10. Call to Explore

Table of Contents

FallacyTag isn’t a pitch. It’s a public proposal—offered freely—for how we might surface reasoning more clearly in digital spaces.

The core bet is simple: visibility often helps, though sometimes it provokes defensiveness. When reasoning is surfaced in ways that invite rather than threaten, it becomes easier to refine, to reflect on, to teach—and, in doing so, to counter the structural erosion of coherent thought that proliferates in digital spaces through the architecture of avoidance. FallacyTag doesn’t directly improve inherent intelligence, nor does it enforce smarter thinking, and it’s not a moderator or a fact-checker. But it may help notice—and noticing is the entry point to deeper thinking, provided it’s paired with a framework—or even just a habit—that turns awareness into metacognition1.

I’m sharing this idea in the hope that others will critique it, build on it, or take it in new directions. If any part is useful—concept, framing, implementation sketch—you’re welcome to adapt it. If you disagree, I hope you’ll say so in public.

In the short term, I’ll be listening: watching for thoughtful responses, alternative prototypes, or expressions of interest. If energy builds, I’m open to helping incubate an open-source version. But that’s not required. The idea is bigger than any one path.

10.1 Invitations, by Audience

What happens next isn’t a roadmap. It’s an invitation:

Audience map

Figure: Audience map

10.1.1 Builders

Prototype freely.

  • Use the product line architecture in Section 6—with its intentional variability summarized in Appendix D—or discard it2. Start small—build a tagging layer for a single subreddit, a niche forum, or an LLM that flags just a few fallacy types. Begin by implementing a single integration point (e.g., a browser extension for a specific text-based forum) to validate the core pipeline, before tackling more complex modalities like video or audio, where delivery is more constrained.
  • Consider the trade-offs across the flow–friction spectrum detailed in Section 5.4, choosing modalities that offer the best trade-off between cognitive fit and delivery feasibility for early-stage deployments.
  • Test in real, noisy environments, not just lab conditions—keeping in mind the broader institutional and social resistances to automated feedback noted in Section 7.1, where earlier tools like Turnitin’s Revision Assistant saw adoption stall and were eventually phased out.
  • Evaluate fit—not only for technical accuracy, but for cultural and rhetorical resonance—using the feasibility criteria in Section 7 and the Scorecard in Section 9 as structured prompts.
  • Remember: the concept is feasible, but only within a narrow operational scope, with rigorous design and bounded expectations. Recall that FallacyTag encodes specific architectural constraints—deliberate tradeoffs that prioritize transparency, restraint, and trustworthiness. Your prototypes should reflect these commitments to ensure long-term viability and avoid inherent risks.
  • When you solicit user feedback, remember: in the reference design, it’s diagnostic, not adaptive—we don’t use it to retrain models or alter inference, to preserve interpretability and avoid bias drift. See Appendix C for why. When implementing the telemetry aggregator, design logging to capture not just dismissal rates, but also context-rich inference traces and UI interaction patterns. This data will be crucial for human reviewers to adjust prompts and heuristics effectively without compromising privacy or autonomy.

10.1.2 Educators

Borrow selectively.

  • Use fallacy tagging as a teaching lens—manual or automated—but consider starting with manual tagging to build deeper processing before introducing automated cues.
  • Let your students find the system’s edge cases before the critics do, including how “valid reasoning”—defined in their terms—may look different across languages, disciplines, and cultures. Beyond identifying edge cases, please encourage students to debate the contextual applicability of tags or to propose alternative interpretations of reasoning patterns in their work or observed discourse. This transforms system limitations into rich learning opportunities about the nuances of informal logic and rhetorical traditions.
  • Remember that the system’s tags are framed as low-cost prompts for cognitive activation—signals meant to foster reflection rather than deliver definitive error marking (Section 7.2). For educators, the emphasis should be on using FallacyTag as a tool for metacognitive monitoring, prompting awareness of reasoning itself rather than offering evaluative feedback. This gentle nudge invites students to reflect on how they think, reducing the psychological reactance often triggered by direct correction and fostering self-regulation. The design deliberately aims for productive discomfort (Section 3.3): engaging learners with their reasoning on their terms, rather than delivering a punitive “you’re wrong”. By starting here, students can internalize the underlying reasoning patterns and strengthen metacognitive awareness before relying on automated scaffolding, laying the groundwork for a deeper, more transferable grasp of argumentation principles.

10.1.3 Designers

Make the invisible visible—without making it oppressive. Your work determines whether FallacyTag feels like an invitation or an intrusion. Shape interfaces that reveal reasoning structure without stealing the conversation’s flow. Borrow from information design, game UX, and learning sciences:

  • Clarity without intimidation – Use visual hierarchy to make tags legible but not overwhelming; afford quick dismissal or collapse.
  • Contextual subtlety – Match the visual tone to the environment (e.g., academic forums vs. social media threads).
  • Nudge craft – Reinforce productive discomfort (Section 3.3) by designing interactions that encourage reflection without triggering defensiveness.
  • Cross-cultural fit – Build affordances for local adaptation in layout, color, and iconography to avoid imposing a single visual/interaction norm.

