Why the Safe Choices Stifle Innovation
How Bias-Aware Assessment (BAA) Uncovers the Innovators We’ve Been Overlooking
Having observed hiring processes and knowledge assessments for decades, one stark reality has emerged: the processes prioritise predictability over potential. Our systems, whether in education or hiring, are designed to select the most ‘reasonable’ candidates — those who tick the preferred boxes, those who look good on paper, and those who likely won’t disrupt the status quo. However, this approach should be questioned in an era where innovation is necessary for survival — on an individual, corporate, and national level.
AI, automation, and rapid technological change are reshaping industries at a pace that no one can fully predict. Yet, we still rely on outdated assessment models prioritising safe, middle-of-the-road choices over individuals who can adapt, challenge, and create.
That’s why I developed the Bias-Aware Assessment (BAA) — a new approach designed to measure what matters: the ability to navigate uncertainty, challenge assumptions, and drive innovation.
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Why Traditional Assessments Fail Us
Consider knowledge assessments. They have barely evolved from their 19th-century origins. We still test people on what they can remember, not what they can do with that knowledge. Exams based on memorisation do not measure the ability to apply information in real-world situations or adapt to new and conflicting data. Yet, despite their limitations in assessing practical skills and adaptability, we continue to use them as a benchmark of competence.
Then there’s hiring. Most hiring decisions reward the candidate who ‘fits the mould’ best — not the one most likely to challenge outdated thinking or introduce new perspectives. This approach may feel low-risk but is often the most significant risk. Organisations that hire for predictability end up stagnating. If we want to build companies, schools, and teams that thrive in an unpredictable world, we need a new approach — one that prioritises adaptability over memorisation, critical thinking over compliance, and dynamic decision-making over fixed scoring systems. The need for this change is more than a suggestion, it’s an urgent call to action to cope with dramatic societal change.
Bias-Aware Assessment (BAA) Measures What Matters
Instead of relying on static scores or rigid evaluation criteria, BAA takes a different approach. Inspired by the ELO prioritisation model, it uses comparative judgment — pairwise comparisons — to assess candidates dynamically.
The ELO rating system, initially developed by Hungarian-American physics professor Arpad Elo to rank chess players, assigns a numerical rating to each item — in this case, product features — to reflect their relative strength or value. Each feature starts with an initial rating, then features are compared head-to-head through a series of pairwise matchups — one against the other, as in a tournament. After each comparison, ratings are adjusted: The winning feature’s rating increases while the losing feature’s rating decreases. Over multiple comparisons, Elo ratings naturally evolve to reflect an intuitive yet mathematically sound ranking that aligns closely with user judgments.
Here’s how it works:
1️⃣ Knowledge Assessment: Beyond Retention Instead of testing what someone remembers, BAA evaluates how well they can apply knowledge in novel scenarios. For example, instead of asking: “What year did the Industrial Revolution begin?” (A fact that can be Googled in seconds) BAA presents a scenario: “Two economic policies were introduced during the Industrial Revolution — one emphasising free markets, the other strict labour protections. Given global economic challenges, which policy model would be more applicable, and why?” There’s no single “right” answer. What matters is how well the individual justifies their reasoning, weighs trade-offs, and considers multiple perspectives.
How BAA Avoids Subconscious Bias in Knowledge Assessment
a). Moves Away from “One-Size-Fits-All” Grading — Traditional assessments often reward answers that align with the expected, dominant narrative — reinforcing the examiner’s bias or the prevailing academic framework. — BAA doesn’t grade based on a predefined “correct” response. Instead, it ranks responses against each other, ensuring the most well-reasoned arguments rise to the top rather than the safest or most expected ones.
b). Forces Explicit Trade-Offs in Judgment — BAA asks the test-taker to compare multiple responses instead of presenting a single scenario and grading a response. — Example in Action: — Candidate A argues that strict labour protections are better for modern economies, citing evidence from labour economics. — Candidate B argues that free markets drive greater long-term prosperity, referencing innovation trends. — Candidate C offers a hybrid model balancing both. — Instead of simply grading these responses against a fixed rubric, BAA forces a direct comparison: “Which argument presents the strongest case and why?”
This step forces deeper cognitive engagement and reflection, helping to surface bias. If an assessor consistently ranks one ideological viewpoint higher than others, BAA can detect that pattern and flag it as a bias indicator, reassuring fairness in the assessment process.
c). Uses Dynamic Ranking Instead of Static Scores — BAA employs a continuous ranking system (inspired by Elo) where strong arguments naturally rise through repeated comparisons rather than a single grading moment. — This reduces bias from first impressions or dominant social narratives — an argument that consistently holds up under multiple comparisons gains credibility over time rather than being judged in isolation.
d). Encourages Diversity of Thinking — Instead of rewarding safe, middle-of-the-road responses, BAA recognises individuals who demonstrate:
✅ Cognitive flexibility — willingness to challenge their assumptions.
✅ Trade-off awareness — the ability to recognise the benefits and limitations of any decision.
✅ Bias reflection — demonstrating awareness of their own subconscious preferences in decision-making. — Example: If a candidate consistently favours one economic policy over the other, BAA can surface that pattern and prompt them to defend their reasoning against counterarguments.
