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REVIEW 3 major objections 6 minor 74 references

Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Working data scientists see AutoAI as a complementary partner, not a replacement, and expect it to act as collaborator and teacher.

desk verdict A solid, honestly-scoped qualitative study that gives practitioners' perceptions of AutoAI a shared vocabulary; its real limits are on the table, so it deserves a referee. read the letter →

arxiv 1909.02309 v1 pith:PTSWZ2J6 submitted 2019-09-05 cs.HC cs.AIcs.LG

Dakuo Wang , Justin D. Weisz , Michael Muller , Parikshit Ram , Werner Geyer , Casey Dugan , Yla Tausczik , Horst Samulowitz
show 1 more author
Alexander Gray
This is my paper · ORCID
classification cs.HCcs.AIcs.LG
keywords AutoAIHuman-AIcollaborationdatasciencepracticeautomationMLscientistperceptionsfutureofworkdomainexpertise
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks how practicing data scientists expect automated AI (AutoAI) systems to change their work, and reports that the answer is a mix of unease and optimism. Through 20 semi-structured interviews with data scientists at a large multinational technology company, the authors find that practitioners see automation of data cleaning, feature engineering, and model building as inevitable, yet they remain confident that their own expertise will stay indispensable. The central claim is that data scientists perceive AutoAI as a complementary partner, taking over tedious pipeline steps while leaving human judgment, domain knowledge, and storytelling in charge, so the future of data science is human-AI collaboration rather than replacement. The paper matters because it redirects AutoAI design from full automation toward tools that explain, teach, and collaborate.

What carries the argument

The analytic machinery is a three-role typology for AutoAI, collaborator, teacher, and data scientist, derived from open coding of the interviews, together with the scatter-gather pattern of data science teamwork it is mapped onto. Scatter-gather names the alternating rhythm in which data scientists work alone on data and code and then gather to share insights and plan next steps. The typology does the argument's work: it gives AutoAI a social position in the team, and it lets the authors claim that the design goal should be a system that advises, explains, and learns from humans rather than one that silently replaces them.

What would settle it

A longitudinal field study would settle it: give a team of data scientists sustained access to a production AutoAI system for several months, then measure whether they still describe their own role as indispensable and complementary. If experienced users report that AutoAI replaces rather than complements their judgment, or that domain expertise can be fully encoded into the tool, the central claim fails.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is a set of perceptions held by working data scientists about a technology most of them had not yet used: AutoAI is seen as both a threat and an inevitability, and ultimately as a collaborator and teacher rather than a substitute. Informants valued AutoAI for speeding up the path from data to insights, providing a baseline model to improve on, and demonstrating coding and modeling practices; they worried that it could erode technical depth, hide the reasoning behind models, and ignore the domain knowledge needed to interpret messy real-world data. The authors conclude that AutoAI should be designed to augment rather than automate the human role, with transparency and explanation built in, and they cast AutoAI as a first-class participant in the scatter-gather rhythm of data science teamwork.

Load-bearing premise

The load-bearing premise is that reactions to a short AutoAI demo plus hypothetical questions stand in for perceptions of real, mature AutoAI tools, because 15 of the 20 informants had never used one.

Editorial extensions

If this is right

  • AutoAI interfaces should treat explainability and transparency as core design requirements, since informants linked trust to seeing how models are built and why choices were made.
  • AutoAI should be positioned to augment the data scientist, taking over repetitive pipeline steps while leaving human judgment and domain expertise in control.
  • AutoAI can serve as a teaching tool, generating code and explanations that help novices and experienced practitioners learn or refresh data science practice.
  • The scatter-gather view implies AutoAI features that support the gather phase, such as recommending analyses the team has not tried, building consensus, and advising from team-level effort, rather than only automating individual work.
  • Data science roles may shift toward eliciting domain knowledge and communicating results, while managers may be drawn to the cost savings of automation even when practitioners are not.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the perceptions reported here are an artifact of novelty, sustained use of mature AutoAI tools could push practitioners toward either deeper trust or sharper resistance; a longitudinal replication would reveal which.
  • The complementary-role pattern may extend beyond data science to other expert professions, such as radiologists, analysts, or journalists, whose craft also mixes routine pipeline work with judgment and domain knowledge.
  • A testable design implication the authors did not develop: AutoAI that cites the sources of its choices, as one informant requested, could be evaluated for whether it increases trust and learning outcomes relative to explanation-only interfaces.
  • The strongest open question is whether the complementary-role perception survives contact with a production AutoAI that is genuinely better than the human at a task; if it does not, the collaborator framing would need revising.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper reports a semi-structured interview study with 20 data scientists at IBM, aiming to understand how practitioners perceive AutoAI (automated machine learning) systems and how these systems might change data science work. The authors describe current work practices (including a 'scatter-gather' collaboration pattern), informants' mixed and ambivalent perceptions of AutoAI, and three contributions: (1) identification of the scatter-gather pattern and how AutoAI might fit into it; (2) framing AutoAI as a first-class collaborative subject rather than merely a tool; and (3) the claim that data scientists see AutoAI as complementary to their own role, never eliminating the need for human input. The study used a demo of an AutoAI system built by the authors, shown to informants during interviews, and open coding of transcripts.

