A Design Perspective on Data

Feinberg, Melanie

doi:10.1145/3025453.3025837

Cited by 87 publications

(64 citation statements)

References 27 publications

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“…Feinberg described the work of data science as the design of data [14] (see also Seidelin's account of data as a design material [60]), and Kiss and Sziranyi noted that datasets in some fields are "commonly handcrafted" [38]. Patel et al explored analogous human-constructive activities during feature engineering [55].…”

Section: Data Science Teams and Disciplinary Diversitymentioning

confidence: 99%

Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

198

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The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

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Section: Data Science Teams and Disciplinary Diversitymentioning

confidence: 99%

Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

198

View full text Add to dashboard Cite

show abstract

“…Under digital data, without aiming for a comprehensive list, we refer to quantitative data, such as data in databases, sensor data or open data. While HCI has a long tradition of discussing various aspects of digital data (such as information visualization (Card, Mackinlay and Shneiderman, 1999), knowledge discovery and information retrieval (e.g., (Fayyad, Piatetsky-Shapiro and Smyth, 1996;Marchionini, 2006;Dörk, Carpendale and Williamson, 2011)), or users" engagement with personal data (Mortier et al, 2014)), using digital data in the HCI design process to enquire about different phenomena is a more recent, and still emerging trend (e.g., (Speed and Oberlander, 2016;Bogers et al, 2016;Giaccardi et al, 2016;Feinberg, 2017)).…”

Section: Introductionmentioning

confidence: 99%

Design Enquiry Through Data: Appropriating a Data Science Workflow for the Design Process

Kun

Mulder

Kortuem

2018

Electronic Workshops in Computing

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The recent developments in data science and end-user data tools indicate an opportunity for designers to adapt new data tools for design enquiry. Data has an unquestionable role in the future of the design practice for creating new digital products and services. Today's data deluge also opens up new ways of enquiring about the world through data. The current work explores how designers could appropriate a data science workflow in their design research process. Two studies are conducted to explore how a data science workflow could be adapted into a design research process. We present how the participants appropriated data techniques for creative uses and how they synthesized a data-centric enquiry into their research process. We found that designers appropriate data using their creative capacities in hypothesis forming for data collection and exploratory data analysis, and we highlight some implications of this. Our findings can inform the design space of the creativity support of future data tools and future data-centric design methods.

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“…However, in the course of data science work, raw data are transformed and engineered into features. Therefore, data scientists' relative lack of interest in the design of these features [10,44] should be examined in future research.…”

Section: Information Needs For Establishing Trustmentioning

confidence: 99%

Trust in AutoML

Drozdal

Weisz²,

Wang³

et al. 2020

Proceedings of the 25th International Conference on Intelligent User Interfaces

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We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies -qualitative interviews, a controlled experiment, and a card-sorting task -to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool. CCS CONCEPTS• Human-centered computing → User studies; Empirical studies in HCI ; • Computing methodologies → Artificial intelligence.

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A Design Perspective on Data

Cited by 87 publications

References 27 publications

Human-AI Collaboration in Data Science

Human-AI Collaboration in Data Science

Design Enquiry Through Data: Appropriating a Data Science Workflow for the Design Process

Trust in AutoML

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