Explainable AI: The Safety Layer Energy Systems Need

Most AI failures in safety-critical systems aren’t “mysterious.” They’re uninspected decisions—outputs that look plausible until they’re wrong in exactly the way you didn’t anticipate.

That’s why a small detail from autonomous vehicle research should matter to anyone building AI for energy and utilities: safer autonomy often comes down to asking the system the right questions, at the right time, in the right format.

Self-driving cars are under intense public scrutiny, but the same trust problem exists in power systems—just with different consequences. In December, when peaks are sharp, crews are stretched, and regulators are watching reliability metrics, nobody wants a black-box model quietly steering operational decisions. If AI is going to influence dispatch, switching, inspections, or asset health, it needs to explain itself well enough that humans can intervene before the bad outcome.

This post is part of our AI in Robotics & Automation series, and the theme carries cleanly into energy: autonomous agents and automated decision loops only earn their place when they’re understandable, testable, and safe.

Explainable AI isn’t a “nice-to-have” anymore

Explainable AI (XAI) is the practice of interrogating models so humans can understand why a decision was made and where it could be wrong. In autonomous vehicles, that interrogation can happen in real time (for driver handover) or after an incident (for debugging and accountability).

Energy and utilities are heading down the same road. Grid operations, DER orchestration, vegetation management, substation monitoring, and predictive maintenance are increasingly automated. The operational loop is tighter than it used to be—milliseconds for inverter controls, minutes for dispatch, days for maintenance scheduling.

When AI is embedded into those loops, two things become true:

1. Mistakes are inevitable. Not because teams are careless, but because the real world changes, sensors fail, and adversarial edge cases exist.
2. Silent mistakes are unacceptable. If a model can’t express uncertainty or justify a recommendation, operators can’t safely rely on it.

In automotive research, one researcher described autonomous driving as a general “black box.” That’s familiar to anyone who’s watched a complex model score a transformer risk higher than expected and had to answer the uncomfortable question: “Okay—why?”

The trust gap is operational, not philosophical

Trust isn’t built by telling stakeholders that a model has high accuracy. Trust is built when:

• Operators can predict how the model behaves
• Engineers can reproduce and diagnose failures
• Leaders can audit decisions after incidents
• Regulators and insurers can see a defensible rationale

That’s exactly the motivation behind explainability work in autonomous vehicles—and it’s exactly what utilities need as they automate more of the grid.

“The speed limit is 85 mph” — why the wrong explanation can save you

One striking example in the autonomous vehicle world: researchers modified a 35 mph speed limit sign with a small sticker that distorted the “3.” The vehicle interpreted it as 85 mph and accelerated.

Here’s the part energy teams should steal: the proposal wasn’t just “make the vision model better.” It was also:

Show the rationale in real time so a human can intervene.

A dashboard message like “Speed limit is 85 mph, accelerating” gives a passenger the chance to override before the vehicle acts dangerously.

Translate that to energy operations

Energy automation has its own version of the “85 mph sign”—signals that are technically valid but contextually wrong. Examples I’ve seen repeatedly:

• A meter stream drops out and the model “fills in” with confident but wrong estimates
• A substation camera gets glare or snow occlusion and the model flags a nonexistent fault
• A wind forecast shifts due to a front arriving early, and the dispatch optimizer continues as if nothing changed
• A vibration sensor drifts, and the model interprets the drift as a bearing fault

In those moments, the safest systems don’t just output a label. They output a decision + a reason + a confidence + an intervention option.

For a grid operator, a useful “explanation UI” might look like:

• “Feeder load spike predicted in 18 minutes due to EV fast-charger cluster (confidence 0.72). Primary driver: 3 sites on Circuit 12.”
• “Transformer risk elevated due to rising top-oil temperature and increased harmonic distortion. Most influential signals: THD trend (+), ambient temp (+), cooling fan state (unknown).”

The goal isn’t to bury operators in charts. The goal is to provide just enough clarity to decide whether to act.

Asking “trick questions” is how you find brittle automation

A powerful technique described in the vehicle research is simple: ask the model questions designed to expose whether it truly understands its own reasoning.

In practice, teams run simulations, pose targeted queries about why the model chose a maneuver, and include “trick” prompts that reveal when the explanation module can’t justify the action.

Energy teams should adopt the same mindset: don’t only validate prediction accuracy—validate the model’s ability to explain and fail safely.

What “trick questions” look like on the grid

If you operate AI-enabled automation in utilities, here are practical “question sets” that surface fragility fast:

1. Counterfactuals (What would change your mind?)

   • “If solar output were 15% higher, would you still recommend curtailment?”
   • “If feeder voltage were 0.5% higher, would the switch plan change?”

2. Feature integrity checks (What are you relying on?)

   • “Which 5 inputs contributed most to this fault classification?”
   • “What happens if the PMU stream is delayed by 2 seconds?”

3. Time-pressure sensitivity (Are you stable under stress?)

   • “Would your decision change if response time were 10 seconds instead of 2 minutes?”

