# Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development

arXiv: https://arxiv.org/abs/2501.11909 · HTML: https://mlsanity.com/paper/2501.11909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
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- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [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)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Trustworthy AI](https://mlsanity.com/paper/2011.02272/) (2020)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [Explainable AI via Learning to Optimize](https://mlsanity.com/paper/2204.14174/) (2022)
- [Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework](https://mlsanity.com/paper/2105.10197/) (2021)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [GAISSALabel: A tool for energy labeling of ML models](https://mlsanity.com/paper/2401.17150/) (2024)
- [Dependability in a Multi-tenant Multi-framework Deep Learning as-a-Service Platform](https://mlsanity.com/paper/1805.06801/) (2018)

## Abstract (arXiv)

As artificial intelligence (AI) becomes integral to economy and society, communication gaps between developers, users, and stakeholders hinder trust and informed decision-making. High-level AI labels, inspired by frameworks like EU energy labels, have been proposed to make the properties of AI models more transparent. Without requiring deep technical expertise, they can inform on the trade-off between predictive performance and resource efficiency. However, the practical benefits and limitations of AI labeling remain underexplored. This study evaluates AI labeling through qualitative interviews along four key research questions. Based on thematic analysis and inductive coding, we found a broad range of practitioners to be interested in AI labeling (RQ1). They see benefits for alleviating communication gaps and aiding non-expert decision-makers, however limitations, misunderstandings, and suggestions for improvement were also discussed (RQ2). Compared to other reporting formats, interviewees positively evaluated the reduced complexity of labels, increasing overall comprehensibility (RQ3). Trust was influenced most by usability and the credibility of the responsible labeling authority, with mixed preferences for self-certification versus third-party certification (RQ4). Our Insights highlight that AI labels pose a trade-off between simplicity and complexity, which could be resolved by developing customizable and interactive labeling frameworks to address diverse user needs. Transparent labeling of resource efficiency also nudged interviewee priorities towards paying more attention to sustainability aspects during AI development. This study validates AI labels as a valuable tool for enhancing trust and communication in AI, offering actionable guidelines for their refinement and standardization.