Explainable AI for Safer Grids: Lessons From AVs

A single sticker changed a “35” speed limit sign just enough that a Tesla Model S reportedly read it as “85”—and accelerated.

That moment sticks because it’s not really about cars. It’s about automation under pressure: a system taking real-world sensor inputs, making a decision in milliseconds, and offering everyone around it exactly zero visibility into why it did what it did.

In the AI in Robotics & Automation series, we usually talk about robots on factory floors, autonomous inspection drones, or automated substations. The common thread is the same: when you automate decisions in physical environments, safety and trust become product requirements, not “nice-to-haves.” That’s why the current push for explainable AI (XAI) in autonomous vehicles is directly relevant to energy and utilities, where AI is now advising (and sometimes controlling) grid operations, outage response, asset maintenance, and battery dispatch.

Explainable AI is a safety feature, not a PR feature

Explainable AI is practical when it answers one question clearly: what did the system “see,” and what drove the decision?

Autonomous vehicle stacks are often described as black boxes: perception → prediction → planning → control. When something goes wrong, engineers and regulators need to know where the decision chain broke. Passengers need to know whether to intervene. Bystanders need confidence the system isn’t improvising.

Energy systems are the same, except the “passengers” are grid operators, field crews, and reliability engineers.

Here’s my stance: If an AI model can influence a switching plan, a DER curtailment decision, a protection setting recommendation, or a maintenance deferral, it should be explainable by design. Not in a 30-page model card no one reads—explainable at the moment of use.

In critical infrastructure, “trust me” isn’t an interface.

The shared problem: humans can’t supervise what they can’t understand

Autonomous vehicles struggle with the handoff problem: when does the human take over, and how do they know?

Utilities face a parallel issue:

• A control room operator must decide whether to follow an AI-recommended remedial action.
• A reliability engineer must decide whether to accept a model’s probability-of-failure forecast.
• A field supervisor must decide whether a drone/robot inspection finding is a true defect or a false alarm.

Explainability doesn’t replace verification. But it makes supervision possible at scale.

“Ask the right questions” translates perfectly to grid AI

The IEEE Spectrum piece highlights an important shift: instead of treating explanations as after-the-fact paperwork, researchers are treating them as questions you pose to a model to probe its decision-making.

That mindset is gold for energy and utilities. Most organizations start by asking their AI one question:

• “What’s the prediction?”

They should be asking at least five:

1. What inputs mattered most to this decision?
2. What alternative actions did you consider, and why were they rejected?
3. How sensitive is this decision to sensor error or missing data?
4. What’s your confidence, and what’s driving uncertainty?
5. What would need to be true for you to change your decision?

Those questions aren’t academic. They map directly to day-to-day operational risk.

Example: the “sticker on the sign” becomes “bad telemetry on the feeder”

The speed-sign example is a classic adversarial or brittle perception failure: a tiny change to an input causes a huge change in output.

Grid analogs show up constantly:

• A drifting temperature sensor makes a transformer look like it’s overheating.
• A CT/PT scaling issue corrupts load readings.
• A communications drop causes a DER output estimate to freeze.
• A mislabeled asset record shifts a maintenance priority score.

If an AI model responds to these conditions without an explanation layer, you get silent failure.

If it does explain itself, you can surface an operator-facing rationale like:

• “Recommending load transfer because feeder current is above threshold; however, confidence reduced due to missing AMI reads in last 12 minutes.”

That’s the difference between “automation” and “automation people can safely use.”

Real-time explanations: what to show, and what not to show

One of the hardest parts of explainable AI isn’t technical—it’s human factors.

The Spectrum article points out a real issue: different users need different explanation depth and formats (audio, visualization, text, haptics). In utilities, that’s even more pronounced because the roles vary widely.

A good pattern is tiered explanations:

• Tier 1 (Operational): a one-line reason and a confidence indicator
• Tier 2 (Diagnostic): top contributing signals, thresholds, recent anomalies
• Tier 3 (Engineering): traceable feature attributions, data lineage, model version, scenario comparisons

What real-time XAI looks like in energy operations

If you’re using AI for grid optimization or automated switching support, real-time explanation should answer:

• What constraint is binding? (voltage, thermal, N-1, protection coordination)
• What measurement drove the action? (feeder load, bus voltage, transformer hotspot)
• What’s the risk if we do nothing? (estimated overload duration, forecasted voltage excursion)

If you’re using AI for predictive maintenance:

• What failure mode is suspected? (partial discharge pattern, cooling degradation, bearing wear)
• Which evidence supports it? (harmonics, vibration bands, thermal gradients)
• What changed recently? (trend break, step change, increased variance)

A blunt rule I’ve found useful: If an operator can’t explain your AI’s recommendation to a peer in under 30 seconds, the interface is wrong.

