Studying at York
Ethics and Epistemology of Digital Methods in Science - PHI00105M
- Department: Philosophy
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2024-25
Module summary
The module will introduce some of the central issues in contemporary philosophical discussions of digital methods in science, including the use of computer simulations, computational models, digital visualization methods, experiments designed, run, and interpreted using AI. To fully understand these issues, background knowledge will be introduced from philosophy of science on the nature and varieties of scientific models and modelling practices, model abstractions and idealisations, explanation, scientific imagination, trust, consensus, objectivity, and feminist philosophy of science.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 2 2024-25 |
Module aims
To explore some central ongoing debates about digital methods in science based on philosophical analysis and paradigmatic case studies.
To develop some key skills, including
– to work your way to an understanding of challenging philosophical puzzles, views, and arguments in an autonomous way, showing critical awareness and command of the material;
– to discuss complex and difficult conceptual problems with others, working together to develop understanding and critique and evaluate theories;
– to evaluate views and arguments methodically and in detail;
– to develop your own view on a question—based on and informed by a strong understanding of contributions to the debate—and then assemble a detailed reasoned case for that view;
– to undertake independent research reading;
– to find your way through a range of connected debates, making connections between them and developing those connections to gain a deeper understanding of the debates and create better arguments.
Module learning outcomes
By the end of this module students should be able to
- understand and explain a range of key problems, issues, and debates in philosophy of science concerning the importation of digital methods and express this understanding in clear, precise, and accessible terms;
- develop and articulate ranges of alternative solutions to specific problems and issues in an open-minded way, drawing on module materials;
- develop and articulate arguments for the alternative solutions considered in relation to specific problems and issues, drawing on module materials, identifying some points of weakness and some potential points for development;
- make a judgement about what is the best view on a particular problem and argue in defence of this judgement;
- apply simple strategies for improving your work, based on critical reflection and feedback.
Module content
There are no formal prerequisites for taking the module, but knowledge of at least some science (social or natural) and of some basic philosophy of science will be helpful. A number of recent monographs have been published over the past couple of years might be helpful, including Frigg and Nguyen’s Modelling Nature: An Opinionated Introduction to Scientific Representation (Springer 2020), The Scientific Imagination, edited by Arnon Levy and Peter Godfrey-Smith (Oxford University Press 2020), and Calculated Surprises by Johannes Lenhard (Oxford University Press 2019).
A number of useful encyclopaedia entries on issues in philosophy of science are available in the excellent Routledge Encyclopaedia of Philosophy, available online.
The Stanford Encyclopedia of Philosophy, which is also available online, contains a number of extremely detailed articles on a variety of topics, including Frigg and Hartmann’s “Models in Science” and Eric Winsberg's "Computer Simulations in Science".
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
All feedback will be returned in line with University and Departmental policy.
Indicative reading
- Michael Weisberg (2007) “Who is a modeller?” The British Journal for the Philosophy of Science: 207-233
- Natalia Carrillo and Tarja Knuuttila (2021) “An artefactual perspective on idealization: Galvanic cells and electric circuits in nerve signal research.” In: Alejandro Cassini y Juan Redmond (ed.) Models and Idealizations in Science: Fictional and Artefactual Approaches (Cham: Springer).
- Charles M. Wharton and Jordan Grafman (1998) “Cognitive and AI models of reasoning,” Trends in Cognitive Sciences 2 2): 54-59.
- Andrea Ferrario, Michele Loi & Eleonora Viganò (2020) “In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions,” Philosophy and Technology 33 (3): 523-539.
- Mariarosaria Taddeo (2010) Modelling Trust in Artificial Agents, A First Step Toward the Analysis of e-Trust, Minds and Machines 20 (2):243-257.
- Yan Teng (2021) “Towards trustworthy blockchains: normative reflections on blockchain-enabled virtual institutions.” Ethics and Information Technology 23 (3):385-397.