How Much Automation Does a Data Scientist Want?
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, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle. However, do DS and ML workers really...
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| creator | Wang, Dakuo Liao, Q. Vera Zhang, Yunfeng Khurana, Udayan Samulowitz, Horst Park, Soya Muller, Michael Amini, Lisa |
| description | 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, \autoai, 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. |
| doi_str_mv | 10.48550/arxiv.2101.03970 |
| format | Article |
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advancements of many Artificial Intelligence (AI) applications. There is an
active research thread in AI, \autoai, 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.</description><identifier>DOI: 10.48550/arxiv.2101.03970</identifier><language>eng</language><subject>Computer Science - Human-Computer Interaction ; Computer Science - Learning</subject><creationdate>2021-01</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2101.03970$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2101.03970$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Dakuo</creatorcontrib><creatorcontrib>Liao, Q. Vera</creatorcontrib><creatorcontrib>Zhang, Yunfeng</creatorcontrib><creatorcontrib>Khurana, Udayan</creatorcontrib><creatorcontrib>Samulowitz, Horst</creatorcontrib><creatorcontrib>Park, Soya</creatorcontrib><creatorcontrib>Muller, Michael</creatorcontrib><creatorcontrib>Amini, Lisa</creatorcontrib><title>How Much Automation Does a Data Scientist Want?</title><description>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, \autoai, 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.</description><subject>Computer Science - Human-Computer Interaction</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzskKwjAYBOBcPIj6AJ7MC1iTtNlOIu6geFDwWP40KQa0lTZub29dLjPMZfgQ6lMSJYpzMoLq6e8Ro4RGJNaStNFoVT7w9pad8OQWygsEXxZ4VroaA55BALzPvCuCrwM-QhHGXdTK4Vy73r876LCYH6ar4Wa3XE8nmyEISZqIZW6czKkQJs-oVVxZ7lizrVRSC2OVSRioRAihE6MzJhuhNUxLAE7iDhr8br_i9Fr5C1Sv9CNPv_L4DUAmPEs</recordid><startdate>20210106</startdate><enddate>20210106</enddate><creator>Wang, Dakuo</creator><creator>Liao, Q. Vera</creator><creator>Zhang, Yunfeng</creator><creator>Khurana, Udayan</creator><creator>Samulowitz, Horst</creator><creator>Park, Soya</creator><creator>Muller, Michael</creator><creator>Amini, Lisa</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210106</creationdate><title>How Much Automation Does a Data Scientist Want?</title><author>Wang, Dakuo ; Liao, Q. Vera ; Zhang, Yunfeng ; Khurana, Udayan ; Samulowitz, Horst ; Park, Soya ; Muller, Michael ; Amini, Lisa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-a637fbe7f166bfc1d858d5e2f16d78796bd8b42a8466694b9c27485db297aa503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computer Science - Human-Computer Interaction</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Dakuo</creatorcontrib><creatorcontrib>Liao, Q. Vera</creatorcontrib><creatorcontrib>Zhang, Yunfeng</creatorcontrib><creatorcontrib>Khurana, Udayan</creatorcontrib><creatorcontrib>Samulowitz, Horst</creatorcontrib><creatorcontrib>Park, Soya</creatorcontrib><creatorcontrib>Muller, Michael</creatorcontrib><creatorcontrib>Amini, Lisa</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Dakuo</au><au>Liao, Q. Vera</au><au>Zhang, Yunfeng</au><au>Khurana, Udayan</au><au>Samulowitz, Horst</au><au>Park, Soya</au><au>Muller, Michael</au><au>Amini, Lisa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How Much Automation Does a Data Scientist Want?</atitle><date>2021-01-06</date><risdate>2021</risdate><abstract>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, \autoai, 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.</abstract><doi>10.48550/arxiv.2101.03970</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.2101.03970 |
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| recordid | cdi_arxiv_primary_2101_03970 |
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| subjects | Computer Science - Human-Computer Interaction Computer Science - Learning |
| title | How Much Automation Does a Data Scientist Want? |
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