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

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A two-hour educational workshop can turn professionals into algorithmic transparency advocates, with participants reporting real advocacy actions such as speaking up at an AI strategy meeting.

desk verdict Honest small pilot on teaching transparency advocacy; useful taxonomy, but effectiveness claim overstates self-report evidence. read the letter →

arxiv 2412.15363 v1 pith:WBICAEMX submitted 2024-12-19 cs.CY cs.AIcs.HC

Andrew Bell , Julia Stoyanovich This is my paper · ORCID
classification cs.CYcs.AIcs.HC
keywords algorithmictransparencyExplainableAIresponsibleeducationadvocacytemperedradicalsliteracyorganizationalchange
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests whether a short educational workshop can create "transparency advocates"—employees who push their organizations toward greater algorithmic transparency from the ground up. Across two workshops with 27 professionals in news and media and technology startups, participants reported higher understanding of algorithmic transparency and greater willingness to advocate for it. Four participants described taking real advocacy actions in the days after the workshop, including speaking up at an organization-wide AI strategy meeting. The paper argues that advocacy is not a single behavior but occurs at conversational, implementational, and influential levels, and that domain context shapes both willingness and ability to advocate.

What carries the argument

The load-bearing mechanism is the two-hour workshop, built around five modules covering transparency definitions, tools (model cards, datasheets, explainer dashboards, Shapley values), the stakeholder-first Transparency Playbook, a role-playing breakout activity where participants argue for and against disclosure at a fictional startup, and common objections to transparency with rebuttals. The role-play is central because it rehearses the tensions participants will meet as advocates and equips them with counterarguments. A pre/post survey adapted from prior responsible data-science teaching measures six constructs, and semi-structured interviews capture reported actions.

What would settle it

A randomized evaluation in which employees within the same organizations are assigned to the workshop or to a placebo training, with advocacy actions measured from verifiable records such as meeting minutes, adoption of model cards or datasheets, emails, or project artifacts, would settle whether the workshop causes advocacy rather than merely accompanying it.

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Extended reading notes

Core claim

The paper claims that education can translate algorithmic transparency research into practice by equipping motivated individuals inside organizations with knowledge, tools, and argumentative strategies to advocate for transparency. The authors delivered a two-hour, open-source workshop built on a stakeholder-first transparency playbook, and their qualitative interviews and pre/post surveys indicate gains in both transparency literacy and willingness to advocate. The most notable reported outcome is a participant who raised transparency concerns at an organization-wide AI strategy meeting days after the workshop, directly applying the workshop's lessons. The paper also claims that advocacy has three levels—conversational, implementational, and influential—and that news and media professionals tend to be more willing but less equipped to act, while startup professionals have more technical means but less organizational room to prioritize transparency.

Load-bearing premise

The conclusions rest on participants' own unverified reports, in surveys and unrecorded interviews, that their understanding and advocacy increased, without a control group or external confirmation of the reported actions.

Editorial extensions

If this is right

  • Organizations can cultivate bottom-up pressure for algorithmic transparency through short, low-cost workshops, even in the absence of strong regulation.
  • Advocacy is multi-level: education should prepare people for conversational, implementational, and influential actions, since each requires different skills and authority.
  • Professional domain shapes advocacy, so training and support must be tailored: news and media professionals need tools, while startup professionals need resources and prioritization.
  • A single advocate can bring transparency onto a high-stakes organizational agenda, and the reported AI strategy meeting episode suggests a possible ripple effect on company practices.
  • Open-source workshop materials make the intervention replicable, allowing other organizations and educators to test and adapt it.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the effects replicate with larger samples, workshop-based advocate cultivation could complement regulation by creating internal demand for transparency that outlasts individual workshops.
  • A natural extension is a longitudinal study tracking whether reported advocacy translates into auditable organizational artifacts, such as published model cards or disclosure policies, months after training.
  • The level taxonomy suggests a testable prediction: training that matches advocacy level to role, such as engineers to implementational actions and managers to influential ones, will produce more sustained change than generic training.
  • Domain differences imply that startup-focused interventions may need to bundle transparency with business value, such as risk reduction or investor due diligence, rather than relying on ethical appeals alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper reports on a workshop-based educational intervention designed to create "transparency advocates" who can drive bottom-up organizational change toward algorithmic transparency. The authors delivered a two-hour workshop, developed over several years and based on their prior stakeholder-first transparency playbook, to two groups of professionals: 15 news/media professionals and 12 technology startup professionals. Data include pre/post workshop surveys (n=15 matched responses) and semi-structured follow-up interviews with 7 participants, which were not recorded but captured via detailed notes. The paper claims that the workshop increased participants' algorithmic transparency literacy and their willingness to advocate for transparency in their professional lives, and it proposes a three-level taxonomy of advocacy (conversational, implementational, influential) plus domain-specific differences in advocacy barriers. The authors openly acknowledge the small, self-selected sample and the absence of statistical testing, and they make all workshop materials publicly available.

