Masterclass 2035

AI Ethics & Responsible Innovation

Modules

40+

Hours

∞

Impact

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Foundation & Principles

Understand the core ethical frameworks that guide responsible AI development.

Ethics Philosophy Values

Bias & Fairness

Learn to identify, measure, and mitigate biases in AI systems.

Detection Metrics Mitigation

Transparency & Explainability

Master techniques for making AI decisions interpretable and trustworthy.

XAI LIME SHAP

Privacy & Security

Implement privacy-preserving techniques and secure AI pipelines.

Differential Privacy Federated Learning

Governance & Compliance

Navigate regulatory landscapes and establish accountability frameworks.

GDPR AI Act Auditing

Applied Ethics

Put theory into practice with real-world simulations and case studies.

Case Studies Simulation Projects

⚖️

Fairness

Ensure equitable outcomes across all demographic groups.

🔍

Transparency

Make AI decision-making processes understandable and open.

🎯

Accountability

Establish clear responsibility for AI system outcomes.

🔒

Privacy

Protect individual data rights and confidentiality.

🛡️

Safety

Prevent harm and ensure robust, reliable AI behavior.

💚

Beneficence

Maximize positive impact on individuals and society.

1.1 Types of Bias in AI Systems 45 min

Bias in AI systems can manifest in multiple forms: historical bias from training data, representation bias from sampling, measurement bias from proxy variables, and algorithmic bias from model architecture choices.

# Example: Detecting demographic parity
from aif360.metrics import BinaryLabelDatasetMetric

metric = BinaryLabelDatasetMetric(
    dataset,
    unprivileged_groups=[{'gender': 0}],
    privileged_groups=[{'gender': 1}]
)
print(f"Disparate Impact: {metric.disparate_impact()}")

🤔 Ethical Dilemma

"If a hiring algorithm trained on historical data perpetuates past discrimination, who is responsible — the developers, the company using it, or the data providers?"

Bias Severity 50%

Moderate bias levels may go unnoticed in standard testing but significantly impact minority groups.

1.2 Pre-processing Mitigation Techniques 60 min

Pre-processing techniques modify the training data before model training. Key approaches include reweighting, resampling, and representation learning methods like Fair Representation Learning.

1.3 In-processing & Post-processing Methods 75 min

In-processing methods incorporate fairness constraints during training, while post-processing adjusts model outputs to satisfy fairness criteria. Each approach has trade-offs between accuracy and fairness.

2.1 Group Fairness Metrics 50 min

Group fairness metrics compare outcomes across demographic groups. Key metrics include Demographic Parity (equal positive prediction rates), Equalized Odds (equal TPR and FPR), and Calibration (equal precision).

# Equalized Odds Check
def equalized_odds(y_true, y_pred, group):
    tpr_privileged = recall(y_true[group==1], y_pred[group==1])
    tpr_unprivileged = recall(y_true[group==0], y_pred[group==0])
    fpr_privileged = fpr(y_true[group==1], y_pred[group==1])
    fpr_unprivileged = fpr(y_true[group==0], y_pred[group==0])
    return abs(tpr_privileged - tpr_unprivileged) < 0.1

2.2 Individual Fairness 45 min

Individual fairness requires that similar individuals receive similar predictions. This requires defining an appropriate similarity metric over individuals.

2.3 Impossibility Theorems 40 min

Mathematical proofs show that certain fairness criteria cannot be satisfied simultaneously. Understanding these trade-offs is crucial for making informed ethical decisions.

3.1 Explainable AI (XAI) Foundations 55 min

Explainable AI encompasses techniques that make model predictions understandable to humans. This includes inherently interpretable models (decision trees, linear models) and post-hoc explanation methods.

# SHAP Values for Feature Importance
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test, feature_names=features)

3.2 LIME & Local Explanations 50 min

Local Interpretable Model-agnostic Explanations (LIME) explain individual predictions by approximating the model locally with an interpretable surrogate.

3.3 Documentation & Model Cards 35 min

Model cards and datasheets provide standardized documentation for AI systems, including intended use cases, limitations, and ethical considerations.