Post-incident explainability: faster root cause, better compliance

Real-time explanations help prevent mistakes. Post-incident explanations help you learn.

The article discusses analyzing decisions after errors and using techniques like SHAP (SHapley Additive exPlanations) to score how influential different features were. This is especially relevant in utilities because many AI deployments die in the “trust gap” after the first unexpected outcome.

Post-incident XAI should support:

• Root cause analysis: which signals and internal steps led to the action
• Counterfactual review: what would have changed the decision
• Drift detection: whether the input distribution shifted (seasonal load patterns, new DER behavior)
• Governance evidence: model versioning, feature provenance, audit trail

A practical workflow utilities can adopt

1. Capture decision context every time the model recommends action (inputs, timestamps, data quality flags).
2. Run attribution analysis (e.g., SHAP-style) on the recommendation.
3. Compare to known-good cases (same asset class, similar weather/load, similar topology).
4. Label the outcome (correct, incorrect, ambiguous) and feed it into continuous evaluation.
5. Update explanation rules so future users see the right warnings.

Done well, this becomes a reliability loop—not a blame loop.

“Trick questions” and red-teaming: the missing discipline in grid AI

The researchers described asking models “trick questions” to expose when an explanation module fails.

Utilities should adopt this mindset explicitly. In robotics and automation, we already do versions of this with safety PLC testing, fail-safe states, and fault injection. Grid AI deserves the same rigor.

Here are red-team tests worth institutionalizing:

• Sensor spoofing / perturbation: small changes to telemetry that shouldn’t flip decisions
• Missing-data scenarios: communications loss, partial AMI visibility, delayed SCADA points
• Topology surprises: switching state mismatch between GIS/OMS/SCADA
• Rare-event conditions: storms, cold snaps, wildfire PSPS operations, black-start sequences
• Conflicting objectives: reliability vs. cost vs. emissions vs. customer impact

A model that performs well on average but fails badly on edge cases is dangerous in critical infrastructure.

Legal and public-trust parallels: accountability needs explanations

The Spectrum article notes how explanations can clarify what happened when an AV hits a pedestrian: did it follow rules, recognize the collision, stop, and call emergency services?

Grid incidents carry similar accountability questions:

• Did the system follow operational constraints and protection rules?
• Did it recognize abnormal conditions (faults, islanding, oscillations)?
• Did it escalate appropriately (alarms, operator handoff, safe-state controls)?
• Did it log actions and context for investigation?

Explainability isn’t just “nice transparency.” It’s operational defensibility.

What to do next if you’re deploying AI in energy & utilities

Most companies get stuck because they treat explainable AI as something to bolt on after the model is trained. There’s a better way to approach this: treat explainability as part of the automation system, like interlocks and alarms.

Here’s a pragmatic checklist you can use this quarter:

1. Define decision rights: Is AI advising, recommending, or controlling?
2. Standardize explanation formats: one-line rationale + drill-down layers.
3. Instrument data quality: missingness, latency, sensor health, topology confidence.
4. Adopt “operator-ready” thresholds: don’t show raw attributions without translating them into constraints and risks.
5. Build a post-incident package: automatic replay, attribution report, and counterfactuals.
6. Red-team monthly: simulate faults, spoofed inputs, and rare events.

If you’re in the robotics & automation side of utilities—inspection robots, autonomous drones, substation automation—apply the same logic: every autonomous action should come with a human-usable rationale and a safe intervention path.

Where this is heading in 2026: explainability becomes operational plumbing

Explanations are quickly becoming an integral component of autonomous systems because they solve a basic operational reality: humans remain accountable, even when machines decide.

Autonomous vehicles are forcing the conversation in public view. Energy and utilities should take the hint—quietly, before a major incident makes it loud.

If you’re building AI for grid optimization, predictive maintenance, or automated field robotics, the question to design around is simple:

Can the system explain itself clearly enough that a responsible expert can supervise it in real time?

If the answer is “not yet,” that’s not a blocker. It’s your roadmap.