Significance. If the results hold, this is one of the early empirical examinations of how practicing data scientists perceive AutoAI, a topic of direct relevance to CSCW and HCI research on human-AI collaboration and the future of work. The paper's strengths include original interview data with substantial quotes, a transparent description of the methods, and an unusually candid limitations section (Section 6.2). The qualitative analysis is appropriate for an exploratory question, and the authors are careful to distinguish perceptions from actual experience. However, the central claim that data scientists view AutoAI as 'never eliminating' human input is sensitive to the specific elicitation instrument (the authors' own demo) and to the sample composition; the paper's contribution statements are somewhat stronger than the evidence supports.

major comments (3)
  1. [§2.1, §4.2, §6.2] The paper's third contribution—that data scientists see AutoAI as complementary and never eliminating human input—rests primarily on reactions to the authors' own AutoAI demo, which is described in Section 2.1 and shown in Figure 2. This demo presents a transparent, human-in-the-loop interface (progress pane, pipeline visualization, leaderboard, clickable model details). Because 15 of 20 informants had never previously used AutoAI (Section 5.2; Section 6.2), their answers necessarily mix first impressions of this particular interface with hypothetical reasoning. The paper acknowledges this in Section 6.2 but does not address the possibility that the demo's transparent design primed 'collaborator' attributions; a more opaque AutoAI that returned only a final model might have elicited more replacement-oriented views. Please either restrict Contribution 3 to perceptions of transparent, human-in-the-loop AutoAI interfaces, or provide evidence that perceived indispensability is independent of interface design—for example, by comparing responses of the 5 prior AutoAI users with the 15 novices.
  2. [§5.3.3, Contribution 3] The claim that data scientists 'see AutoAI as taking on a complementary role to their own, never eliminating the need for their own human input' is stronger than the data presented. In Section 5.3.3, I15 (a manager/director) explicitly predicts 'there'll be less data scientists needed than today,' and I7 says the role of the data scientist 'could get a bit murky' as domain experts might do data science themselves. These statements suggest meaningful variation in views, not consensus. Please quantify or qualify the result: report how many of the 20 informants expressed the complementary view versus replacement concerns, and note that the sample includes managers (I7, I14, I15) whose perspectives may differ from those of non-managerial data scientists.
  3. [§4.1, §6.2, Abstract] All informants were recruited from a single multinational technology company via snowball sampling, and most worked in small teams of 2-3 data scientists. The 'scatter-gather' collaboration pattern and many of the perceptions reported may be specific to this organizational and team context. While Section 6.2 candidly lists this limitation, the abstract and contribution statements do not carry the same caveats; as written, they generalize beyond the evidence. Please add explicit boundary conditions to the contributions (e.g., 'in this sample, data scientists perceived...') and, where feasible, discuss what would need to be true for these findings to transfer to other organizations and team sizes.
minor comments (6)
  1. [Abstract] The abstract contains a grammatical error: 'based on a target objectives' should be 'based on a target objective' or 'based on target objectives.'
  2. [§4] 'generalizability to some extend' should be 'generalizability to some extent.'
  3. [§5.1.3] The word 'conducing' appears where 'conducting' is intended.
  4. [§5.3.2] 'being ateacher' is a typo and should read 'being a teacher.'
  5. [§2 vs. §5] The paper alternates between 'AutoAI' and 'AutoML' (e.g., abstract and Section 5.2.1 use both terms). Please define the scope and use one term consistently, or explicitly state they are used interchangeably.
  6. [§5.1] The 'scatter-gather' pattern is introduced as a finding, but the term is not defined when first used in the results; consider defining it in the Methodology section.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's claims are grounded in original interview data, and the one self-referential element (the authors' own AutoAI demo) is an acknowledged elicitation limitation rather than a circular derivation.

