# Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation

arXiv: https://arxiv.org/abs/2010.11411 · HTML: https://mlsanity.com/paper/2010.11411/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 24 of 24)

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- [Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence](https://mlsanity.com/paper/2111.00826/) (2021)
- [Value Alignment of Social Media Ranking Algorithms](https://mlsanity.com/paper/2509.14434/) (2025)
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- [Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research](https://mlsanity.com/paper/2406.04116/) (2024)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [Maestro: A Gamified Platform for Teaching AI Robustness](https://mlsanity.com/paper/2306.08238/) (2023)
- [AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages](https://mlsanity.com/paper/2505.10300/) (2025)
- [Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction](https://mlsanity.com/paper/2603.00078/) (2026)
- [AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation](https://mlsanity.com/paper/2604.09617/) (2026)

## Abstract (arXiv)

Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency, and Ethics. In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation. This paper presents an early use of our approach in a college-level computer science course. Through an in-class activity, we report empirical data for the initial effectiveness of our approach. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics and apply them in real-world contexts, help them recognize the significance of considering diverse social values in the development of deployment of algorithmic systems, and enable them to communicate, negotiate and synthesize the perspectives of diverse stakeholders. Our study also demonstrates a number of caveats we need to consider when using the different variants of the Value Cards toolkit. Finally, we discuss the challenges as well as future applications of our approach.