# Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education arXiv: https://arxiv.org/abs/2311.05792 ยท HTML: https://mlsanity.com/paper/2311.05792/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 97 references that are in the map. - First posted on arXiv 9 Nov 2023. ## Builds on (most cited first, 30 of 32) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020) - [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018) - [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020) - [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021) - [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022) - [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020) - [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021) - [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020) - [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018) - [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020) - [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022) - [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019) - [Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices](https://mlsanity.com/paper/2006.12358/) (2020) - [StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement](https://mlsanity.com/paper/2202.06205/) (2022) - [How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?](https://mlsanity.com/paper/1909.03486/) (2019) - [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020) - [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021) - [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022) - [Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"](https://mlsanity.com/paper/2111.01122/) (2021) - [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022) - [Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects](https://mlsanity.com/paper/2002.03389/) (2020) - [Overcoming Failures of Imagination in AI Infused System Development and Deployment](https://mlsanity.com/paper/2011.13416/) (2020) - [Towards A Process Model for Co-Creating AI Experiences](https://mlsanity.com/paper/2104.07595/) (2021) - [Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks](https://mlsanity.com/paper/2102.12592/) (2021) - [Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations](https://mlsanity.com/paper/2006.04282/) (2020) - [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021) - [Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia](https://mlsanity.com/paper/2105.05849/) (2021) - [Characterizing machine learning process: A maturity framework](https://mlsanity.com/paper/1811.04871/) (2018) - [Talking datasets: Understanding data sensemaking behaviours](https://mlsanity.com/paper/1911.09041/) (2019) ## Built on by (most cited first, 5 of 5) - [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025) - [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026) - [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024) - [Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses](https://mlsanity.com/paper/2509.08862/) (2025) - [Harm is not Universal: Community-Specific Toxicity Detection is Urgently Needed](https://mlsanity.com/paper/2607.24898/) (2026) ## Abstract (arXiv) Despite the promises of ML in education, its adoption in the classroom has surfaced numerous issues regarding fairness, accountability, and transparency, as well as concerns about data privacy and student consent. A root cause of these issues is the lack of understanding of the complex dynamics of education, including teacher-student interactions, collaborative learning, and classroom environment. To overcome these challenges and fully utilize the potential of ML in education, software practitioners need to work closely with educators and students to fully understand the context of the data (the backbone of ML applications) and collaboratively define the ML data specifications. To gain a deeper understanding of such a collaborative process, we conduct ten co-design sessions with ML software practitioners, educators, and students. In the sessions, teachers and students work with ML engineers, UX designers, and legal practitioners to define dataset characteristics for a given ML application. We find that stakeholders contextualize data based on their domain and procedural knowledge, proactively design data requirements to mitigate downstream harms and data reliability concerns, and exhibit role-based collaborative strategies and contribution patterns. Further, we find that beyond a seat at the table, meaningful stakeholder participation in ML requires structured supports: defined processes for continuous iteration and co-evaluation, shared contextual data quality standards, and information scaffolds for both technical and non-technical stakeholders to traverse expertise boundaries.