Why We Never Allowed Overrides

Ops teams constantly wanted to override the algorithm. "Obviously courier A should take this order." They could see the map. The courier was right there. The decision looked wrong.

We never allowed it.

This sounds like arrogance. Or control obsession. Or indifference to the people doing the actual work. It was none of those. It was a design choice about where human judgment should enter an algorithmic system.

The Visibility Gap

The dispatcher looks at a map and sees a courier nearby. The decision seems obvious. But they're evaluating two or three constraints: proximity, availability, maybe vehicle type.

The algorithm evaluates dozens. Time windows for every active order. Vehicle capacity. Courier fatigue limits. Earnings balance across the shift. Labor law constraints. Predicted future demand. Return time to zone. Downstream effects on every other order. Customer satisfaction targets. ETA reliability commitments.

And critically: trade-off management. The cheapest route isn't always the best route. A decision that looks suboptimal on cost might be optimal when you weigh service quality, courier experience, and operational sustainability together. At 500K orders per day, you're not optimizing one dimension. You're balancing competing objectives continuously.

The override that looks "obviously better" on one visible constraint often breaks trade-offs you can't see from a map view. This isn't a criticism of dispatchers. It's a statement about what's visible from different positions in the system.

Three Places Human Judgment Can Enter

When people talk about "human oversight" of algorithms, they usually mean override capability. But that's only one of three entry points.

Output layer. Override individual decisions. This is what most people picture.

Input layer. Improve the constraints, data, and parameters the algorithm uses. Map data gets corrected. Prep time estimates get refined. New constraints get added.

Feedback layer. Flag patterns and trigger investigation. The human notices something seems off, the team investigates, the system learns.

Most organizations default to the output layer. It's intuitive. It feels like control. But it's the weakest form of oversight. The human fixes one decision. The system remains unchanged. The same failure happens again tomorrow.

Input and feedback layer interventions compound. When a dispatcher's local knowledge improves the map data, that knowledge lives in the system permanently. The human's insight didn't fix one order. It fixed a category of orders, forever.

What Override Culture Actually Creates

When you give ops teams an override button, two things happen over time.

The system stops learning. Every override is a patch, not a fix. The underlying problem remains. You accumulate invisible technical debt: a growing gap between what the algorithm thinks is true and what's actually happening in the field.

The algorithm becomes something to work around, not work with. When overrides are available and easy, the rational response to friction is to override and move on. This isn't a failure of ops teams. It's a predictable outcome of system design. The path of least resistance leads away from system learning.

Medical clinical decision support systems illustrate this pattern at scale. A 2020 systematic review found override rates between 90% and 96% across 23 studies [1]. When researchers investigated the appropriateness of these overrides, they found the majority were clinically justified. One study at Brigham and Women's Hospital found over 90% of dose-alert overrides were appropriate [2]. The alerts were often irrelevant. The physicians were right to ignore them.

The problem wasn't doctors ignoring good advice. The problem was that the system wasn't learning from override patterns. The overrides became permanent workarounds. Years later, the same inappropriate alerts were still firing, still being overridden.

Override capability without a feedback loop is the worst of both worlds. You get the friction with none of the learning.

What We Did Instead

When someone flagged a decision that seemed wrong, our response wasn't "trust the algorithm." It was "let us check the data."

Not reactive, waiting for ops to build a case. Proactive: we investigate whether the signal reveals something real.

One case that stuck with me: ops spotted routes in a specific area that looked illogical. Couriers looping around when a direct route existed. Instead of overriding individual orders, they flagged the pattern.

We investigated. The maps didn't know about a train crossing. Once we understood the root cause, we fixed the map data. That fix helped every future order in that zone. Not just the one order they wanted to override.

The dispatcher's local knowledge entered at the input layer, not the output layer. It compounded.

Not every "this feels wrong" moment means the algorithm is broken. Investigation might reveal a genuine gap we fix. Or a constraint ops wasn't aware of, and the conversation builds shared understanding. Or a constraint that no longer serves current business objectives, and we revisit whether it should still exist.

The value isn't in proving who's right. It's in treating operational friction as signal worth investigating.

Design Choices, Not Doctrine

The choice isn't binary: full override capability or none.

Constrained overrides. The human chooses among algorithm-generated alternatives. Human judgment enters, but within a solution space the algorithm has already validated.

Smart escalation. Override triggers mandatory review. The override becomes a feedback signal, not a dead end.

Tiered authority. Different override levels for different decision types.

And sometimes, override is the right product decision. If the edge case is rare, the comprehensive fix expensive, and the override cheap, building the perfect algorithmic solution is over-engineering. A well-designed override with a feedback mechanism might be exactly right.

Two questions shape where you land: How fast can you realistically fix root causes? And what's the cost of a wrong algorithmic decision versus the cost of override?

The thesis isn't "overrides are always wrong." It's this:

Overrides without feedback loops are always wrong. You've chosen not to learn.

Defaulting to overrides when you could fix root causes is choosing a patched system over a learning system.

But sometimes the patch is the right product decision. Make that choice deliberately, not by default.

Where Knowledge Should Enter

The question isn't whether humans should have input into algorithmic systems. They should. Local knowledge matters. Operational intuition catches things data misses.

The question is where that input enters, and whether it creates a loop back into the system.

Override buttons are easy to build. They feel like control. But without a feedback mechanism, they're a design failure dressed up as a feature. You've given people a way to work around the system without giving the system a way to learn from them.

We never allowed overrides. Not because we thought the algorithm was always right. It wasn't. Not because we didn't trust ops teams. We did. We didn't allow overrides because we wanted their knowledge to compound. We wanted a system that got smarter over time, not one that got patched.

Sometimes the patch is the right call. But make that choice knowing what you're choosing.

References

[1] Poly TN, Islam MM, Yang HC, Li YC. Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review. JMIR Medical Informatics. 2020;8(7):e15653. https://medinform.jmir.org/2020/7/e15653

[2] Wong A, Rehr C, Seger DL, et al. Evaluation of Harm Associated with High Dose-Range Clinical Decision Support Overrides in the Intensive Care Unit. Drug Safety. 2019;42(4):573-579.