How Experienced Designers of Enterprise Applications Engage AI as a Design Material

Yildirim, Nur; Kass, Alex; Tung, Teresa; Upton, Connor; Costello, Donnacha; Giusti, Robert; Lacin, Sinem; Lovic, Sara; O'Neill, James M; Meehan, R. O’Reilly; Loideáin, Eoin Ó; Pini, Azzurra; Corcoran, Medb; Hayes, Jeremiah F.; Cahalane, Diarmuid J; Shivhare, Gaurav; Castoro, Luigi; Caruso, Giovanni; Oh, Changhoon; McCann, James; Forlizzi, Jodi; Zimmerman, John

doi:10.1145/3491102.3517491

Cited by 35 publications

(27 citation statements)

References 68 publications

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“…From an interaction design perspective, AI presents unique challenges for ideating and rapid prototyping novel, implementable AI experiences [24,90,104] (see [104] for a review of AI's design challenges). Designers mostly join projects towards the end for communicating the AI system's output to end users, and are rarely involved in problem formulation [21,24,105].…”

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

“…In addition to challenges specific for individual roles, there are many challenges in cross-disciplinary collaboration, mainly due to a lack of shared workflow, shared language, and shared expertise [31,50,78]. Some researchers characterized this as a gap between AI and product expertise [102,105]: data science and AI teams struggle to elicit user requirements from product teams, and they tend to overlook how the AI system will generate value for users [54,72,78]. Product teams (e.g., PMs, designers) tend to envision AI concepts that cannot be built or too high stakes to deploy an imperfect AI model [24,104].…”

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

“…A small but growing number of studies explored the emergent best practices for designing AI products. Successful practitioners formed close collaborations between data science and product teams [54,90,103,105], where product teams worked to sensitize data science and AI partners to human-centered approaches [107]. Design teams in particular served as facilitators on AI teams to establish a shared understanding and alignment between different disciplines using interim artifacts and visualizations [105,107].…”

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

“…Successful practitioners formed close collaborations between data science and product teams [54,90,103,105], where product teams worked to sensitize data science and AI partners to human-centered approaches [107]. Design teams in particular served as facilitators on AI teams to establish a shared understanding and alignment between different disciplines using interim artifacts and visualizations [105,107]. Several researchers highlighted that such artifacts and boundary objects [56,88] might play a key role in scaffolding cross-disciplinary collaboration among AI practitioners and stakeholders [15,90,102,105].…”

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

“…Design teams in particular served as facilitators on AI teams to establish a shared understanding and alignment between different disciplines using interim artifacts and visualizations [105,107]. Several researchers highlighted that such artifacts and boundary objects [56,88] might play a key role in scaffolding cross-disciplinary collaboration among AI practitioners and stakeholders [15,90,102,105].…”

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

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Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook

Yildirim¹,

Pushkarna²,

Goyal³

et al. 2023

Preprint

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Artificial intelligence (AI) presents new challenges for the user experience (UX) of products and services. Recently, practitioner-facing resources and design guidelines have become available to ease some of these challenges. However, little research has investigated if and how these guidelines are used, and how they impact practice. In this paper, we investigated how industry practitioners use the People + AI Guidebook. We conducted interviews with 31 practitioners (i.e., designers, product managers) to understand how they use human-AI guidelines when designing AI-enabled products. Our findings revealed that practitioners use the guidebook not only for addressing AI's design challenges, but also for education, crossfunctional communication, and for developing internal resources. We uncovered that practitioners desire more support for early phase ideation and problem formulation to avoid AI product failures. We discuss the implications for future resources aiming to help practitioners in designing AI products. CCS CONCEPTS• Human-centered computing → Interaction design process and methods.

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Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

Section: Related Work 21 Challenges Of Designing Ai Productsmentioning

confidence: 99%

See 3 more Smart Citations

Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook

Yildirim¹,

Pushkarna²,

Goyal³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Grasping AI: experiential exercises for designers

Murray-Rust,

Lupetti,

Nicenboim

et al. 2023

AI & Soc

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Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into the functioning of physical and digital products, creating unprecedented opportunities for interaction and functionality. However, there is a challenge for designers to ideate within this creative landscape, balancing the possibilities of technology with human interactional concerns. We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We introduced into an interaction design course (n = 100) nine ‘AI exercises’ that draw on more than human design, responsible AI, and speculative enactment to create experiential engagements around AI interaction design. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible, and thereby help students be more reflective and responsible on how to design with AI and its complex properties in both their design process and outcomes.

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When and How to Use AI in the Design Process? Implications for Human-AI Design Collaboration

Lee,

Law,

Hoffman

2024

International Journal of Human–Computer Interaction

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How Experienced Designers of Enterprise Applications Engage AI as a Design Material

Cited by 35 publications

References 68 publications

Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook

Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook

Grasping AI: experiential exercises for designers

When and How to Use AI in the Design Process? Implications for Human-AI Design Collaboration

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