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

Eiband, Malin; Buschek, Daniel; Hußmann, Heinrich

doi:10.1145/3397481.3450694

Cited by 24 publications

(10 citation statements)

References 86 publications

(219 reference statements)

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“…However, it is essential to understand the AI partner by the users to build an engaging and trustworthy partnership. Many intelligent systems lack the core interaction design principles such as transparency and explainability and it makes them hard to understand and use [49]. To address the challenge of transparency of AI interaction should be designed to support users in understanding and dealing with intelligent systems despite their complex black-box nature.…”

Section: Discussionmentioning

confidence: 99%

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

Rezwana,

Maher

2022

Preprint

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Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process.Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.CCS Concepts: • Human-centered computing → Interaction design process and methods; Collaborative interaction.

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Section: Discussionmentioning

confidence: 99%

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

Rezwana,

Maher

2022

Preprint

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“…However, it is insufficient for in-depth understanding of the underlying dimensions. Using the terms of recent work on understanding of intelligent systems [9], previews mainly facilitated interaction knowledge: Users learned how to use the system effectively -without necessarily being able to explicitly explain it. Here, text labels may help yet require an interpretable model.…”

Section: Discovering Interpretations Of the Control Dimensionsmentioning

confidence: 99%

GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information

Dang,

Mecke,

Buschek

2022

Preprint

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Solution Target b a Example: User exploration path (Not part of UI) StartFigure 1: This paper compares different user interfaces with sliders for interactive control of a generative model for face images (StyleGAN2). We study different numbers of sliders and two designs: (a) Regular sliders and (b) Filmstrip sliders that provide feedforward information via preview images. Participants used these UIs in image reconstruction tasks to generate a given target images. The right part of the figure (c) shows one example of a resulting "exploration path" towards a target (for more examples see Appendix A).

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“…It is important to note that different issues and considerations arise from varying concerns. For instance, some examples include how it is important to ensure the AI system is not difficult to use or explain to users, difficult to manage or maintain, or perceived as creepy by the potential users (as noted above) [29,131]. Additionally, the systems will need awareness and capabilities supporting numerous types of intelligence such as social, emotional, physical, etc.…”

Section: Orthogonal Hcai Conceptsmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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

Cited by 24 publications

References 86 publications

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information

Modeling Human Behavior Part I -- Learning and Belief Approaches

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