2️⃣ Hiring Assessment: Eliminating the Safe Candidate Bias
Traditional hiring assessments reward candidates who follow established career paths and give predictable, rehearsed interview answers. BAA disrupts this by forcing trade-offs in real-world decision scenarios.
Example Scenario: Your company is losing market share due to outdated technology. As a department head, you have the option to: — A) Maintain the current process to avoid disruption (Safe but stagnant) — B) Implement a small incremental change (Middle-of-the-road) — C) Challenge leadership to overhaul the system, even if it causes temporary instability (Bias-Aware, Innovation-Driven)
Traditional hiring panels tend to favour candidates who pick B — safe enough to seem proactive but not bold enough to take risks. But BAA is designed to identify the people who consistently demonstrate the ability to think ahead, make complex trade-offs, and push for meaningful change, challenging the status quo and driving innovation.
How BAA Surfaces Brave Decision-Makers
We won’t have a future if all we do is play it safe. Our collective future belongs to those who make brave choices — individuals who step forward when uncertainty looms, take strategic risks when others hesitate, and challenge assumptions rather than accept them.
For over two hundred years, our assessment models have rewarded predictability over courage, favouring candidates who make the safest choices rather than the boldest.
BAA changes that. It’s not designed to identify the person who always picks the least disruptive option. It’s designed to find the people who can stand at the crossroads of risk and reward and make the brave call — even when that call means venturing into uncharted territory.
1. Identifying Those Who Make Tough Trade-Offs
In a world where uncertainty is the norm, the ability to balance competing priorities is a vital skill. But most assessments don’t test for that. BAA does. Instead of asking, “What is the correct answer?” it asks, “Which path forward is the most strategic, and why?”
For example, a traditional hiring process might ask a candidate how they would implement a cost-cutting measure. On the other hand, a BAA-driven assessment would present two competing solutions — one that maximizes long-term innovation but introduces short-term instability, and another that preserves stability but limits growth — and ask the candidate to justify their choice. This approach separates those who can think in trade-offs from those who choose the safest path.
2. Challenging Status Quo Thinkers
People who challenge assumptions drive progress. Yet, traditional hiring and assessment methods tend to reward those who can recite industry best practices, rather than those who question whether those best practices are still relevant.
BAA forces candidates into situations where they must defend or rethink established norms. It places them in scenarios where conventional wisdom doesn’t provide an easy answer. A candidate who thrives in this environment isn’t just knowledgeable — they’re adaptable, resilient, and willing to push beyond “the way things have always been done.”
3. Rewarding Strategic Risk-Takers
Not all risks are reckless. Some are calculated moves toward a better future. BAA identifies those who can distinguish between impulsive decisions and strategic risks that lead to progress.
Imagine a leadership candidate faced with a choice: Stick with an outdated product because it’s safe, or advocate for an overhaul despite potential resistance. A traditional hiring process might look for their ability to defend their decision. But BAA looks for something more profound: Can they anticipate the long-term consequences of inaction? Can they weigh the costs of risk against the costs of stagnation? The candidates who can do this aren’t just decision-makers. They’re leaders.
The Case for Adaptability Over Resistance
History shows that those who adapt thrive whenever a disruptive technology emerges, while those who resist stagnate. The printing press, which once threatened scribes, became the backbone of modern knowledge dissemination. GPS changed the wayfinding skills of travellers but unlocked new cognitive efficiencies. AI is now challenging traditional knowledge work, forcing a shift from memorisation to evaluation, from execution to oversight.
The same principle applies to hiring and assessment. We can’t afford to keep hiring for predictability when the world demands adaptability with a dash of courage. BAA is designed to find those who embrace change, challenge assumptions, and demonstrate resilience in uncertainty. Embracing adaptability is vital and a necessity for future success.
Prioritising adaptability over resistance is no longer optional — it’s essential for survival. The urgency of this shift is clear, and Bias-Aware Assessment (BAA) is the tool we need to make it happen.
Three Reasons for Bias-Aware Hiring & Learning
- Safe candidates won’t build the future.
- Predictable hires won’t effectively navigate disruption.
- Traditional knowledge tests won’t prepare students for a world where AI knows every answer.
We need a dynamic, evolving assessment model that values innovation, adaptability, and critical thinking over rote memorisation and static evaluation criteria. Bias-Aware Assessment (BAA) has the potential to be that model, ushering in a new era of assessment and enhanced performance.
By replacing outdated, one-dimensional assessment methods with comparative judgment and dynamic ranking, we can finally start identifying the people who do more than fit the mould — they reshape it.
I plan to test this model in hiring and knowledge assessment scenarios, comparing BAA-based results to traditional assessment outcomes. If we can prove that Bias-Aware candidates outperform their middle-of-the-road counterparts in real-world innovation, we’ll have a compelling case for change.
Our collective future must be in the hands of those who challenge assumptions. It’s time we started hiring and assessing for that.
About the author:
📌 Greg Twemlow, Founder of XperientialAI & Designer of the Fusion Bridge
XperientialAI: AI-powered learning for leaders, educators, and organisations.
Fusion Bridge: My latest work — building AI-enabled frameworks for innovation & leadership.
🌎 Read more of my 300+ articles → https://gregtwemlow.medium.com/
📧 Contact: greg@xperiential.ai or greg@fusionbridge.org
