Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Open accessAbstract
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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Organisations use data science to analyse, predict, and enhance productivity.
“Organisations in the current world use this discipline to analyse, predict, and accurately work in the direction which enhances their productivity [35], [36].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [36]
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Fan, M., Yang, X., Yu, T., Liao, Q. V., & Zhao, J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512943 · Cited by 81 · see citing works
Human-AI Collaboration in a Cooperative Game Setting
Ashktorab, Z., Liao, Q., Dugan, C., Johnson, J. M., Pan, Q., Zhang, W., Kumaravel, S., & Campbell, M. S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415167 · Cited by 83 · see citing works
“The term “human-AI collaboration” has emerged in recent work studying user interaction with AI systems [2, 11, 45, 58].”
BackgroundSelf-citationAutoDS: Towards Human-Centered Automation of Data Science
Wang, D., Andrés, J., Weisz, J. D., Oduor, E., & Dugan, C. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445526 · Cited by 79 · see citing works
Self-citationCaldwell, S., Sweetser, P., O’Donnell, N., Knight, M. J., Aitchison, M., Gedeon, T., Johnson, D. M., Brereton, M. F., Gallagher, M. R., & Conroy, D. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3514257 · Cited by 82 · see citing works
Li, H., Wang, Y., & Qu, H. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642726 · Cited by 77 · see citing works
“The previous studies in human-AI collaboration often focus on specifc domains, such as human-AI collaborative decision making ( e.g. , [33, 86, 121]), creativity ( e.g. , [28, 76, 102]), and data science ( e.g. , [24, 123]).”
BackgroundKim, J., & Lee, S.-S. (2022) · TechTrends · doi:10.1007/s11528-022-00788-9 · Cited by 75 · see citing works
“Their view indicates that although AI's automation contributes to the task performance enhancement, it should reflect levels of students' domain-specific skills and students' agency (Wang et al., 2019).”
BackgroundDesigning Ground Truth and the Social Life of Labels
Muller, M., Wolf, C. T., Andres, J., Desmond, M., Joshi, N. N., Ashktorab, Z., Sharma, A., Brimijoin, K., Pan, Q., Duesterwald, E., et al. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445402 · Cited by 83 · see citing works
“Building ML models involves data practices in a number of data phases along the data science pipeline [5, 114].”
BackgroundSelf-citationBetter Together? An Evaluation of AI-Supported Code Translation
Weisz, J. D., Muller, M., Ross, S. I., Martinez, F., Houde, S., Agarwal, M., Talamadupula, K., & Richards, J. T. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511157 · Cited by 69 · see citing works
Self-citationDesigning AI to Work WITH or FOR People?
Wang, D., Maes, P., Ren, X., Shneiderman, B., Shi, Y., & Wang, Q. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3450394 · Cited by 68 · see citing works
“In addition to the healthcare domain, AI also presents enormous opportunities for the people who may interact with it [5, 28].”
BackgroundSelf-citationNagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023) · Electronics · doi:10.3390/electronics12051092 · Cited by 76 · see citing works
Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students
Zheng, C., Yuan, K., Guo, B., Hadi Mogavi, R., Peng, Z., Ma, S., & Ma, X. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642807 · Cited by 70 · see citing works
Wang, L., Wang, D., Tian, F., Peng, Z., Fan, X., Zhang, Z., Yu, M., Ma, X., & Wang, H. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449083 · Cited by 85 · see citing works
Self-citationBossen, C., & Pine, K. H. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3569892 · Cited by 64 · see citing works
LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing
Wang, B. K., Li, Y., Lv, Z., Xia, H., Xu, Y., & Sodhi, R. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645143 · Cited by 72 · see citing works
“Our work builds upon existing literature in human-AI co-creation [8, 14, 21, 24, 30, 32, 39, 54, 56, 72] and further contributes by developing a new AI system for video editing and studying its impact.”
BackgroundThe Impact of Imperfect XAI on Human-AI Decision-Making
Morrison, K., Spitzer, P., Turri, V., Feng, M., Kühl, N., & Perer, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3641022 · Cited by 69 · see citing works
“CSCW, and more specifically human-AI collaboration, is prevalent across high-stakes scenarios [16, 49, 75, 80].”
BackgroundPassing the Data Baton : A Retrospective Analysis on Data Science Work and Workers
Crisan, A., Fiore-Gartland, B., & Tory, M. K. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030340 · Cited by 66 · see citing works
“Within many organizations, data science work extends to a phase where data preparation and analysis processes are routinely put into production and deployed to tackle real-world problems [39, 54, 59, 81].”