full rationale

This is a qualitative interview study with no mathematical derivation chain, so the standard circularity patterns (self-definitional equations, fitted parameters renamed as predictions, imported uniqueness theorems) do not apply. The central claim that data scientists perceive AutoAI as complementary and never eliminating human input rests on thematic coding of 20 semi-structured interviews, with direct quotations from informants as evidence. The authors do cite their own prior work, e.g., Muller et al. [48] for the broad term 'data science worker' and for prior findings on data work, but these citations are contextual and not load-bearing for the new contribution. The only self-referential aspect is that the interview protocol used a demo of AutoAI built by the authors (Section 2.1, Figure 2) to give informants a baseline understanding. The paper explicitly acknowledges in Section 6.2 that 75% of informants had not previously used AutoAI and therefore could only offer perceptions of how it might affect practice rather than concrete experiences. This is a validity or generalizability limitation (the demo's design may influence the 'collaborator' attribution), not a circularity of evidence: the finding is still an empirical report of what these informants said in response to the stimulus. Per the hard rules, intervention bias is not a circularity argument, and no quote exhibits a reduction of the conclusion to its own inputs. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the three domain assumptions listed; no free parameters apply to this qualitative study. The paper does not introduce invented entities; the roles of AutoAI (collaborator, teacher, data scientist) are interpretive themes derived from interview quotes.

assumptions (3)
  • domain assumption Self-reported perceptions from interviews reflect genuine work practices and attitudes.
    The study uses semi-structured interviews and recall without observational or log-data triangulation (Section 4.2).
  • domain assumption The AutoAI demo shown to informants is a valid stand-in for AutoAI systems.
    The demo was built by the authors and was the basis for most informants' reactions; 75% had never used AutoAI (Sections 2.1, 6.2).
  • domain assumption Twenty IBM data scientists form an informative sample for understanding data science practice.
    Snowball sampling within one company limits generalizability; the paper acknowledges this (Sections 4.1, 6.2).

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Cite this review

Pith. "Pith review of Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI." pith.science (2026). https://pith.science/paper/PTSWZ2J6

@misc{pith2026190902309,
  author       = {Pith},
  title        = {Pith review of: Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PTSWZ2J6}},
  note         = {Machine review of arXiv:1909.02309}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 1909.02309 by the authors.

Figure 1
Figure 1. Typical steps of a data science workflow, consisting of three high-level phases: data preparation, model [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Screenshot from the AutoAI demo used in our interview study. Top-left: progress pane showing the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Works this paper leans on

74 extracted references · 65 canonical work pages

  1. [1]

    Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the people: The role of humans in interactive machine learning. AI Magazine 35, 4 (2014), 105–120. Proc. ACM Hum.-Comput. Interact., Vol. 3, No. CSCW, Article 211. Publication date: November 2019. 211:22 Dakuo Wang et al

    work page 2014

  2. [2]

    Daniel Berrar, Philippe Lopes, and Werner Dubitzky. 2019. Incorporating domain knowledge in machine learning for soccer outcome prediction. Machine Learning 108, 1 (2019), 97–126

    work page 2019

  3. [3]

    Chris Bopp, Ellie Harmon, and Amy Voida. 2017. Disempowered by data: Nonprofits, social enterprises, and the consequences of data-driven work. In Proceedings of the 2017 CHI conference on human factors in computing systems . ACM, 3608–3619

    work page 2017

  4. [4]

    Christine L Borgman, Jillian C Wallis, and Matthew S Mayernik. 2012. Who’s got the data? Interdependencies in science and technology collaborations. Computer Supported Cooperative Work (CSCW) 21, 6 (2012), 485–523

    work page 2012

  5. [5]

    Brian d’Alessandro, Cathy O’Neil, and Tom LaGatta. 2017. Conscientious classification: A data scientist’s guide to discrimination-aware classification. Big data 5, 2 (2017), 120–134

    work page 2017

  6. [6]

    Tommy Dang, Fang Jin, et al. 2018. Predict saturated thickness using tensorboard visualization. In Proceedings of the Workshop on Visualisation in Environmental Sciences . Eurographics Association, 35–39

    work page 2018

  7. [7]

    DataRobot. [n. d.]. Automated Machine Learning for Predictive Modeling. Retrieved 3-April-2019 from https: //www.datarobot.com/

    work page 2019

  8. [8]

