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Debiasing Decisions Toolkit

A community toolkit of checklists, canvases, and facilitation tools to help you recognise and counter cognitive bias in design, architecture, and leadership decisions.

Created by Evelyn van Kelle, Gien Verschatse, and Kenny Schwegler, co-authors of Collaborative Software Design (Manning, 2024).

License: CC BY-SA 4.0 Contributions Welcome

Why this toolkit exists

Every significant decision, a bounded context boundary, an architectural style, a hiring choice, a team structure, has a moment where one option just feels right. That powerful gut feeling is sometimes a genuinely good signal. But it is also sometimes a cognitive bias in disguise.

Research consistently shows that awareness of cognitive bias is not enough to counter it. Simply knowing about anchoring, overconfidence, or status quo bias does not reduce their influence on your decisions. What works is structured behavioural intervention: habits and prompts embedded directly into your workflow at the moment a decision is being made.

This toolkit is grounded in two independent research streams that reach the same conclusion:

  • Morewedge et al. demonstrated that targeted training interventions significantly reduce the effect of cognitive bias.
  • Soll, Milkman & Payne translated this into a practical five-step framework for everyday decision-making.

See: Choiceology's Guide to Better Decisions and Outsmart Your Own Biases (HBR, 2015).

We adopted their framework and extended it for design, architecture, and technical leadership decisions in the IT industry.


How to use this toolkit

Each tool in this toolkit is standalone and context-independent. You do not need to adopt everything — pick what fits your team's current workflow.

The most effective integration point is wherever your team already formalises decisions: Architecture Decision Records, design sessions, planning meetings, hiring panels, or team retrospectives. The goal is to embed the prompts into your existing process rather than adding a separate debiasing step.

Work through the checklist as a team, not individually. Cognitive bias is a collective problem as much as a personal one, and the habits work best when they create shared permission to slow down and question a decision together.


What currently exists

Tool Description Format
Five-Step Debiasing Checklist A checklist for recognising and countering cognitive bias across design, architecture, and leadership decisions Markdown (below)
ADR Template Snippet A copy-paste block to embed the debiasing check directly into your Architecture Decision Records Markdown (below)

More tools are in development — canvases, facilitation guides, and domain-specific extensions. Contributions are warmly welcomed.


The Five-Step Debiasing Checklist

1. Be Decision-Ready

Check that you are in the right mental state to make a good decision.

Question Bias addressed
Am I / are we deciding under emotional stress? Myopic Misery → Action Bias
Am I / are we defaulting to "keep it as-is" without genuinely evaluating alternatives? Status Quo Bias
Is the perceived cognitive load so high that doing nothing has become the path of least resistance? Status Quo Bias

2. Broaden the Frame

Check that you have explored the full solution space.

Question Bias addressed
Did we consider removing something instead of only adding? Additive Bias (Subtraction Neglect)
Did we explore unconventional uses of existing tools or concepts? Functional Fixedness (Golden Hammer)
Instead of asking "what is this?", did we ask "what is it made of, and what could those parts do?" Functional Fixedness
Did we ask "what does this concept actually know and do?" when we heard "service", "manager", or "handler"? Functional Fixedness

3. Seek Independent Advice

Check that the information you acted on was genuinely diverse.

Question Bias addressed
Did we get input from people who formed their opinions independently of each other? Correlation Neglect
Can we trace this decision back to multiple independent sources, or are we following repeated echoes of a single opinion? Correlation Neglect
Did we genuinely consider contradicting views rather than dismissing them? Overconfidence Bias
Is this option driven by overconfidence in our own skills, abilities, or expertise? Overconfidence Bias
If a junior team member had suggested this, what questions would we ask? Authority Bias

4. Test Your Assumptions

Check that you have stress-tested the decision against reality.

Question Bias addressed
Are we accepting this because of who or what suggested it, rather than evaluating it on its merits? (AI, senior architect, vendor) Authority Bias
What problem was this pattern, tool, or technology designed to solve — and do we actually have that problem? Authority Bias
What is our plan when this goes wrong? (Not if — when.) Illusion of Control
Are we designing around confidence rather than designing for failure? Illusion of Control

5. Establish Simple Rules

Check that your decision process was proportional to what is actually at stake.

Question Bias addressed
Is this a Two-Way Door? If it is reversible, have we delegated it rather than seeking group consensus? Law of Triviality (Bikeshedding)
Did we spend time proportional to how hard this decision is to reverse? Law of Triviality
What would make this conversation a waste of time? Have we avoided that? Law of Triviality
Did we check as a group rather than leaving debiasing to individuals alone? False Consensus Effect

ADR Template Snippet

Copy this block into the Decision section of your ADR:

### Debiasing Check

> Before finalising, work through the following as a team.
> Given our current mental state and cognitive load — should we be making this decision today?