BackgroundMethodInfluentialVerma, H., Mlynář, J., Schaer, R., Reichenbach, J., Jreige, M., Prior, J. O., Évéquoz, F., & Depeursinge, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581506 · Cited by 63 · see citing works
“[105], who examined the perspectives and experiences of data science experts while collaborating with automated AI pipelines.”
BackgroundMcneese, N. J., Flathmann, C., O'Neill, T. A., & Salas, E. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107874 · Cited by 54 · see citing works
An ADMM Based Framework for AutoML Pipeline Configuration
Liu, S., Ram, P., Vijaykeerthy, D., Bouneffouf, D., Bramble, G., Samulowitz, H., Wang, D., Conn, A. R., & Gray, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5926 · Cited by 60 · see citing works
Self-citationWeidele, D. K. I., Weisz, J. D., Oduor, E., Muller, M. J., Andrés, J., Gray, A., & Wang, D. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377538 · Cited by 58 · see citing works
“Although the ability for AutoAI to speed up the model-building process is very promising, there are challenges due to the opaque nature of how AutoAI actually creates models [28, 44, 45].”
BackgroundMethodInfluentialSelf-citationKobiella, C., Flores López, Y. S., Waltenberger, F., Draxler, F., & Schmidt, A. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641964 · Cited by 57 · see citing works
“By contrast, a study by Wang et al. [88] among data science practitioners postulates that AI is a “first-class subject” in data science rather than a tool and a study by Hayashi and Wakabayashi [42] implies that in courtroom decision-making the AI system may be considered more trustworthy than the…”
ResultForgetting Practices in the Data Sciences
Muller, M. J., & Strohmayer, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517644 · Cited by 55 · see citing works
Self-citationFits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
Crisan, A., & Fiore-Gartland, B. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445775 · Cited by 56 · see citing works
Choudhury, A. (2022) · JMIR Human Factors · doi:10.2196/35421 · Cited by 56 · see citing works
“Regarding perceptions of AI, there is relatively less precedent operationally [100,101].”
BackgroundZheng, C., Wang, D., Wang, A. Y., & Ma, X. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517615 · Cited by 54 · see citing works
“An interview study reported that DS workers believe that automation can give them some assistant, but it can never (and should not be designed to) replace their jobs [46].”
BackgroundSelf-citationMahmud, B. U., Hong, G. Y., & Fong, B. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3542698 · Cited by 63 · see citing works
“Much research work has been done, and more is ongoing to evaluate the human–AI system in a collaborative environment [10, 11].”
BackgroundSelective Explanations: Leveraging Human Input to Align Explainable AI
Lai, V. W.-M., Zhang, Y., Chen, C., Liao, Q. V., & Tan, C. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610206 · Cited by 64 · see citing works
“This difficulty to use current XAI techniques can be attributed to their lack of compatibility with how humans produce and consume explanations, as criticized by many drawing from the social sciences literature [62, 73, 94].”
BackgroundNahar, N., Zhang, H., Lewis, G. A., Zhou, S., & Kästner, C. (2023) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain58948.2023.00034 · Cited by 58 · see citing works
My teammate is AI: understanding students’ perceptions of student-AI collaboration in drawing tasks
Kim, J., & Cho, Y. H. (2025) · Asia Pacific Journal of Education · doi:10.1080/02188791.2023.2286206 · Cited by 50 · see citing works
“These quotes give us important insights that AI should value students’ agency and harness students’ preference and sense of ownership in learning (Wang et al., 2019).”
BackgroundIncreasing the Speed and Accuracy of Data Labeling Through an AI Assisted Interface
Desmond, M., Muller, M. J., Ashktorab, Z., Dugan, C., Duesterwald, E., Brimijoin, K., Finegan‐Dollak, C., Brachman, M., Sharma, A., Joshi, N. N., et al. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450698 · Cited by 51 · see citing works
“A common paradigm for supporting and optimizing human decision processes is to leverage human-AI collaboration [6, 35, 40].”
BackgroundSelf-citationPipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
Ono, J. P., Castelo, S., López, R., Bertini, E., Freire, J., & Silva, C. T. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030361 · Cited by 48 · see citing works
“For example, while ATMSeer [63] visualizes pipelines produced by ATM [53], AutoAIViz can only show pipelines from AutoAI [62].”
MethodInfluentialEffects of Communication Directionality and AI Agent Differences in Human-AI Interaction
Ashktorab, Z., Dugan, C., Johnson, J., Pan, Q., Zhang, W., Kumaravel, S., & Campbell, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445256 · Cited by 50 · see citing works
“has investigated this kind of collaboration in various domains including drawing pictures collaboratively [49], collaborating with AI systems to automate data science projects [65].”