4. Out-of-distribution recognition (Do you know when you’re lost?)

   • “Have you seen conditions like this? If not, what’s your fallback behavior?”

1. Human override design (Can a person intervene safely?)
   • “What action is safe if the operator disagrees?”
   • “What’s the minimum set of steps required to revert?”

These are not academic. They become acceptance criteria for automation that touches reliability.

SHAP in plain terms: making AI decisions inspectable

The vehicle study discusses SHAP (SHapley Additive exPlanations), a widely used technique for explaining model outputs by assigning “credit” to each input feature.

In plain terms: SHAP helps you answer, “Which signals actually drove this decision, and how strongly?”

For energy and utilities, SHAP-style analysis becomes especially valuable after an event:

• Why did the model recommend a particular switching action?
• Why did it miss a developing fault?
• Why did the forecast swing?

What SHAP reveals that accuracy metrics hide

Accuracy metrics tell you whether predictions were right on average. SHAP tells you whether the model is:

• Using the right signals (physics-consistent drivers)
• Over-weighting a proxy (e.g., time-of-day instead of real load drivers)
• Sensitive to noise (a small sensor drift causes big recommendation changes)
• Ignoring important context (weather, topology changes, asset configuration)

One strong stance: if you can’t produce a feature-level explanation after a high-impact event, you shouldn’t deploy that model in closed-loop control. Keep it advisory until it’s inspectable.

Explanations must match the user—and utilities have more user types than AVs

Autonomous vehicle researchers point out a hard problem: what level of explanation should be shown to passengers? People differ by technical ability, attention, stress, and preferences.

Utilities face an even broader set of “explanation consumers,” including:

• Control room operators
• Field crews
• Asset managers
• Reliability engineers
• Cybersecurity teams
• Regulators and auditors
• Executives accountable for SAIDI/SAIFI and major event performance

A single explanation format won’t work.

A practical “three-layer” explanation design

Here’s what works in real deployments:

1. Operator layer (fast, minimal, actionable)

   • One sentence: recommendation + primary driver + confidence + safe alternative

2. Engineer layer (diagnostic, reproducible)

   • Top contributing features, time windows, data quality flags, comparable historical cases

3. Audit layer (governance-ready)

   • Model version, training data period, change log, thresholds, approvals, and event timeline

If you’re building AI agents for grid optimization or predictive maintenance, designing these layers early saves you months of rework later.

The legal and safety playbook is converging across sectors

Autonomous vehicle discussions often turn to accountability: when something goes wrong, did the system follow the rules, recognize the incident, and trigger the right response?

Energy has the same questions after:

• A wildfire ignition
• A major outage cascade
• A protective relay misoperation
• A BESS thermal event
• A safety incident involving field work

Explanations are the bridge between “the model said so” and “we can defend this decision.”

A useful standard for critical actions: every AI-driven recommendation that could affect safety or reliability should be able to answer:

• What did you observe?
• What options did you consider?
• Why did you pick this action?
• What uncertainty do you have?
• What would make you change your recommendation?
• What’s the safe fallback?

That’s not overkill. It’s operational maturity.

How to apply this next week: a deployable checklist

You don’t need a research lab to start benefiting from explainable AI. Here’s a practical set of next steps for energy and utilities teams deploying AI in robotics and automation contexts (inspection drones, autonomous monitoring, agentic dispatch support, and predictive maintenance).

1) Add “explainability acceptance tests” to your release process

Before a model ships, require:

• Top 10 feature contributions for representative decisions
• Counterfactual test cases (what changes the output?)
• Data quality flags (missing/late/noisy inputs) and behavior under each
• An explicit “I don’t know” mode for out-of-distribution conditions

2) Design the intervention loop—not just the model

Define:

• Who can override?
• How quickly?
• What happens after override (learning, logging, alerting)?
• What is the safe default action?

3) Build explanations that fit the control room

Keep it simple:

• One primary driver (not seven)
• One confidence statement
• One recommended action
• One safe alternative

If explanations don’t help someone decide in under 10 seconds, they’ll be ignored.

4) Use post-event explainability as a reliability improvement engine

After every near-miss or misprediction:

• Run a SHAP-style breakdown
• Identify “wrong-but-confident” patterns
• Add targeted “trick question” scenarios to your test suite
• Retrain only after you’ve fixed data and monitoring gaps

Where this is heading for AI in Robotics & Automation

Robotics and automation in energy are accelerating—autonomous inspection, automated switching support, agentic scheduling, and AI-assisted restoration are no longer pilot-only ideas. The limiting factor is shifting from model capability to operational trust.

Explainable AI is how you earn that trust without slowing progress to a crawl. It turns autonomy from a black box into an accountable teammate.

If you’re planning AI-enabled grid optimization, predictive maintenance, or automated inspection in 2026, a good north star is simple: don’t just build models that are accurate—build models you can interrogate under pressure.

When your system makes the “85 mph” mistake, will you know before it acts—or only after the incident report is filed?