Sound design here is not just cosmetic—it’s part of the system’s interpretive humility. Done well, it keeps the tool’s prompts low-cost and cognitively activating (Section 7.2) while minimizing the risk of enforcement drift.

10.1.4 Critics

You’re essential. This capability is easy to overreach and hard to retract.

  • Push for transparency, local control, and interpretive humility.
  • We propose FallacyTag as a means to disrupt the architecture of avoidance by making reasoning more visible. Critics are invited to rigorously examine if this tool genuinely fosters deeper, more effortful thinking or if it risks creating new “production deficiencies” where users rely on the tool rather than internalizing reasoning patterns independently.
  • Guard against reinforcing dominant cultural or epistemic norms. What counts as “good reasoning” can differ across communities and contexts, and as discussed in Section 8.1, FallacyTag must navigate such interpretive ambiguities—spanning cultural divergence and genre variation—with humility. Those differences are not a problem to erase, but a reason the system is deliberately scoped: the architectural tradeoffs in Section 6.2 and the constraints in Section 7.5—across spectrums like Interpretability ↔ Expressive Power and Reflection ↔ Enforcement Drift—are not limitations to regret but design principles that protect trust. This includes designing for productive discomfort (Section 3.3)—an intentional nudge that prompts engagement without coercion, embodying the project’s ethics-of-intervention and preserving structural humility.
  • FallacyTag is not a moderator or a fact-checker, and it should never be mistaken for either—its role is to surface reasoning structure, not to adjudicate truth or enforce norms.
  • While feedback is invited, it’s intentionally diagnostic, not adaptive—its purpose is to inform future prompt versioning, guide interface tuning, and support regression testing, all within human-in-the-loop processes (see Sections 6.1 and 7.1), and never to retrain models or alter inference directly. This separation exists precisely to avoid the ambiguity, social bias, and tribal rejection risks discussed in Appendix C.
  • Be ruthless, but don’t be paralytic—exploration needs dissent as much as it requires code.

Some will reach for classroom tools. Others might imagine browser extensions, comment-layer overlays, or integrated LLM feedback loops. I’m especially interested in experiments that center user consent, clarity, nuance, and respect for diverse reasoning traditions.

We now have the technical tools to support better reasoning online. The question is whether we’ll use them well.

So: take what’s useful. Leave the rest. Build something sharper. Share what fails. Share what works.

FallacyTag is just one shape. Others might work better. Or explode. Either way: send notes.

10.2 Questions Worth Studying

And for those in research—students, advisors, lab leads—FallacyTag opens more than a product space. It opens questions. Real ones. Unsettled ones:

  • Can sentence-level fallacy detection achieve reliability in messy, real-world discourse?
  • How do phrasing and tone affect user acceptance of logic cues?
  • When does feedback lead to reflection—and when does it escalate conflict?
  • Could tag disagreement itself become a teaching lens?
  • How do reasoning visibility tools interact with different rhetorical traditions and epistemic norms in global discourse?
  • How can reasoning visibility tools disrupt the architecture of avoidance in digital discourse—counteracting the positive feedback loops that surface, incentivize, and reward shallow or evasive thinking?
  • What scaffolding helps users not just respond to tags, but internalize reasoning patterns?
  • What are the long-term ethical implications for individual intellectual autonomy and collective discourse when reasoning is regularly scaffolded by AI, even gently?
  • These aren’t tweaks to an interface—they’re foundations for new forms of reflective tooling. If FallacyTag finds traction, it will be because others test these boundaries—critically, empirically, and in public.

The deeper challenge remains:

How do we help people see the shape of their thinking—before it breaks?

Previous

  1. Following Flavell’s (1979) seminal definition, metacognition encompasses both knowledge about one’s cognitive processes and the regulation of those processes through planning, monitoring, and evaluating. Within this framework, monitoring—the awareness of a phenomenon or one’s performance—can occur without adequate regulation. As Veenman, Van Hout-Wolters, and Afflerbach (2006) note, learners may possess relevant metacognitive knowledge yet fail to apply it, a situation termed a production deficiency, where a person knows what to do but fails to act on that knowledge. In such cases, awareness alone does not translate into improved learning without an interpretive framework and strategies for action. Research on formative feedback suggests that the framing of visibility matters: cues perceived as evaluative or threatening can trigger reactance, while cues framed as supportive can foster openness and self-regulation (Kluger & DeNisi, 1996; Nicol & Macfarlane‐Dick, 2006) —a dynamic highly relevant to how reasoning visibility tools like FallacyTag are received. ↩

  2. If you choose to diverge significantly from the product line architecture, consider the potential trade-offs in terms of modularity, testability, and future extensibility. ↩