BackgroundSelf-citationExploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games
Hou, K., Hou, T., & Cai, L. (2023) · Systems · doi:10.3390/systems11050217 · Cited by 53 · see citing works
AI-Assisted Human Labeling: Batching for Efficiency without Overreliance
Ashktorab, Z., Desmond, M., Andres, J., Muller, M., Joshi, N. N., Brachman, M., Sharma, A., Brimijoin, K., Pan, Q., Wolf, C. T., et al. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449163 · Cited by 45 · see citing works
“A number of CSCW papers in recent years have addressed questions of human-AI collaboration, for example in medical decision-making [8], data science work [66], or IT infrastructure design practices [72].”
BackgroundSelf-citationTowards an AI‐driven business development framework: A multi‐case study
John, M. M., Olsson, H. H., & Bosch, J. (2022) · Journal of Software Evolution and Process · doi:10.1002/smr.2432 · Cited by 53 · see citing works
Bie, T. D., Raedt, L. D., Hernández‐Orallo, J., Hoos, H. H., Smyth, P., & Williams, C. K. I. (2022) · Communications of the ACM · doi:10.1145/3495256 · Cited by 44 · see citing works
“Also, data science tools in the context of human-AI collaboration are seen as displacing the work practice of data scientists, leading to new roles [47].”
Exploring the Use of Personalized AI for Identifying Misinformation on Social Media
Jahanbakhsh, F., Katsis, Y., Wang, D., Popa, L., & Muller, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581219 · Cited by 42 · see citing works
“The existence of a somewhat similar efect has been reported in contexts where users collaborate with a system to make a decision [22, 40, 71, 100].”
BackgroundSelf-citationDecision Making Strategies and Team Efficacy in Human-AI Teams
Munyaka, I. N. S., Ashktorab, Z., Dugan, C., Johnson, J. M., & Pan, Q. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579476 · Cited by 43 · see citing works
Self-citationSalimzadeh, S., He, G., & Gadiraju, U. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641905 · Cited by 41 · see citing works
“In recent years, the use of AI technologies has evolved to encompass more collaborative approaches that involve both humans and AI systems working together [5, 21, 22, 73, 129].”
BackgroundHopkins, A., & Booth, S. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462527 · Cited by 40 · see citing works
“In past research analyzing ML practice, the vast majority of studies draw participants from Big Tech companies or academia [1, 5, 22, 24, 28, 29, 29, 30, 36, 37, 41, 45, 58, 60], with few exceptions [9, 25, 42].”
BackgroundDoes More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making
Lu, Z., Wang, D., & Yin, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3653708 · Cited by 40 · see citing works
Self-citationA study of UX practitioners roles in designing real-world, enterprise ML systems
Zdanowska, S., & Taylor, A. S. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517607 · Cited by 39 · see citing works
MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming
Li, Y., Sun, L., & Zhang, Y. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711032 · Cited by 86 · see citing works
Kross, S., & Guo, P. J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476052 · Cited by 38 · see citing works
“We believe our findings show how the socio-emotional infrastructure that data scientists provide will not be easily automated away , even as increasingly sophisticated AutoML methods become commoditized.”
BackgroundMethodInfluentialPreparing Future Designers for Human-AI Collaboration in Persona Creation
Goel, T., Shaer, O., Delcourt, C., Gu, Q., & Cooper, A. (2023) · Proceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work · doi:10.1145/3596671.3598574 · Cited by 37 · see citing works
Combining Multi-Agent Systems and Artificial Intelligence of Things: Technical challenges and gains
Luzolo, P. H., Elrawashdeh, Z., Tchappi, I., Galland, S., & Outay, F. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101364 · Cited by 43 · see citing works
Weisz, J. D., Kumar, S. V., Muller, M., Browne, K.-E., Goldberg, A., Heintze, K. E., & Bajpai, S. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3706670 · Cited by 37 · see citing works
Self-citationvan Berkel, N., Bellio, M., Skov, M. B., & BLANDFORD, A. E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3571815 · Cited by 37 · see citing works
“Informants may have various concerns towards the introduction of AI-enabled systems, such as job automation [111], fear of loss of manual skills [103], or legal/responsibility implications [76].”
BackgroundMetaWriter: Exploring the Potential and Perils of AI Writing Support in Scientific Peer Review
Sun, L., Tao, S., Hu, J., & Dow, S. P. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637371 · Cited by 37 · see citing works
“Understanding the tension between human agency and machine automation can help researchers design systems that successfully weave AI scaffolding into the task without sacrificing a user’s agency [31, 83].”