    Vasant Dhar. 2013. Data Science and Prediction. Commun. ACM 56, 12 (Dec. 2013), 64–73. https://doi.org/10.1145/ 2500499

    work page 2013

Show all 74 references
  1. [9]

    Joan DiMicco, David R Millen, Werner Geyer, Casey Dugan, Beth Brownholtz, and Michael Muller. 2008. Motivations for social networking at work. In Proceedings of the 2008 ACM conference on Computer supported cooperative work . ACM, 711–720

    2008

  2. [10]

    Seth Dobrin and IBM Analytics. 2017. How IBM builds an effective data science team. https://venturebeat.com/2017/ 12/22/how-ibm-builds-an-effective-data-science-team/

    2017

  3. [11]

    Paul Dourish and Edgar Gómez Cruz. 2018. Datafication and data fiction: Narrating data and narrating with data. Big Data & Society 5, 2 (2018), 2053951718784083

    2018

  4. [12]

    Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2018. Neural architecture search: A survey. arXiv preprint arXiv:1808.05377 (2018)

    2018 arXiv

  5. [13]

    EpistasisLab. [n. d.]. tpot. Retrieved 3-April-2019 from https://github.com/EpistasisLab/tpot

    2019

  6. [14]

    Melanie Feinberg. 2017. A design perspective on data. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2952–2963

    2017

  7. [15]

    Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015. Efficient and robust automated machine learning. In Advances in Neural Information Processing Systems . 2962–2970

    2015

  8. [16]

    Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189–1232

    2001

  9. [17]

    Yolanda Gil, James Honaker, Shikhar Gupta, Yibo Ma, Vito D’Orazio, Daniel Garijo, Shruti Gadewar, Qifan Yang, and Neda Jahanshad. 2019. Towards Human-Guided Machine Learning. (2019)

    2019

  10. [18]

    Google. [n. d.]. Cloud AutoML. Retrieved 3-April-2019 from https://cloud.google.com/automl/

    2019

  11. [19]

    Google. [n. d.]. Colaboratory. Retrieved 3-April-2019 from https://colab.research.google.com

    2019

  12. [20]

    Brian Granger, Chris Colbert, and Ian Rose. 2017. JupyterLab: The next generation jupyter frontend. JupyterCon 2017 (2017)

    2017

  13. [21]

    Corrado Grappiolo, Emile van Gerwen, Jack Verhoosel, and Lou Somers. 2019. The Semantic Snake Charmer Search Engine: A Tool to Facilitate Data Science in High-tech Industry Domains. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval . ACM, 355–359

    2019

  14. [22]

    Ben Green, Alejandra Caro, Matthew Conway, Robert Manduca, Tom Plagge, and Abby Miller. 2015. Mining adminis- trative data to spur urban revitalization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1829–1838

    2015

  15. [23]

    H2O. [n. d.]. H2O. Retrieved 3-April-2019 from https://h2o.ai

    2019

  16. [24]

    Jeffrey Heer. 2019. Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences 116, 6 (2019), 1844–1850

    2019

  17. [25]

    Jeffrey Heer and Ben Shneiderman. 2012. Interactive dynamics for visual analysis. Queue 10, 2 (2012), 30

    2012

  18. [26]

    Jeffrey Heer, Fernanda B Viégas, and Martin Wattenberg. 2007. Voyagers and voyeurs: supporting asynchronous collaborative information visualization. In Proceedings of the SIGCHI conference on Human factors in computing systems . ACM, 1029–1038

    2007

  19. [27]

    Youyang Hou and Dakuo Wang. 2017. Hacking with NPOs: collaborative analytics and broker roles in civic data hackathons. Proceedings of the ACM on Human-Computer Interaction 1, CSCW (2017), 53

    2017

  20. [28]

    Shamsi T Iqbal and Eric Horvitz. 2010. Notifications and awareness: a field study of alert usage and preferences. In Proceedings of the 2010 ACM conference on Computer supported cooperative work . ACM, 27–30

    2010

  21. [29]

    Piper Jackson. 2019. Casting a Wider (Neural) Net: Introducing Data Science and Machine Learning to a Larger Audience. In Proceedings of the Western Canadian Conference on Computing Education . ACM, 14

    2019

  22. [30]

    Project Jupyter. [n. d.]. Jupyter Notebook. Retrieved 3-April-2019 from https://jupyter.org Proc. ACM Hum.-Comput. Interact., Vol. 3, No. CSCW, Article 211. Publication date: November 2019. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Autom...

