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Week 4

In this CoDesign study we will together use a design probe and explore the area of Machine Teaching (MT)1 in a commuting context.

Goal for the coming week

During this week you will use your understanding of how the app and the teaching works. Your task will be to use that understanding and give me input on how the teaching should be integrated in the app for this context. You will also be asked to reflect over how the privacy a machine teaching approach can provide should be communicated to the user in this implementation.

Therefor we could get insights in how to answer research questions regarding privacy formulated like these:

RQ1: How does the fact that user defines the borders regarding the knowledge transferred to the machine learning model affect the feeling of privacy the user experience?

RQ2: How does teach as you go and batch teaching affect the experience of privacy?

RQ3: How can or should a privacy aspect be communicated to the user over time and instantly.

Final on teaching UI: Sketches on teaching UI that I can implement week 5!

Fourth week usage: "ASSIGNMENT"

  • Use the app freely, teach, clear add more or less data and play around.
  • Reflect over privacy in the research questions
  • Think/sketch on how an teaching UI for this context could be integrated in the app.

Background info: Privacy.

Machine Teaching can be a way to build systems that provides privacy-by-design since you as a user define what to teach the system. Theoretically this means that a user or group of users can teach a model only the things he or she select and if the system is designed for privacy and security the user decides if any information should be shared. During this week we will discuss those issues, some related articles are attached23

Feedback

Add feedback here both general issues, thoughts and sketches or just notes so you remember, the more the better. Especially things that relates to the research questions above is valuable:

https://github.com/k3larra/commuter/issues

This page:

https://github.com/k3larra/commuter/blob/master/UserStudy/week4/presentation.md

References

[1] P. Y. Simard et al., “Machine Teaching: A New Paradigm for Building Machine Learning Systems,” 2017.
[2] M. Ananny and K. Crawford, “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability.”
[3] N. Boukhelifa, A. Bezerianos, and E. Lutton, “Evaluation of Interactive Machine Learning Systems,” ArXiv, pp. 1–20, 2018.