🗓️ Week 10 - Prediction vs Explanation

2023/24 Autumn Term

Author

Published

27 November 2023

This week, we won’t be using slides during the lecture and will instead be reading and discussing academic articles. Click on the Lecture Material to check out the list of articles.

The main question we would be asking ourselves is: what should be the role of modelling (be it statistical, machine learning, simulation, etc.) in the social sciences? Should we aim for explanations, make predictions, or a mix of both? Or is it time we drop the “should”s?

👨‍🏫 Lecture Material

Here is the plan we will follow this Monday:

A recap of data science terminology (~30 min)

Do you remember all the terms we have explored so far in this course?

Literature review (~70 min)

Then we will go through the literature on the role of modelling in the social sciences. We will read and discuss the following papers:

Hofman, Jake M., Amit Sharma, and Duncan J. Watts. 2017. “Prediction and Explanation in Social Systems.” Science 355 (6324): 486–88.
Rettberg, Jill Walker. 2022. “Algorithmic Failure as a Humanities Methodology: Machine Learning’s Mispredictions Identify Rich Cases for Qualitative Analysis.” Big Data & Society 9 (2): 205395172211312.
Hullman, Jessica, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, and Arvind Narayanan. 2022. “The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning.” In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 335–48. Oxford United Kingdom: ACM.
Verhagen, Mark D. 2022. “A Pragmatist’s Guide to Using Prediction in the Social Sciences.” Socius: Sociological Research for a Dynamic World 8 (January): 237802312210817.

Our guiding questions for each of the readings will be as follows:

What is the main idea of the article?
What are the main takeaways?
What are the main implications of the article?

Online and AI tools for modern literature review (~20 min)

I will show you how to use the following tools to conduct a literature review:

*Original tweet: https://twitter.com/Artifexx/status/1632277025472888833*

🎥 Looking for lecture recordings? You can only find those on Moodle.

📚 Recommended Reading

Check the 📔 Syllabus for this week’s complete list of indicative and recommended readings.

✍️ Coursework to prepare for next week’s lecture

Within your respective assigned groups, find 2-3 examples of AI ethical issues (you can look at newspapers or academic papers for sources of examples.)

📟 Communication

Post your reflections, questions, and links on Slack.
Book office hours if you want to discuss your coursework with me.