HAI-GEN 2021: 2nd Workshop on Human-AI Co-Creation with Generative Models

Geyer, Werner; Chilton, Lydia B.; Weisz, Justin D.; Maher, Mary Lou

doi:10.1145/3397482.3450707

Cited by 8 publications

(4 citation statements)

References 1 publication

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“…We developed seven design principles for generative AI applications based on recent research in the HCI and AI communities, specifically around human-AI co-creative processes. We conducted a literature review of research studies, guidelines, and analytic frameworks from these communities [1,5,23,27,39,48,57,65,68,69,81,82,96,104], which included experiments in human-AI co-creation [2,3,55,64,106,115,116], examinations of representative generative applications [11,50,51,64,72,87,90,92], and a review of publications in recent workshops [35,77,78,114].…”

Section: Design Principles For Generative Ai Applicationsmentioning

confidence: 99%

Toward General Design Principles for Generative AI Applications

Weisz¹,

Müller²,

He³

et al. 2023

Preprint

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Fig. 1. Seven principles for the design of generative AI systems. Six of these principles are presented in overlapping circles, indicating their relationships to each other. One principle stands alone, the directive to design against potential harms that may be caused by a generative model's output, misuse, or other harmful effects. These principles are bounded in an environment of generative variability, in which the outputs of a generative AI application may vary in quantity, quality, character, or other characteristics.Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Based on recent research on human-AI co-creation within the HCI and AI communities, we present a set of seven principles for the design of generative AI applications. These principles are grounded in an environment of generative variability. Six principles are focused on designing for characteristics of generative AI: multiple outcomes & imperfection; exploration & control; and mental models & explanations. In addition, we urge designers to design against potential harms that may be caused by a generative model's hazardous output, misuse, or potential for human displacement. We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.CCS Concepts: • Human-centered computing → HCI design and evaluation methods; Interaction paradigms; HCI theory, concepts and models.

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Section: Design Principles For Generative Ai Applicationsmentioning

confidence: 99%

Toward General Design Principles for Generative AI Applications

Weisz¹,

Müller²,

He³

et al. 2023

Preprint

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show abstract

“…The term Human-centered AI has emerged in many academic works and public discussions [22,25,45,65,85,89]. While definitions vary, human-centered approaches to AI aim to develop AI systems that serve the needs, improve the conditions, and align with the values of human stakeholders.…”

Section: Human-centered Approaches To Aimentioning

confidence: 99%

“…This question of what do users need to understand about AI systems is core to the nascent field of Human-Centered Explainable AI (HCXAI) [21,22,52], which is a subset of the fields of human centered AI and human centered data science [6,7,25,41,[65][66][67]. Our work is informed by a few key lessons from recent work in HCXAI, mostly conducted in the context of discriminative ML (e.g., for decision-support systems).…”

Section: Introductionmentioning

confidence: 99%

Investigating Explainability of Generative AI for Code through Scenario-based Design

Sun

Liao²,

Müller

et al. 2022

27th International Conference on Intelligent User Interfaces

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What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code autocompletion. We conducted 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Drawing from prior work, we also propose 4 types of XAI features for GenAI for code and gathered additional design ideas from participants. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains. CCS CONCEPTS• Computing methodologies → Natural language generation; • Software and its engineering; • Human-centered computing → Human computer interaction (HCI); User studies;

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“…Recent advances in AI, especially in deep generative models, have renewed interested in how AI can support mixedinitiative creative interfaces [16] to fuel human-AI cocreation [28]. Mixed initiative [33] means designing interfaces where a human and an AI system can each "take the initiative" in making decisions.…”

Section: Related Workmentioning

confidence: 99%

AI Song Contest: Human-AI Co-Creation in Songwriting

Huang¹,

Koops²,

Newton-Rex³

et al. 2020

Preprint

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Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges. Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the "flare and focus" aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs. These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression.

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HAI-GEN 2021: 2nd Workshop on Human-AI Co-Creation with Generative Models

Cited by 8 publications

References 1 publication

Toward General Design Principles for Generative AI Applications

Toward General Design Principles for Generative AI Applications

Investigating Explainability of Generative AI for Code through Scenario-based Design

AI Song Contest: Human-AI Co-Creation in Songwriting

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