    2019

  23. [31]

    Project Jupyter. [n. d.]. JupyterLab. https://www.github.com/jupyterlab/jupyterlab

  24. [32]

    Kaggle. [n. d.]. Kaggle: Your Home for Data Science. Retrieved 3-April-2019 from https://www.kaggle.com

    2019

  25. [33]

    Kaggle. 2017. The State of Data Science & Machine Learning. https://www.kaggle.com/surveys/2017

    2017

  26. [34]

    Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011. Wrangler: Interactive visual specification of data transformation scripts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM, 3363–3372

    2011

  27. [35]

    James Max Kanter and Kalyan Veeramachaneni. 2015. Deep feature synthesis: Towards automating data science endeavors. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 1–10

    2015

  28. [36]

    Mary Beth Kery, Marissa Radensky, Mahima Arya, Bonnie E John, and Brad A Myers. 2018. The story in the notebook: Exploratory data science using a literate programming tool. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 174

    2018

  29. [37]

    Udayan Khurana, Deepak Turaga, Horst Samulowitz, and Srinivasan Parthasrathy. 2016. Cognito: Automated feature engineering for supervised learning. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) . IEEE, 1304–1307

    2016

  30. [38]

    Ákos Kiss and Tamás Szirányi. 2013. Evaluation of manually created ground truth for multi-view people localization. In Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications . ACM, 9

    2013

  31. [39]

    Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian E Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica B Hamrick, Jason Grout, Sylvain Corlay, et al. 2016. Jupyter Notebooks-a publishing format for reproducible computational workflows.. In ELPUB. 87–90

    2016

  32. [40]

    Donald Ervin Knuth. 1984. Literate programming. Comput. J. 27, 2 (1984), 97–111

    1984

  33. [41]

    Hoos, Frank Hutter, and Kevin Leyton-Brown

    Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. 2017. Auto-WEKA 2.0: Automatic Model Selection and Hyperparameter Optimization in WEKA. J. Mach. Learn. Res. 18, 1 (Jan. 2017), 826–830. http://dl.acm.org/citation.cfm?id=3122009.3122034

    2017

  34. [42]

    Georgia Kougka, Anastasios Gounaris, and Alkis Simitsis. 2018. The many faces of data-centric workflow optimization: a survey. International Journal of Data Science and Analytics 6, 2 (2018), 81–107

    2018

  35. [43]

    Hoang Thanh Lam, Johann-Michael Thiebaut, Mathieu Sinn, Bei Chen, Tiep Mai, and Oznur Alkan. 2017. One button machine for automating feature engineering in relational databases. arXiv preprint arXiv:1706.00327 (2017)

    2017 arXiv

  36. [44]

    Fei-Fei Li. 2018. How to Make A.I. That’s Good for People. The New York Times (7 March 2018). Retrieved 3-April-2019 from https://www.nytimes.com/2018/03/07/opinion/artificial-intelligence-human.html

    2018

  37. [45]

    Zachary C Lipton. 2016. The mythos of model interpretability. arXiv preprint arXiv:1606.03490 (2016)

    2016 arXiv

  38. [46]

    Yaoli Mao, Dakuo Wang, Michael Muller, KUSH VARSHNEY, IOANA Baldini, CASEY Dugan, and ALEKSANDRA MOJSILOVIÄĘ. 2020. How Data Scientists Work Together With Domain Experts in Scientific Collaborations. In Proceedings of the 2020 ACM conference on GROUP . ACM

    2020

  39. [47]

    Matthew Thomas Martinez. 2016. An Overview of Google’s Machine Intelligence Software TensorFlow. Technical Report. Sandia National Lab.(SNL-NM), Albuquerque, NM (United States)

    2016

  40. [48]

    Vera Liao, Casey Dugan, and Thomas Erickson

    Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019. How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. In Proceedings of the 2019 CHI Conference on Human Factors in ...

