Question

Building an Expectation-Based AI Governance Model (EBAGM) in Python

What if AI governance wasn’t just about accuracy—but about aligning with human expectations?

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Introduction

Most AI systems today are evaluated using metrics like accuracy, precision, and recall. But in real-world scenarios, that’s not enough.

A model can be technically correct and still feel unfair, biased, or unethical to humans.

This is where a new idea comes in:

Expectation-Based AI Governance Model (EBAGM)

Instead of only focusing on data and outputs, EBAGM introduces:

• Human expectations
• Perceived intent
• Ethical alignment

In this blog, I’ll walk you through building a working prototype in Python.

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⚙️ What is EBAGM?

EBAGM is a governance framework with 5 layers:

1. Expectation Layer (E) → What humans expect (fairness, privacy, etc.)
2. Data Governance (D → D') → Modify data based on expectations
3. Model (M) → AI decision-making
4. Perceived Intent (P) → Does AI feel fair?
5. Feedback Loop → Adjust system if misaligned

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Step 1: Creating a Biased Dataset

We simulate a loan approval system with built-in bias.

import pandas as pd
import numpy as np

np.random.seed(42)

data = pd.DataFrame({
    "income": np.random.randint(20000, 100000, 100),
    "credit_score": np.random.randint(300, 850, 100),
    "gender": np.random.choice([0, 1], 100)
})

data["approved"] = (
    (data["income"] > 50000) &
    (data["credit_score"] > 600)
).astype(int)

# Bias toward males
data.loc[data["gender"] == 1, "approved"] |= (np.random.rand(100) > 0.7)

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Step 2: Defining Expectations

This is the core of EBAGM.

expectations = {
    "fairness": 0.9,
    "accuracy": 0.8,
    "privacy": 0.7
}

Here, fairness is prioritized.

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⚖️ Step 3: Data Governance

If fairness is high, we remove sensitive attributes.

def apply_governance(data, expectations):
    data = data.copy()
    
    if expectations["fairness"] > 0.8:
        data = data.drop(columns=["gender"])
    
    return data

governed_data = apply_governance(data, expectations)

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Step 4: Train the Model

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

X = governed_data.drop(columns=["approved"])
y = governed_data["approved"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LogisticRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

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Step 5: Perceived Intent Score (PIS)

This is the key innovation.

Instead of just checking accuracy, we measure:

Does the model appear fair?

def perceived_intent_score(original_data, X_test, predictions, expectations):
    df = original_data.loc[X_test.index].copy()
    df["pred"] = predictions
    
    male_rate = df[df["gender"] == 1]["pred"].mean()
    female_rate = df[df["gender"] == 0]["pred"].mean()
    
    bias = abs(male_rate - female_rate)
    
    pis = 1 - (bias * expectations["fairness"])
    
    return round(pis, 3), round(bias, 3)

pis, bias = perceived_intent_score(data, X_test, predictions, expectations)

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Step 6: Governance Feedback

def governance_feedback(pis, threshold=0.75):
    if pis < threshold:
        return "⚠️ Misalignment detected"
    else:
        return "✅ System aligned"

print(governance_feedback(pis))

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Sample Output

Perceived Intent Score (PIS): 0.955
Bias: 0.05

✅ System aligned with expectations

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Key Insight

Even though the model doesn’t explicitly optimize fairness, governance decisions (like removing gender) improved ethical alignment.

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⚠️ Experiment: Lower Fairness

Change:

"fairness": 0.2

Now:

• Gender is NOT removed
• Bias increases
• PIS drops

You may see:

⚠️ Misalignment detected

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Why This Matters

Traditional AI governance focuses on:

• Data quality
• Model performance

EBAGM adds something new:

Human expectation alignment

This leads to three new concepts:

1. Expectation Misalignment

When AI is correct but feels wrong

2. Perceived Consciousness Risk

When humans think AI has intent

3. Utility vs Ethics Conflict

When optimization clashes with fairness

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Final Thoughts

EBAGM is a simple but powerful idea:

AI systems shouldn’t just be correct—they should be aligned with human expectations.

This prototype shows how we can:

• Embed ethics into pipelines
• Quantify perceived fairness
• Build adaptive governance systems

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What’s Next?

You can extend this by:

• Adding fairness metrics (demographic parity, equal opportunity)
• Using real datasets (Kaggle)
• Adding explainability (SHAP, LIME)
• Running multi-scenario experiments

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Closing Idea

“The future of AI governance is not just about controlling systems, but about aligning them with how humans expect them to behave.”

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If you’re exploring AI ethics, data governance, or philosophy of AI—this is a space worth building in.

Let me know if you want the research paper version or a GitHub-ready project