BackgroundBrewer, J., Patel, D., Kim, D., & Murray, A. M. (2024) · Business Horizons · doi:10.1016/j.bushor.2024.04.011 · Cited by 35 · see citing works
Artificial Intelligence as a tool for analysis in Social Sciences: methods and applications
Hernández-Lugo, M. D. L. C. (2024) · LatIA · doi:10.62486/latia202411 · Cited by 34 · see citing works
Mallick, R., Flathmann, C., Duan, W., Schelble, B. G., & Mcneese, N. J. (2024) · Int. J. Hum. Comput. Stud. · doi:10.1016/j.ijhcs.2024.103355 · Cited by 32 · see citing works
John, M. M., Olsson, H. H., & Bosch, J. (2020) · International Conference on Software and System Process · doi:10.1145/3379177.3388892 · Cited by 35 · see citing works
“Current working practices of data scientists and influence of AutoAI (Automated AI) on these practices are described in [30].”
BackgroundPublic Health Calls for/with AI: An Ethnographic Perspective
Ismail, A., Thakkar, D., Madhiwalla, N., & Kumar, N. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610203 · Cited by 36 · see citing works
“Researchers have explored the evolving nature of Human-AI collaboration due to the introduction of AI systems into organizational workflows (e.g. [16, 62, 70, 92, 99, 101].)”
BackgroundSun, Y., Chen, J., Yao, B., Liu, J., Wang, D., Ma, X., Lu, Y., Xu, Y., & He, L. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687035 · Cited by 32 · see citing works
Self-citationLewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30 · see citing works
AutoML in The Wild: Obstacles, Workarounds, and Expectations
Sun, Y., Song, Q., Gui, X., Ma, F., & Wang, T. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581082 · Cited by 31 · see citing works
“[80] focused on data scientists’ perceptions of AutoML’s utility and potential impacts on data science practices and occupations.”
BackgroundZhang, R., Flathmann, C., Musick, G., Schelble, B. G., McNeese, N. J., Knijnenburg, B. P., & Duan, W. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3635474 · Cited by 31 · see citing works
“AI is being applied in several facets of daily life, such as AI teammates in games, virtual assistants, and AI-powered clinical decision support systems [77, 98, 101].”
BackgroundA Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?
Wang, Z., Huang, C., & Yao, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3702004 · Cited by 32 · see citing works
High hopes, hard falls: consumer expectations and reactions to AI-human collaboration in advertising
Ryoo, Y., Bakpayev, M., Jeon, Y. A., Kim, K. K., & Yoon, S. (2025) · International Journal of Advertising · doi:10.1080/02650487.2025.2458996 · Cited by 32 · see citing works
“Our studies address a significant gap in the advertising literature by examining the largely overlooked but increasingly relevant concept of AI-human collaboration (Li 2019; Qin and Jiang 2019; Wang, Weisz, et al. 2019).”
Wang, A. Y., Wang, D., Drozdal, J., Liu, X., Park, S., Oney, S., & Brooks, C. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451617 · Cited by 31 · see citing works
“For example, Wang et al. [31] summarized a typical data science workfow as three high-level phases: data preparation, model building, and model deployment, where each phase contains many sub-goals and activities; Similarly, [30] proposes a taxonomy of ten stages and six user personas to describe…”
BackgroundSelf-citationWho is Helping Whom? Student Concerns about AI-Teacher Collaboration in Higher Education Classrooms
Han, B., Coghlan, S., Buchanan, G., & McKay, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711104 · Cited by 31 · see citing works
Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability
Hosain, M. T., ANİK, M. H., RAFİ, S., TABASSUM, R., Insia, K., & SIDDIKY, M. M. (2023) · Journal of Metaverse · doi:10.57019/jmv.1306685 · Cited by 36 · see citing works
Arjmandi, M., Fattahi, M., Motevasel, M., & Rezaveisi, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-47174-w · Cited by 30 · see citing works
“These include programming languages like Python or R, statistical analysis tools such as SAS 56 and SPSS 57 , integrated development environments (IDEs) like Jupyter Notebook 58,59 , and automated model building systems such as AutoML 60 and AutoAI 61 .”
Method
- Authors
- Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A.
- Affiliations
- IBM (United States) (US)
- IBM Research - Cambridge (United States) (US)
- University of Maryland, College Park (US)
- ORCID
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2019-11-07
- Volume
- 3
- Issue
- CSCW
- Pages
- 1-24
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W4288086169
- Cited by
- 342
- References
- 60
- Citations by year
- 2026: 23 · 2025: 66 · 2024: 71 · 2023: 69 · 2022: 61 · 2021: 38 · 2020: 14
- FWCI
- 49.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- human-AI collaboration, automated machine learning, data scientists, workplace automation, job security, feature engineering, data preprocessing, model scoring, future of work
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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