**1. Be Decision-Ready**
- [ ] Are we deciding under time pressure or stress? (Myopic Misery)
- [ ] Are we defaulting to "keep it as-is" without evaluating alternatives? (Status Quo Bias)
- [ ] Is cognitive load making inaction the path of least resistance? (Status Quo Bias)

**2. Broaden the Frame**
- [ ] Did we consider removing something instead of only adding? (Additive Bias)
- [ ] Did we explore unconventional uses of existing tools? (Functional Fixedness)
- [ ] Did we ask what existing concepts actually know and do? (Functional Fixedness)

**3. Seek Independent Advice**
- [ ] Did we get input from people who formed opinions independently? (Correlation Neglect)
- [ ] Are we acting on diverse sources or repeated echoes? (Correlation Neglect)
- [ ] Did we genuinely consider contradicting views? (Overconfidence Bias)
- [ ] If a junior suggested this, what questions would we ask? (Authority Bias)

**4. Test Your Assumptions**
- [ ] Are we accepting this because of who/what suggested it vs. its merits? (Authority Bias)
- [ ] What problem was this designed to solve — do we actually have it? (Authority Bias)
- [ ] What is our plan when this goes wrong? (Illusion of Control)

**5. Establish Simple Rules**
- [ ] Is this a Two-Way Door? If so, have we delegated it? (Law of Triviality)
- [ ] Did we spend time proportional to reversibility? (Law of Triviality)
- [ ] What would make this conversation a waste of time? (Law of Triviality)
- [ ] Did the full group check collectively, not just individuals? (False Consensus Effect)

Cognitive Biases Reference

Bias What it does How it shows up in decisions
Myopic Misery Sadness or stress creates short-sighted focus on immediate resolution Rushing decisions during pressured sprints or incident response
Status Quo Bias Strong preference for current state when cognitive load is high "We've always done it this way" — avoiding refactoring or boundary redesign
Additive Bias Defaulting to adding rather than removing when solving problems Solving every challenge by adding a new service, layer, or process
Functional Fixedness Limiting tools and concepts to their traditional use Always reaching for the same pattern regardless of context (Golden Hammer)
Overconfidence Bias More confidence in skills and estimates than data supports Underestimating complexity, overestimating team familiarity with a technology
Correlation Neglect Mistaking frequency of an opinion for its accuracy Echo chambers in architecture guilds, Slack threads, or pull request reviews
Authority Bias Attributing greater accuracy to authority figures or AI Blindly trusting AI-generated code or senior architect proposals without scrutiny
Illusion of Control Overestimating ability to control chance-driven outcomes Assuming a design will behave as intended under real production conditions
Law of Triviality Disproportionate focus on trivial issues while ignoring complex ones Long debates on naming conventions while system boundaries go unexamined
False Consensus Effect Overestimating how widely your views are shared Assuming the team agrees on an approach without making disagreement safe

The Core Heuristic

When "this just feels right" appears in your decision-making — pause and ask:

Is this a genuinely good decision, or a bias in disguise?


Contributing

This toolkit is a living resource. We actively invite contributions from practitioners across software design, architecture, technical leadership, and beyond.

What we welcome:

  • Additional debiasing questions from your own practice
  • Domain-specific extensions (event-driven systems, AI-assisted development, platform engineering, hiring, team design)
  • New tools: canvases, facilitation guides, workshop formats
  • Translations
  • Experience reports of bias patterns you have observed in the wild

How to contribute: Please read CONTRIBUTING.md before opening a pull request.

The guiding principle for contributions: short, habit-forming questions or tools grounded in research or documented practice. We curate to keep the toolkit usable rather than exhaustive.


References

  • Kahneman, D. — Thinking, Fast and Slow
  • Soll, J., Milkman, K. & Payne, J. — Outsmart Your Own Biases (HBR, 2015)
  • Morewedge, C. et al. — debiasing training research
  • Lerner, J., Li, Y. & Weber, E. — The Financial Costs of Sadness (2013)
  • Adams, G., Converse, B., Hales, A. & Klotz, L. — People Systematically Overlook Subtractive Changes (Nature, 2021)
  • McCaffrey, T. — Innovation Relies on the Obscure (2012)
  • Logg, J., Minson, J. & Moore, D. — Algorithm Appreciation (2019)
  • Langer, E. — The Illusion of Control (1975)
  • Enke, B. & Zimmermann, F. — Correlation Neglect in Belief Formation (2017)
  • van Kelle, E., Verschatse, G. & Baas-Schwegler, K. — Collaborative Software Design (Manning, 2024)
  • Harmel-Law, A. — Facilitating Software Architecture (O'Reilly, 2023)

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material for any purpose, including commercially

Under the following terms:

  • Attribution — You must give appropriate credit to Evelyn van Kelle and Kenny Baas-Schwegler, provide a link to the license, and indicate if changes were made.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same CC-BY-SA 4.0 license.

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Structured behavioural interventions for better decisions in software, architecture, and technical leadership. Checklists, canvases, and more.

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