Towards Algorithmic Experience

Alvarado, Oscar; Wærn, Annika

doi:10.1145/3173574.3173860

Cited by 82 publications

(38 citation statements)

References 23 publications

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“…Such systems should expose the probabilities computed by the machine learning systems. With this, we extend on recent research that studies algorithmic experience and awareness [2,9,15]. Our results also connect to Eslami et al 's finding that users can detect algorithmic bias during their regular usage of a service [10].…”

Section: Discussionsupporting

confidence: 87%

More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition

Heuer

Breiter

2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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This paper investigates the user experience of visualizations of a machine learning (ML) system that recognizes objects in images. This is important since even good systems can fail in unexpected ways as misclassifications on photo-sharing websites showed. In our study, we exposed users with a background in ML to three visualizations of three systems with different levels of accuracy. In interviews, we explored how the visualization helped users assess the accuracy of systems in use and how the visualization and the accuracy of the system affected trust and reliance. We found that participants do not only focus on accuracy when assessing ML systems. They also take the perceived plausibility and severity of misclassification into account and prefer seeing the probability of predictions. Semantically plausible errors are judged as less severe than errors that are implausible, which means that system accuracy could be communicated through the types of errors.

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Section: Discussionsupporting

confidence: 87%

More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition

Heuer

Breiter

2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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“…Diakopoulos presents a call for algorithmic accountability and propose an algorithmic transparency standard [11]. Other academics highlight the importance of a human-centered design of algorithmic systems [3,28] or adhering to a design framework for algorithmic experience (AX) [1,33] in the area of social media platforms.…”

Section: Introductionmentioning

confidence: 99%

“…Building on the AX framework [1] for social media, we adapt the framework for movie recommender algorithms by expanding it with two new design areas: algorithmic usefulness and algorithmic social practices. This specialized framework enriches the present debate on AX and recommender algorithms, enables refined design guidelines, and promotes positive user experiences with movie recommender algorithms.…”

Section: Introductionmentioning

confidence: 99%

“I Really Don’t Know What ‘Thumbs Up’ Means”: Algorithmic Experience in Movie Recommender Algorithms

Alvarado

Abeele

Geerts

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Many of our daily activities and decisions are driven by algorithms. This is particularly evident in our interactions with contemporary cultural content, where recommender algorithms deal with most of their access, production, and distribution. In this context, the Algorithmic Experience (AX) design framework emerged to guide the design of users' experiences with algorithms for social media platforms. However, thus far, a framework to design specifically for AX within the context of movie recommender algorithms was lacking. To this end, the present study combines a semiotic inspection analysis of the Netflix interface with sensitized design workshops and semi-structured interviews to explore AX requirements for movie recommender algorithms. Linking the analysis with design opportunities, we shed light on AX suited for movie recommender systems. Moreover, we extend the current AX design framework with two new design areas: algorithmic usefulness and algorithmic social practices.

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“…Separate profile page enables users to edit what the system knows about them [55] "Algorithmic profiling management": Profile page reveals what system knows and how this influences content, including past interactions; controls enable modifications [5].…”

Section: Activementioning

confidence: 99%

How to Support Users in Understanding Intelligent Systems? Structuring the Discussion

Eiband

Buschek

Hußmann

2021

26th International Conference on Intelligent User Interfaces

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The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for humancomputer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear.We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We thus hope to advance and structure the dialogue in the HCI research community on supporting users in understanding intelligent systems. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models.

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Towards Algorithmic Experience

Cited by 82 publications

References 23 publications

More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition

More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition

“I Really Don’t Know What ‘Thumbs Up’ Means”: Algorithmic Experience in Movie Recommender Algorithms

How to Support Users in Understanding Intelligent Systems? Structuring the Discussion

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