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Design Indirections

How Designers Find Their Ways in Shaping Algorithmic Systems

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Abstract

Digital products and services now commonly include algorithmic personalization or recommendation features. This has raised concerns of reduced user agency and their unequal treatment. Previous research hence called for increasing the participation of, among others, designers in the development of these features. To achieve this, researchers have suggested the development of better educational material and tools to enable prototyping with data and machine learning models. However, previous studies also suggest designers may find other ways to impact the development and implementation of such features, for instance through collaboration with data scientists. We build on that line of inquiry, through 19 in-depth interviews with designers working in small to large international companies to investigate how they actually intervene in shaping products including algorithmic features. We outline how designers intervene at different levels of the algorithmic systems: at a technical level, for instance by providing better input data ; at an interface or information architecture level, sometimes circumventing algorithmic discussions ; or at a organizational level, re-centering the outcome of algorithmic systems around product-centric questions. Building upon these results, we discuss how supporting designers engagement and influence on algorithmic systems may not only be a problem of technical literacy and adequate tooling. But that it may also involve a better awareness of the power of interface work, and a stronger negotiation skills and power literacy to engage in strategic discussions.

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Notes

  1. See e.g., https://dschool.stanford.edu/resources/i-love-algorithms

  2. https://www.aixdesign.co/shop

  3. https://www.imagination.ooo/project/ai-cards

  4. https://www.trytriggers.com/

  5. https://dschool.stanford.edu/resources/i-love-algorithms

  6. One of these informants, now working as an HCI researcher, had worked in the past on a recommender system and showed interest in the project so they were invited to join the project as a co-author of this paper.

  7. www.taguette.org

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Acknowledgements

We are very grateful to all the interviewees for their time, insights and candidness without which this work would not have existed. We would like to thank the reviewers and Camille Roth for their feedback on how to improve the manuscript. One author is part of LabEx ASLAN - Laboratoire d’excellence ASLAN. This work has been published under the framework of the IdEx University of Strasbourg.

Author information

Authors and Affiliations

  1. Centre Marc Bloch, CNRS, Berlin, Germany

    Jérémie Poiroux

  2. Université de Strasbourg, Strasbourg, France

    Nolwenn Maudet

  3. École de design Nantes Atlantique, Nantes, France

    Karl Pineau

  4. University of Sussex, Brighton, England

    Emeline Brulé

  5. Univ Lyon, UCBL, CNRS, INSA Lyon, Centrale Lyon, Univ Lyon 2, LIRIS, UMR5205, F-69622, Villeurbanne, France

    Aurélien Tabard

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  1. Jérémie Poiroux
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Correspondence to Jérémie Poiroux.

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Appendices

Appendix A: A Note on Our Coding Method

While we mainly relied on thematic analysis to analyze our data, we also used Activity Theory in preliminary stages. As we were familiarizing with the data, we became interested in the conflicts described by designers and temporarily turned towards Engeström’s activity system model (Engeström, 2001; Kaptelinin and Nardi, 2006). His model identifies conflicts within an organization as a motor of changes (and hence designers’ impact) and provided a structured framework to compare informants’ highly varied accounts. Early on, our codes reflected our work with activity system model categories (Engeström, 2001), taking a deductive approach. For instance, we had codes related to ‘dividing labor’ to describe how the informant presented situations in which work of implementing a new feature was attributed. However, we found this approach difficult to implement due to the lack of multiple perspectives within each company that could better enable to track internal conflicts and how they led to change. This led us to go back to a more inductive approach following the thematic analysis described in the method section.

Appendix B: Interview Guide

  1. 1.

    Factual questions to set the framework, about the organization, the personal situation within the company and the team

  2. 2.

    Overview of the algorithms What is a recommendation algorithm in your company? What algorithms (recommendation, filtering, search, classification) exist within your product?

  3. 3.

    Which ones do you use, which ones do you have ‘contact’ with, which ones do you think you have an impact on?

  4. 4.

    Are there any algorithms you are not working on? Why not?

  5. 5.

    Have you participated in the creation/implementation of a new algorithm? How and with whom?

  6. 6.

    How did you become aware of this algorithm (code, via discussion with the developers, etc.)?

  7. 7.

    Have you, at any time, seen problems emerge with one of these algorithms (or a product using an algorithm)?

  8. 8.

    If so, in this particular example, what exactly was the issue?

  9. 9.

    How did you become aware of this problem (tests with users, external audit, personal awareness, etc.)?

  10. 10.

    Do you know what the background of this algorithm was? Who implemented it and why?

  11. 11.

    What is your technical or logical knowledge of this algorithm?

  12. 12.

    What was your reaction? Were you able to solve this problem? If so, how, and if not, why?

  13. 13.

    Did you work with somebody to solve this problem and if so, with whom? What is your relationship with the engineers? Are you in regular contact?

  14. 14.

    Did this event changed the way you test algorithms now? On the way you work with algorithms?

  15. 15.

    If not, how do you make sure the algorithms work the way you want them to?

  16. 16.

    Do you conduct tests? If so, can you tell us more about their implementation?

  17. 17.

    Who are your users? Do you mobilize data, statistics, etc.? What is your relationship with the users?

  18. 18.

    General questions to conclude the interview Does this type of ‘algorithmic’ project reflect the progress of other projects you may have been working on recently? To what extent? In your opinion, how much control should designers have over algorithms? What is the specificity of their contribution (compared to that of marketing for example)? What space do you give to your values and personal ethics in your design work? And what place do you give to the values of the users?

  19. 19.

    Personal questions about the organization of the team, the methods of intervention on projects, the background, the experience in design and in the organization, the familiarity with programming.

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Poiroux, J., Maudet, N., Pineau, K. et al. Design Indirections. Comput Supported Coop Work (2023). https://doi.org/10.1007/s10606-022-09459-y

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