Reading Week

Overview

Important

This week’s learning objectives are:

  1. Get caught up on any missed reading(s).
  2. Start to work on the first few questions of the group assessment.
  3. Start to test your ability to work with the Inside Airbnb data (Week 5 Practical).

Past student performance strongly suggests that this is a good week to:

  1. Catch up on readings, particularly the more critical ones and the ones focussing on the impact of Airbnb.
  2. Go back over the first five notebooks in order to self-test and check your understanding. This doesn’t mean re-doing the full notebook, but (for example) seeing if you can use pandas to analyse the simpler data we looked at in weeks 3 and 4.

Looking ahead to the Group Work, I’d also strongly suggest that you browse the Full Reference List for ideas. The bibliography is a working document and I will add more items as and when I come across them or new works are published, but this is a good time to start reading about the ethical and practical issues arising from Airbnb’s operations and the data to which we have access.

Readings

For the group work you will also want to consider the following articles and outputs:

Connections

I am not suggesting that you read every one of these readings (though split across a group this is light work!); rather, I am pointing you towards content that can inform your responses to the set questions in the Group Work. These readings focus on the ethical issues raised by spatial data science and algorithmic approaches to decision-making. You should be drawing connections to D’Ignazio and Klein (2020) and their data-informed critiques of ‘just let the data scientists crunch the data’ thinking. Some of these readings may feel very challenging in terms of their language or approach to Machine Learning/AI, but they will reward attention and reflection, and you can expect to see this reflected in a better mark on the group project as your engagement with the substance of the assesmsnet will also be more reflective and relevant.

Additional Context

You might also find the following content interesting in terms of the practical limitations of ‘AI’ tools and the ways in which they reproduce errors in our own thinking rather that offering a neutral insight into processes:

And here’s a nice example of why it’s not about the algorithm:

Screenshot of an IEEE piece from the 1980s that I'm unable to access any other way

Algorithmic Perfection

Source: Zemanek (1983)

How I’m fighting bias in algorithms

Ethics, Politics & Data-driven Research and Technology

What constitutes dataset bias? (and what can we do about it?)

Does debiasing word embeddings actually work? (+ explanation of GN-GloVe, Hard-debiasing)

References

Amoore, L. 2019. “Doubt and the Algorithm: On the Partial Accounts of Machine Learning.” Theory, Culture, Society 36 (6):147–69. https://doi.org/10.1177%2F0263276419851846.
Bemt, V. van den, J. Doornbos, L. Meijering, M. Plegt, and N. Theunissen. 2018. “Teaching Ethics When Working with Geocoded Data: A Novel Experiential Learning Approach.” Journal of Geography in Higher Education 42 (2):293–310. https://doi.org/10.1080/03098265.2018.1436534.
Crawford, K., and M. Finn. 2015. “The Limits of Crisis Data: Analytical and Ethical Challenges of Using Social and Mobile Data to Understand Disasters.” GeoJournal 80 (4):491–502. https://doi.org/10.1007/s10708-014-9597-z.
D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. MIT Press. https://bookbook.pubpub.org/data-feminism.
Elwood, S., and A. Leszczynski. 2018. “Feminist Digital Geographies.” Gender, Place and Culture 25 (5):629–44. https://doi.org/10.1080/0966369X.2018.1465396.
Mattern, Shannon. 2015. “Mission Control: A History of the Urban Dashboard.” Places Journal.
———. 2020. “A City Is Not a Computer.” In The Routledge Companion to Smart Cities, 17–28. Routledge.
Zemanek, H. 1983. “Algorithmic Perfection.” Annals of the History of Computing. AMER FED INFORM PROCESSING SOC 1899 PRESTON WHITE DR, RESTON, VA 22091.