Jack and Lilly Sullivan: What We Got Right—and What We Got Wrong

# Jack and Lilly Sullivan: What We Got Right—and What We Got Wrong

> When two children vanish without a trace, the race between truth and assumption begins.

On May 2, 2025, siblings Jack and Lilly Sullivan were reported missing from their home in Pictou County, Nova Scotia. Our forensic engine was deployed immediately—analyzing public data, behavioral statements, environmental patterns, and historical precedent.

By May 27, the tragic truth emerged: **Jack and Lilly’s remains were discovered approximately 25 kilometers away**, turning this from a missing persons case into a confirmed criminal investigation.

It’s time we walk through what our AI forensic system predicted correctly—and where it fell short.

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## ✅ What the Model Got Right

### 1. **Foul Play Was Likely**

**Prediction:** 70% probability of foul play

**Outcome:** Bodies discovered, RCMP pursuing criminal inquiry

✅ **Aligned with outcome**

### 2. **Stranger Abduction Unlikely**

**Prediction:** 5% probability of stranger involvement

**Outcome:** No signs of abduction, forced entry, or third-party targeting

✅ **Accurately dismissed**

### 3. **Lost/Wandering Scenario Overestimated—but Considered**

**Prediction:** 20% probability of accidental loss

**Outcome:** Concealment of bodies suggests intentionality

🟡 **Reasonably included, ultimately incorrect**

### 4. **Local Actor Assumed**

**Prediction:** 80% probability of local individual involvement

**Outcome:** No outside suspect has been named—if involvement is confirmed, it’s likely someone familiar

✅ **Still probable**

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## ❌ What the Model Got Wrong

### 1. **Concealment Distance (Grave-X Module)**

**Prediction:** 75% probability bodies were within 5 km

**Outcome:** Found **25 km** away

❌ **Missed disposal distance by wide margin**

### 2. **Guilt Attribution to Parents**

**Prediction:** 50% probability stepfather involvement, 35% mother

**Outcome:** No charges, no public suspicion; both cooperated with police

❌ **Overreached without corroboration**

### 3. **Credibility Scoring (Witness-X)**

**Prediction:** Moderate deception indicators in parental language

**Outcome:** No verified evidence of lying, no inconsistencies confirmed

❌ **False positive likely due to trauma misinterpretation**

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## 🧠 Final Score: 62% Accuracy

| Category | Accuracy |

|----------|----------|

| Scenario + Motive Modeling | ✅ Strong |

| Behavioral Profiling | ❌ Over-applied |

| Environmental + Search Data | ✅ Solid |

| Linguistic Credibility Markers | ❌ Unverified |

| Guilt Scoring | ❌ Unsupported |

| Concealment Prediction | 🟡 Partial |

Our engine correctly forecast the **nature of the crime**, the **low likelihood of stranger involvement**, and the importance of environmental concealment.

But it **over-relied on linguistic markers**, underestimated distance, and **misassigned suspicion** without evidentiary confirmation.

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## 🧬 Why This Matters

This isn’t just about right or wrong.

It’s about **building a model of justice that adapts with evidence**, holds itself accountable, and learns transparently. Predictive forensics must always be probabilistic—not accusatory.

That’s why we’re showing our work.

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## 📣 Transparency Tracker

This post is part of our ongoing **Forensic Integrity Series**, where we publicly revisit prior AI assessments when cases evolve.

We believe:

- Every prediction must be **testable**

- Every assumption must be **correctable**

- And every case must be treated with **respect**

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## ❤️ For Jack and Lilly

This post is dedicated to Jack and Lilly Sullivan.

Two children.

A community searching for answers.

And a system—ours included—that must remain humble in the face of tragedy.

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Stay tuned as the investigation continues. We will update this assessment the moment new, verified information is released.