Significance. If the central claims were established, this work would make a valuable contribution to responsible AI education and AI governance by offering a concrete, replicable, bottom-up mechanism for translating XAI research into organizational practice. The paper's strengths include a freely available, open-source workshop curriculum, a two-domain comparison, and a honest acknowledgment of key limitations (small sample, self-selection, descriptive statistics only). The proposed taxonomy of advocacy levels is a useful conceptual contribution, and the reported real-world actions (e.g., a participant speaking up at an organization-wide AI strategy meeting) are motivating examples. However, the evidentiary basis for the causal claim that the workshop increased literacy and advocacy willingness is fragile, resting primarily on self-report measures and unrecorded interviews; the paper's significance therefore hinges on how these evidential limitations are treated in the framing of the conclusions.

major comments (3)
  1. [Methods (Data Collection and Analysis); Table 3] The claim that the workshop increased algorithmic transparency literacy is not established by the quantitative evidence. The survey's literacy measure (Q5) is a self-rating of understanding on a 1-10 scale, not an objective knowledge test. The observed increase from 5.00 to 7.79 could reflect increased confidence or a shift in self-perception rather than actual knowledge gain. This ambiguity is especially acute because the qualitative results ("Uncovering knowledge gaps") indicate that participants realized they "didn't know what they didn't know," which should, if anything, bias post-workshop self-ratings downward; the paper does not address this tension. The authors should either temper the literacy claim to "perceived understanding" or supplement the self-report with an objective measure (e.g., a short knowledge quiz) in future iterations, and they should explicitly discuss the direction of self-report bias in the current study.
  2. [Results ("Taking action"); Discussion ("Levels of Advocacy")] The attribution of participants' advocacy actions to the workshop is not sufficiently supported. P2 is quoted as saying "I always would've advocated for transparency anyway," and the paper acknowledges that actions "appeared to be motivated, at least in part, by the workshop." This "at least in part" rests entirely on participants' own judgments, which are vulnerable to social desirability bias and hindsight bias. Without a counterfactual, a control group, or independent verification of the reported actions, the paper cannot rule out the possibility that participants were already inclined to advocate and would have acted identically without the workshop. The authors should either substantially soften the causal language in the abstract and conclusion, or provide a more rigorous analysis of the specific workshop elements that plausibly changed behavior (e.g., the role-playing activity's direct influence on P4's strategy-meeting intervention).
  3. [Methods (Data Collection and Analysis); Results (Thematic Analysis Findings)] The reliability of the qualitative findings is compromised by the decision not to record interviews. The paper states that "we chose not to record the interviews" and instead "took detailed notes," but it does not describe how the notes were verified (e.g., by member checking, participant review, or second-coder agreement). Because the thematic analysis was conducted by the authors on their own notes, there is a risk of selective note-taking and confirmatory interpretation. The paper should either provide evidence of note reliability or explicitly acknowledge this as a threat to the trustworthiness of the central qualitative claims, and it should explain how the 33 codes and 6 themes were audited beyond "two separate working sessions."
minor comments (5)
  1. [Discussion ("The Importance of Domain-of-use")] There is a typo: "we found vast differences in the attitudes towards algorithmic transparency in new and media vs. technology startups" should read "news and media."
  2. [Introduction; References] The name "Myerson" appears in the text, but the reference is to "Meyerson (2003)"; please use the correct spelling consistently throughout.
  3. [References] The reference for Covert et al. begins with "DBLP:journals/corr/abs-2004-00668" which appears to be a leftover identifier; this should be cleaned up.
  4. [Appendix (Figure 2)] The figure is referred to as "Appendix Figure 2" in the Methods section but is simply "Figure 2" in the appendix; please number it consistently.
  5. [Methods (Pre- and post-workshop surveys)] The paper states that the survey was "adapted from previous work by Lewis and Stoyanovich (2021)" but does not give details on which items were changed or validated for this context; adding this information would improve replicability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the workshop-effectiveness claims are empirical evaluations, not derivations that reduce to their inputs.

full rationale

This paper contains no derivation chain, equation, fitted parameter, or uniqueness theorem, so the enumerated circularity patterns do not apply. The central claims—that the workshop increased algorithmic transparency literacy and willingness to advocate—are empirical evaluations supported by pre/post surveys and semi-structured interviews. The survey was 'adapted from previous work by Lewis and Stoyanovich (2021)', a self-citation, but that is instrument provenance rather than a load-bearing result: the outcome of the workshop is not entailed by the survey's origin. Similarly, the workshop content draws on the authors' prior playbook, but testing one's own educational material is not circular; the evaluation could have failed, and the paper reports both positive and mixed participant responses. The acknowledged limitations—small sample size, optional participation bias, self-reported outcomes, and no control group—affect evidentiary strength and internal validity, not circularity. No quantity is defined in terms of another, no prediction is statistically forced by a fitted input, and no external result is imported via self-citation to forbid alternatives. The finding is therefore not circular.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims depend on methodological assumptions about self-report and attribution, not on free parameters or invented physical entities.

assumptions (3)
  • domain assumption Self-reported survey responses and interview accounts accurately reflect participants' transparency literacy and advocacy behavior.
    All outcome measures are self-report; interviews were not recorded, and no independent verification of advocacy actions was conducted. Used throughout Results.
  • domain assumption Changes in self-reported outcomes are attributable to the workshop rather than to other factors.
    There is no control group or randomization; the authors acknowledge this in the Conclusion when discussing scalability and generalizability.
  • domain assumption The authors' thematic analysis fairly represents participants' experiences.
    Thematic coding was performed manually by the research team without reporting inter-coder reliability or external audit, per Methods, Data Collection and Analysis.

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Cite this review

Pith. "Pith review of Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice." pith.science (2026). https://pith.science/paper/WBICAEMX

@misc{pith2026241215363,
  author       = {Pith},
  title        = {Pith review of: Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WBICAEMX}},
  note         = {Machine review of arXiv:2412.15363}
}
read the original abstract

Concerns about the risks and harms posed by artificial intelligence (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 creating 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 delivered the workshop to professionals across two separate domains 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 observations: 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 example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.

Figures

Figures reproduced from arXiv: 2412.15363 by the authors.

Figure 2
Figure 2. Completed breakout room activity from one of the four breakout rooms with media professionals. From [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

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Reference graph

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Reviewed August 11, 2026 · model on record in the stance chip above.