Automation Without a Falsifier Is Just Expensive Hope
Every result can be explained as progress. The team keeps adding scope because nobody wrote down what would count as a miss before the result arrived.
The recognizable symptom
The project has a roadmap, a growing prompt library, and a confident narrative. It does not have a frozen primary metric, a comparison, or a sentence that says when to stop.
The mechanism underneath
When the success condition stays vague, every observation can be recruited to defend the original position. This is how a small experiment becomes an unbounded implementation program.
What we built to contain it
We publish conviction calls before outcomes, log misses with the same weight as hits, and keep exploratory robustness separate from the frozen primary estimate. A useful audit should be able to conclude stop.
Example: defining a falsifier and frozen primary metric
Suppose your AI project automates customer support ticket triage. A frozen primary metric could be: 'Median time from ticket creation to first human response, measured in the production environment, using the same timestamp fields as the baseline.' Freeze the baseline: compute the median over the 90 days before deployment, say 12 minutes. Pre-register the falsifier: 'If after 30 days of live operation, the median response time is not at least 20% lower than baseline (i.e., ≤9.6 minutes), we will stop the automation and revert to manual triage.' That's a falsifier because it specifies a measurable threshold, a time window, and a decision rule. You must commit to it before seeing results. In practice, you'd also define how you handle outliers (e.g., exclude tickets with >24h delay due to system outages) and ensure the measurement is identical. The frozen metric is not a dashboard of many KPIs; it's the single number that triggers the stop decision. All other metrics (cost, accuracy, user satisfaction) are secondary and cannot override the primary. A limitation: this works when you have a reliable quantitative outcome. For tasks like 'improve content quality' where no numeric proxy exists, you'd need to construct a rubric and pre-define a minimum score, but that's more subjective and harder to freeze.
Pre-committing to a qualitative falsifier: the anchored rubric and baseline distribution
For qualitative outcomes like content quality or user trust, you can still pre-commit honestly by anchoring the rubric and freezing a baseline distribution. Here's a concrete pattern. Step 1: Define a 5-point rubric with explicit anchor examples for each score. For content quality, score 3 might be 'accurate but generic', score 5 'insightful and specific to the user's context'. Write these anchors before seeing any AI output. Step 2: Assemble a fixed panel of three evaluators who will score all samples. Step 3: Before deployment, have the panel score a random sample of 20 outputs from your current manual process. Record the median and the proportion of scores ≥3. Suppose the baseline median is 3.2 and 65% of scores are ≥3. Step 4: Pre-register the falsifier: 'If after 30 days of live operation, the median score from the same panel on a new random sample of 20 outputs is not at least 0.5 points higher than baseline (i.e., ≥3.7), and the proportion of scores ≥3 is not at least 75%, we stop and revert.' This is honest because the threshold is set before results, the rubric is anchored, and the panel is fixed. To guard against evaluator drift, compute inter-rater reliability (e.g., Cohen's kappa) on a shared set of 5 calibration samples each week; if kappa drops below 0.6, pause and retrain. A bounded limitation: this still depends on evaluator judgment, and for trust you may need a longitudinal study (e.g., repeated interactions) that cannot be falsified in 30 days. In that case, you must accept a slower stop decision or choose a proxy like task completion rate, but you cannot have both speed and full fidelity.
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