Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice

Source publication / research team or educational organization described in paper

USA, United States

Researchers, educators, instructors, or facilitators as described in the source publication

• Professional / adult learning

Workshop / professional learning activity

Not specified in extracted text

and Stoyanovich 2023). While these initiatives have been primarily focused on K-12 students and emphasized the technical aspects of AI (i.e., computer programming) (Dom´ınguez Figaredo and Stoyanovich 2023; Will

Explainable AI / robustness, Ethics / responsible AI

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Adult / workforce

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Concerns about the risks and harms posed by artificial intelli- gence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI).

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Explainable AI / robustness, Ethics / responsible AI

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Ethics / responsible AI education.
• AI capability focus: Explainable AI / robustness, Ethics / responsible AI.

• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Hands-on / experiential learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• In this work, we test an approach for addressing the challenge by creat- ing transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency.

• In this work, we test an approach for addressing the challenge by creat- ing transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency.
• We de- livered the workshop to professionals across two separate do- mains to improve their algorithmic transparency literacy and willingness to advocate for change.

Concerns about the risks and harms posed by artificial intelli- gence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations.

• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• AI literacy
• Ethics and responsible use
• Ethics / responsible AI education
• Explainable AI / robustness
• Ethics / responsible AI

• Completed

Adult / workforce

Professional / adult learning

Explainable AI / robustness, Ethics / responsible AI

Hands-on / experiential learning

Medium

Low to Medium

Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice

• Andrew Bell
• Julia Stoyanovich

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35165

https://ojs.aaai.org/index.php/AAAI/article/view/35165

https://ojs.aaai.org/index.php/AAAI/article/view/35165/37320

002_Making Transparency Advocates_ An Educational Approach Towards Better Algorithmic Transparency in Practice.pdf

9

Concerns about the risks and harms posed by artificial intelli- gence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creat- ing transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency. Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We de- livered the workshop to professionals across two separate do- mains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization- wide AI strategy meeting. We also make two broader obser- vations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals’ willingness for advocacy is affected by their professional field. For ex- ample, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.

• Professional / adult learning

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.