Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices

dc.contributor.authorKogan, Marina
dc.contributor.authorHalfaker, Aaron
dc.contributor.authorGuha, Shion
dc.contributor.authorAragon, Cecilia
dc.contributor.authorMuller, Michael
dc.contributor.authorGeiger, Stuart
dc.date.accessioned2023-03-17T22:48:57Z
dc.date.available2023-03-17T22:48:57Z
dc.date.issued2020
dc.description.abstractSocial media platforms and social network sites generate a multitude of digital trace behavioral data, the scale of which often necessitates the use of computational data science methods. On the other hand, the socio-behavioral and often relational nature of the social media data requires the attention to context of user activity traditionally associated with qualitative analysis. Human-Centered Data Science (HCDS) attempts to bridge this gap by both harnessing the power of computational techniques and accounting for highly situated and nuanced nature of the social media activity. In this workshop we plan to consider the methods, pitfalls, and approaches of how to do HCDS effectively. Moreover, from collating and organizing these approaches we hope to progress to considering best (or at least common) practices in HCDS.en
dc.identifier.doi10.1145/3323994.3369898
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4592
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.subjectqualitative methods
dc.subjectquantitative methods
dc.subjectsocial media data
dc.subjecthuman-centered data science
dc.titleMapping Out Human-Centered Data Science: Methods, Approaches, and Best Practicesen
dc.typeText/Conference Paper
gi.citation.startPage151–156
gi.citations.count31
gi.citations.elementMatthias Braun, Darian Meacham (2024): A Plea for (In)Human-centred AI, In: Philosophy & Technology 3(37), doi:10.1007/s13347-024-00785-1
gi.citations.elementMichael Muller, Lydia B Chilton, Anna Kantosalo, Charles Patrick Martin, Greg Walsh (2022): GenAICHI: Generative AI and HCI, In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, doi:10.1145/3491101.3503719
gi.citations.elementJiao Sun, Q. Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, Justin D. Weisz (2022): Investigating Explainability of Generative AI for Code through Scenario-based Design, In: 27th International Conference on Intelligent User Interfaces, doi:10.1145/3490099.3511119
gi.citations.elementCindy Kaiying Lin, Steven J. Jackson (2023): From Bias to Repair: Error as a Site of Collaboration and Negotiation in Applied Data Science Work, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(7), doi:10.1145/3579607
gi.citations.elementTianling Yang, Milagros Miceli (2024): "Guilds" as Worker Empowerment and Control in a Chinese Data Work Platform, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(8), doi:10.1145/3686904
gi.citations.elementMarianne Aubin Le Quéré, Hope Schroeder, Casey Randazzo, Jie Gao, Ziv Epstein, Simon Tangi Perrault, David Mimno, Louise Barkhuus, Hanlin Li (2024): LLMs as Research Tools: Applications and Evaluations in HCI Data Work, In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613905.3636301
gi.citations.elementMichael Muller, Cecilia Aragon, Shion Guha, Marina Kogan, Gina Neff, Cathrine Seidelin, Katie Shilton, Anissa Tanweer (2020): Interrogating Data Science, In: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing, doi:10.1145/3406865.3418584
gi.citations.elementDevansh Saxena, Erhardt Graeff, Shion Guha, EunJeong Cheon, Pedro Reynolds-Cuéllar, Dawn Walker, Christoph Becker, Kenneth R. Fleischmann (2020): Collective Organizing and Social Responsibility at CSCW, In: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing, doi:10.1145/3406865.3418593
gi.citations.elementAdriana Alvarado Garcia, Ivana Feldfeber, Milagros Miceli, Saide Mobayed, Helena Suárez Val (2022): Crossing Data: Building Bridges with Activist and Academic Practices from and for Latin America (Cruzar datos: Tendiendo Puentes con Prácticas Activistas y Académicas desde y para América Latina), In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, doi:10.1145/3491101.3505222
gi.citations.elementScott Allen Cambo, Darren Gergle (2022): Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science, In: CHI Conference on Human Factors in Computing Systems, doi:10.1145/3491102.3501998
gi.citations.elementParikshit Narendra Mahalle, Gitanjali Rahul Shinde, Priya Dudhale Pise, Jyoti Yogesh Deshmukh (2021): Introduction to Data Science, In: Studies in Big Data, doi:10.1007/978-981-16-5160-1_1
gi.citations.elementAdriana Alvarado Garcia, Marisol Wong-Villacres, Milagros Miceli, Benjamín Hernández, Christopher A Le Dantec (2023): Mobilizing Social Media Data: Reflections of a Researcher Mediating between Data and Organization, In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3544548.3580916