    2019

  41. [49]

    Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B Khalil, and Deepak Turaga. 2017. Learning feature engineering for classification. In Proceedings of the 26th International Joint Conference on Artificial Intelligence . AAAI Press, 2529–2535

    2017

  42. [50]

    Gina Neff, Anissa Tanweer, Brittany Fiore-Gartland, and Laura Osburn. 2017. Critique and contribute: A practice-based framework for improving critical data studies and data science. Big Data 5, 2 (2017), 85–97

    2017

  43. [51]

    Judith S Olson, Dakuo Wang, Gary M Olson, and Jingwen Zhang. 2017. How people write together now: Beginning the investigation with advanced undergraduates in a project course. ACM Transactions on Computer-Human Interaction (TOCHI) 24, 1 (2017), 4

    2017

  44. [52]

    Randal S Olson and Jason H Moore. 2016. TPOT: A tree-based pipeline optimization tool for automating machine learning. In Workshop on Automatic Machine Learning. 66–74

    2016

  45. [53]

    Samir Passi and Steven Jackson. 2017. Data vision: Learning to see through algorithmic abstraction. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing . ACM, 2436–2447

    2017

  46. [54]

    Samir Passi and Steven J Jackson. 2018. Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 136

    2018

  47. [55]

    Kayur Patel, James Fogarty, James A Landay, and Beverly Harrison. 2008. Investigating statistical machine learning as a tool for software development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM, 667–676. Proc. ACM Hum.-Comput. Interact....

    2008

  48. [56]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Le...

    2011

  49. [57]

    Kathleen H Pine and Max Liboiron. 2015. The politics of measurement and action. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems . ACM, 3147–3156

    2015

  50. [58]

    Why Should I Trust You?

    Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD âĂŹ16 (2016). https: //doi.org/10.1145/2939672.2939778

    2016

  51. [59]

    Adam Rule, Aurélien Tabard, and James D Hollan. 2018. Exploration and explanation in computational notebooks. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems . ACM, 32

    2018

  52. [60]

    Cathrine Seidelin. 2018. Developing Notations for Data Infrastructuring in Participatory Design. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing . ACM, 81–84

    2018

  53. [61]

    Shahriari, K

    B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas. 2016. Taking the human out of the loop: A review of bayesian optimization. Proc. IEEE 104, 1 (2016), 148–175

    2016

  54. [62]

    Il-Yeol Song and Yongjun Zhu. 2016. Big data and data science: what should we teach? Expert Systems 33, 4 (2016), 364–373

    2016

  55. [63]

    Manuel Stein, Halldór Janetzko, Daniel Seebacher, Alexander Jäger, Manuel Nagel, Jürgen Hölsch, Sven Kosub, Tobias Schreck, Daniel Keim, and Michael Grossniklaus. 2017. How to make sense of team sport data: From acquisition to data modeling and research aspects. Data 2, 1 (2017), 2

    2017

  56. [64]

    Eva Thelisson. 2017. Towards Trust, Transparency and Liability in AI/AS systems.. In IJCAI. 5215–5216

    2017

  57. [65]

    Eva Thelisson, Kirtan Padh, and L Elisa Celis. 2017. Regulatory mechanisms and algorithms towards trust in AI/ML. In Proceedings of the IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), Melbourne, Australia

    2017

  58. [66]

    Hoos, Frank Hutter, and Kevin Leyton-Brown

    Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. 2012. Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms. CoRR abs/1208.3719 (2012). http://arxiv.org/abs/1208.3719

    2012 arXiv

  59. [67]

    Michelle Ufford, Matthew Seal, and Kyle Kelley. 2018. Beyond Interactive: Notebook Innovation at Netflix

    2018

  60. [68]

    Wil MP Van der Aalst. 2014. Data scientist: The engineer of the future. In Enterprise interoperability VI . Springer, 13–26

    2014

  61. [69]

    Joaquin Vanschoren. 2018. Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018)

    2018 arXiv

  62. [70]

    Stijn Viaene. 2013. Data scientists aren’t domain experts. IT Professional 15, 6 (2013), 12–17

    2013

  63. [71]

    Flavio G Villanustre. 2014. Big data trends and evolution: A human perspective. In Proceedings of the 3rd annual conference on Research in information technology . ACM, 1–2

    2014

  64. [72]

    Michael A Walker. 2015. The professionalisation of data science. International Journal of Data Science 1, 1 (2015), 7–16

    2015

  65. [73]

    Wikipedia contributors. 2019. General Data Protection Regulation — Wikipedia, The Free Encyclopedia. https: //en.wikipedia.org/w/index.php?title=General_Data_Protection_Regulation&oldid=890913930 [Online; accessed 3- April-2019]

    2019

  66. [74]

    Miller Debbie Hughes Will Markow, Soumya Braganza

    Bledi Taska Steven M. Miller Debbie Hughes Will Markow, Soumya Braganza. 2017. The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market. (2017). https://www.ibm.com/downloads/cas/3RL3VXGA [Online; accessed 3-April-2019]. Proc. ACM Hum.-Comput. Inte...

    2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.