basis of ethical reasoning and behavior, and the ways in which this could itself influence and inform our ethical thinking. This second aspect of neuroscience is properly the representation of moral and ethical judgements in neuroscience. Neuroscience helps us understand better the underlying mechanisms of moral reasoning and moral cognition.

This course introduces students to the field of Neuroethics as a bipartite discipline and create a forum for discussion and debate about a range of timely topics. We plan to cover both the neuroscience of ethics and the ethics of neuroscience.

The first module (I) is the ‘ethics of neuroscience’, which deals with the moral issues that arise from imaging neuroscience. The second module (II) is the ‘neuroscience of ethics’, which applies research on the brain to morality (e.g. what drives moral judgment and behavior).

Module I: Ethics of Neuroscience (or the ethical implications of the practice of neuroscience)

Units:

• Memory & Identity

• Mind Reading

• Enhancement

• Manipulating the Mind

Module II: Neuroscience of Ethics (or the cognitive basis of morality)

Units:

• Moral responsibility

• Free will: Does unconscious neural activity determine our behavior prior to conscious awareness? Is, e.g., a psychopath morally responsible if the behavior is the result of brain dysfunction? Is addiction a neurological compulsion?

• Moral cognition: Which areas of the brain are involved in moral thought and action? (Emotional areas? Rational/cognitive areas?)

• Mind reading: Can neuroscientific technologies determine whether someone is lying? Can brain scan results constitute self-incrimination (thus violating the 5th amendment)?

• Moral enhancement: Is there something wrong with making oneself a better person (e.g. more caring and generous) by altering one’s brain directly (e.g. via pills or deep brain stimulation)?

Main textbook

• Farah, Martha J., ed. 2010. Neuroethics: An Introduction with Readings. 1 edition. Cambridge, Mass: MIT Press.

Other resources

• Bloom, Paul. 2018. Against Empathy: The Case for Rational Compassion. Reprint edition. New York: Ecco.

• Churchland, Patricia S. 2011. Braintrust: What Neuroscience Tells Us about Morality. Princeton: Princeton University Press.

• Clausen, Jens, and Neil Levy, eds. 2015. Handbook of Neuroethics. Springer Berlin Heidelberg.

• Greene, Joshua. 2013. Moral Tribes: Emotion, Reason, and the Gap Between Us and Them. New York: Penguin Press HC, The.

• Haidt, Jonathan. 2012. The Righteous Mind: Why Good People Are Divided by Politics and Religion. 1 edition. Vintage.

• Illes, Judy, ed. 2011. The Oxford Handbook of Neuroethics. Oxford, [England]: Oxford University Press.

• ——. 2006. Neuroethics: Defining the Issues in Theory, Practice, and Policy. Oxford University Press.

• Levy, Neil. 2007. Neuroethics: Challenges for the 21st Century. 1 edition. Cambridge, UK?; New York: Cambridge University Press.

Courses for general education, liberal arts curriculum, core curricula, etc.)

These courses are adapted from upper level courses but are more suitable as introductory classes for freshmen or sophomore students or for students who want to look at science and technology through a liberal arts lens.

‘The good’ and ‘the bad’ in science and technology

This course analyzes and assesses science in a conceptual and foundational framework as an interplay between values and facts. The main questions addressed are: what is the role of values in science, when compared to factual judgments? We start with a foundational distinction between the “is” of science (to include truth, realism, explanation, and prediction) and the “ought” of ethics (what is right and what is wrong about our actions, thoughts, ideas, etc.). The next question is related to trust: when, and why, is ethical to trust (or distrust) contemporary science or emerging technologies? In expanding the meaning of the word ‘trust,’ two fundamental aspects of the decision-making and framework-choices are addressed: ethics and epistemology. We pay special attention to the way false information and bad science proliferate, as well as to the erosion of trust in scientific and technological expertise.

Textbooks

• Goldacre, B. (2010). Bad Science: Quacks, Hacks, and Big Pharma Flacks. Farrar, Straus and Giroux.

• McIntyre, L. (2018). Post-Truth. Cambridge, MA: MIT Press.

• McIntyre, L., & Chamberlain, M. (2019). The Scientific Attitude: Defending Science from Denial, Fraud, and Pseudoscience. MA: MIT Press.

• O’Connor, C., & Weatherall, J. O. (2018). The Misinformation Age: How False Beliefs Spread., 1 edition. New Haven: Yale University Press.

Objectives

After the completion of this course, students should be able to:

• acquire critical thinking skills in approaching ethical problems raised by new disciplines in science and by some new technologies, in the broader cultural and social context;

• come to a better understanding of the nature of science and technology and the role of values therein;

• understand the “situational” and contingent aspects of scientific progress and the evolution of paradigms in science and technology, as well as the role of choice and thus our moral responsibility, when these changes happen;

• analyze sources of information about ethical matters in science, separate the theoretical from the practical, and research on their own moral matters in everyday life;

• identify different worldviews and understand their relationship with science;

• understand better ethical values and social norms in the contemporary world;

• gain a positive momentum needed for socially responsible development and overcome the destructive unreflective skepticism towards science and technology.

How science works: truth, facts and beliefs in science

This course highlights some major debates about the nature of science and introduces a host of philosophical aspects of the practice of science. We address several questions on the nature of the scientific inquiry, scientific results and reliability of scientific observations. We set off by discussing the scientific discovery and progress. Is scientific knowledge a linear progress or on the contrary the result of successive radical revolutions? Are scientists driven by logic, reason when they reject old ideas, and replace them with a novel theory? Is scientific justification special? Second, we discuss scientific evidence and observation. When is scientific evidence reliable and robust enough? What piece of information is relevant enough to become scientific evidence? Third we will introduce the problem of truth and facts in the framework of scientific realism. Are scientific theories accurate accounts of the world or should we regard even the most elaborate and well-confirmed theory merely as a useful tool to systematize our experience? Fourth, we discuss the impact society and psychology has on the scientific inquiry. Do norms play any role in the advancement of science? Are sociological facts essential to the form science takes in a given society? What role do social factors play in the advancement of science? What means equity and justice in respect of scientific communities?

Textbooks

• French, S. (2016). Science: key concepts. London: Bloomsbury Press.

• Ladyman, J. (2002). Understanding philosophy of science. London?; New York: Routledge.

Objectives

• After completing this course, you should be able to:

• critically assess a range of important issues which all stem from a central question: “How does science work?”;
• develop reading skills of scientific literature and assess it from a broader perspective;
• integrate foundational interpretations of the nature of scientific method into a liberal arts curriculum;
• assess the relation among sciences and the relation between science and technology;
• understand better the role of values and social norms in science;
• understand the methodological, social and theoretical dimensions of the scientific
• enterprise both from epistemological and metaphysical perspectives;
• analyze critically some scientific theories in physics, biology, social sciences and/or
• cognitive sciences based on concrete examples discussed in this course;
• relate the general discussion to specific scientific theories or models or to specific episodes