gi.citations.elementMichael Muller, Anna Kantosalo, Mary Lou Maher, Charles Patrick Martin, Greg Walsh (2024): GenAICHI 2024: Generative AI and HCI at CHI 2024, In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613905.3636294
gi.citations.elementVictoria Chui, Jessica Pater, Tammy Toscos, Shion Guha (2023): Applying Human-Centered Data Science to Healthcare: Hyperlocal Modeling of COVID-19 Hospitalizations, In: Companion Proceedings of the 2023 ACM International Conference on Supporting Group Work, doi:10.1145/3565967.3570979
gi.citations.elementDavid Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, Felix Portnoy (2021): How AI Developers Overcome Communication Challenges in a Multidisciplinary Team, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(5), doi:10.1145/3449205
gi.citations.elementKathleen Pine, Claus Bossen, Naja Holten Møller, Milagros Miceli, Alex Jiahong Lu, Yunan Chen, Leah Horgan, Zhaoyuan Su, Gina Neff, Melissa Mazmanian (2022): Investigating Data Work Across Domains, In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, doi:10.1145/3491101.3503724
gi.citations.elementAnnabel Rothschild, Amanda Meng, Carl DiSalvo, Britney Johnson, Ben Rydal Shapiro, Betsy DiSalvo (2022): Interrogating Data Work as a Community of Practice, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(6), doi:10.1145/3555198
gi.citations.elementMichael Muller, Lydia B Chilton, Anna Kantosalo, Q. Vera Liao, Mary Lou Maher, Charles Patrick Martin, Greg Walsh (2023): GenAICHI 2023: Generative AI and HCI at CHI 2023, In: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3544549.3573794
gi.citations.elementKevin R. McKee (2024): Human Participants in AI Research: Ethics and Transparency in Practice, In: IEEE Transactions on Technology and Society 3(5), doi:10.1109/tts.2024.3446183
gi.citations.elementMilagros Miceli, Julian Posada, Tianling Yang (2022): Studying Up Machine Learning Data, In: Proceedings of the ACM on Human-Computer Interaction GROUP(6), doi:10.1145/3492853
gi.citations.elementTrine Rask Nielsen, Naja Holten Møller (2022): Data as a Lens for Understanding what Constitutes Credibility in Asylum Decision-making, In: Proceedings of the ACM on Human-Computer Interaction GROUP(6), doi:10.1145/3492825
gi.citations.elementAnissa Tanweer, Cecilia R Aragon, Michael Muller, Shion Guha, Samir Passi, Gina Neff, Marina Kogan (2022): Interrogating Human-centered Data Science: Taking Stock of Opportunities and Limitations, In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, doi:10.1145/3491101.3503740
gi.citations.elementDiane Linke, Claudia Müller-Birn (2024): Identifying Characteristics of Reflection Triggers in Data Science Ethics Education, In: Proceedings of Mensch und Computer 2024, doi:10.1145/3670653.3677486
gi.citations.elementDilruba Showkat, Angela D. R. Smith, Wang Lingqing, Alexandra To (2023): “Who is the right homeless client?”: Values in Algorithmic Homelessness Service Provision and Machine Learning Research, In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3544548.3581010
gi.citations.elementEmma Harvey, Hauke Sandhaus, Abigail Z. Jacobs, Emanuel Moss, Mona Sloane (2024): The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology, In: Proceedings of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613904.3642004
gi.citations.elementAlex H. Poole (2023): Data Flourishing: Developing <scp>Human‐Centered</scp> Data Science through Communities of Ethical Practice, In: Proceedings of the Association for Information Science and Technology 1(60), doi:10.1002/pra2.793
gi.citations.elementMichael Muller, Christine T. Wolf, Josh Andres, Michael Desmond, Narendra Nath Joshi, Zahra Ashktorab, Aabhas Sharma, Kristina Brimijoin, Qian Pan, Evelyn Duesterwald, Casey Dugan (2021): Designing Ground Truth and the Social Life of Labels, In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3411764.3445402
gi.citations.elementMichael Muller, Angelika Strohmayer (2022): Forgetting Practices in the Data Sciences, In: CHI Conference on Human Factors in Computing Systems, doi:10.1145/3491102.3517644
gi.citations.elementJu Yeon Jung, Tom Steinberger, Chaehan So (2023): Towards Actionable Data Science: Domain Experts as End-Users of Data Science Systems, In: Computer Supported Cooperative Work (CSCW) 3(33), doi:10.1007/s10606-023-09475-6
gi.citations.elementDilruba Showkat, Eric P. S. Baumer (2021): Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(5), doi:10.1145/3479534
gi.citations.elementRamaravind Kommiya Mothilal, Shion Guha, Syed Ishtiaque Ahmed (2024): Towards a Non-Ideal Methodological Framework for Responsible ML, In: Proceedings of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613904.3642501
gi.conference.locationSanibel Island, Florida, USA

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