Mining Administrative Data to Spur Urban Revitalization

Green, Ben; Caro, Alejandra; Conway, Matthew; Manduca, Robert; Plagge, T.; Miller, Abby

doi:10.1145/2783258.2788568

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Our informants expressed much skepticism about AutoAI ever being able to automatically infer this kind of domain knowledge without human input. This finding echoes the conclusions of Green et al in which the application of local knowledge ought to be applied before drawing conclusions [22].…”

Section: The Augmented Vs Automated Data Scientistsupporting

confidence: 89%

“…Although data science provides powerful tools, there continue to be issues with the required knowledge to apply those tools to specific situations. In a study of urban revitalization, Green et al concluded in part to "Apply local knowledge before drawing conclusions" [22]. Berrer et al similarly argued for a need to acquire and incorporate rich domain knowledge in predicting outcomes of football games [2].…”

Section: Data Science Teams and Disciplinary Diversitymentioning

confidence: 99%

“…While collaborative practices of data scientists have been discussed extensively in the literature (e.g. [2,4,21,22,54,63,70]), our informants placed much emphasis on their interactions with subject matter experts, in order to understand the data with which they worked, and on other data scientists, in planning how those data would be cleaned, modeled, and presented to the client.…”

Section: Data Sciencementioning

confidence: 99%

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Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

199

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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: The Augmented Vs Automated Data Scientistsupporting

confidence: 89%

Section: Data Science Teams and Disciplinary Diversitymentioning

confidence: 99%

Section: Data Sciencementioning

confidence: 99%

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Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

199

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show abstract

“…Data science and machine learning are complex practices that requires a team with interdisciplinary background [23,41,88,89,107] and skills [14,96,97]. For example, the team often include stakeholders who have deep domain knowledge and own the problem [41,73]; it also must have DS/ML professionals who can actively work with data and write code [61,115].…”

Section: Data Science Team and Data Science Lifecyclementioning

confidence: 99%

How Much Automation Does a Data Scientist Want?

Wang¹,

Liao²,

Zhang³

et al. 2021

Preprint

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Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications.There is an active research thread in AI, AutoML, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.However, do DS and ML workers really want to automate their DS/ML workflow? To answer this question, we first synthesize a human-centered AutoML framework with 6 User Role/Personas, 10 Stages and 43 Sub-Tasks, 5 Levels of Automation, and 5 Types of Explanation, through reviewing research literature and marketing reports. Secondly, we use the framework to guide the design of an online survey study with 217 DS/ML workers who had varying degrees of experience, and different user roles "matching" to our 6 roles/personas. We found that different user personas participated in distinct stages of the lifecycle -but not all stages. Their desired levels of automation and types of explanation for AutoML also varied significantly depending on the DS/ML stage and the user persona. Based on the survey results, we argue there is no rationale from user needs for complete automation of the end-to-end DS/ML lifecycle.We propose new next steps for user-controlled DS/ML automation.CCS Concepts: • Human-centered computing → Computer supported cooperative work.

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A.I. Ethics in the City

Böhlen

2019

Lecture Notes in Computer Science

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Mining Administrative Data to Spur Urban Revitalization

Cited by 6 publications

References 6 publications

Human-AI Collaboration in Data Science

Human-AI Collaboration in Data Science

How Much Automation Does a Data Scientist Want?

A.I. Ethics in the City

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