4.1 Differential Privacy 70 min

Differential privacy provides mathematical guarantees that individual data points cannot be identified from model outputs. The privacy budget (ε) controls the trade-off between privacy and accuracy.

# Differential Privacy with PyTorch
from opacus import PrivacyEngine
privacy_engine = PrivacyEngine()
model, optimizer, dataloader = privacy_engine.make_private(
    module=model,
    optimizer=optimizer,
    data_loader=train_loader,
    noise_multiplier=1.1,
    max_grad_norm=1.0
)

4.2 Federated Learning 60 min

Federated learning enables training on distributed data without centralizing sensitive information. Models are trained locally and only gradients are shared.

4.3 Secure Multi-Party Computation 45 min

SMPC allows multiple parties to jointly compute a function without revealing their individual inputs to each other.

5.1 AI Governance Frameworks 50 min

Effective AI governance requires clear policies, defined roles, and established processes for oversight. This includes ethics review boards, impact assessments, and monitoring systems.

🤔 Governance Challenge

"How do you balance innovation speed with thorough ethical review? When does 'move fast' become 'move recklessly'?"

5.2 AI Auditing Practices 55 min

Regular audits ensure AI systems continue to meet ethical standards. This includes technical audits (model performance), process audits (development practices), and impact audits (real-world outcomes).

5.3 Stakeholder Engagement 40 min

Engaging affected communities in AI development and deployment decisions is crucial for legitimacy and effectiveness.

6.1 EU AI Act Deep Dive 75 min

The EU AI Act establishes a risk-based regulatory framework. High-risk AI systems (healthcare, employment, credit) face strict requirements including conformity assessments, documentation, and human oversight.

/* AI Act Risk Categories */
UNACCEPTABLE: Social scoring, real-time biometric surveillance
HIGH-RISK: Healthcare diagnosis, credit scoring, hiring
LIMITED: Chatbots, emotion recognition (transparency only)
MINIMAL: Spam filters, games (no requirements)

6.2 Global Regulatory Landscape 50 min

Compare approaches across jurisdictions: EU's comprehensive regulation, US sector-specific rules, China's algorithmic recommendations law, and emerging frameworks in other regions.

6.3 Compliance Implementation 60 min

Practical steps for achieving and maintaining regulatory compliance, including documentation requirements, technical measures, and organizational processes.

Healthcare AI Dilemma

Your AI system for healthcare resource allocation has been deployed. Analysis shows it's denying care at higher rates to patients from lower-income zip codes — a proxy for race. The hospital board wants to keep using it because it's 15% more efficient. What do you do?

Outcome Analysis

Ethics Score 0

A widely-used healthcare algorithm systematically underestimated the needs of Black patients, affecting millions of decisions.

📅 2019 🔴 High Impact

Goldman Sachs' credit algorithm gave women lower credit limits than men, even with identical financial profiles.

📅 2019 🟠 Medium Impact

Risk assessment algorithm incorrectly labeled Black defendants as higher risk at twice the rate of white defendants.

📅 2016 🔴 High Impact

Amazon scrapped an AI recruiting tool after discovering it downgraded resumes containing the word "women's."

📅 2018 🟠 Medium Impact

Robert Williams was wrongfully arrested after facial recognition software matched him to a shoplifter based on a blurry image.

📅 2020 🔴 High Impact

AI cancer treatment recommendations were found to be unsafe and based on synthetic rather than real patient data.

📅 2018 🔴 High Impact

🔍

Demographic Parity

P(Ŷ=1|A=0) = P(Ŷ=1|A=1)

Equalized Odds

Equal TPR and FPR across groups

ε-Differential Privacy

ln(P(M(D)∈S)/P(M(D')∈S)) ≤ ε

GDPR Art. 22

Right to human review of automated decisions

EU AI Act

Risk-based framework: minimal to unacceptable

SHAP Values

Game-theoretic feature attribution

LIME

Local interpretable model-agnostic explanations

AI Audit Trail

Log decisions, data versions, model changes

Model Cards

Standardized model documentation template

Bias Types

Historical, representation, measurement, algorithmic

CCPA

California Consumer Privacy Act rights

Federated Learning

Train on distributed data, share only gradients

Ready to Apply Your Knowledge?

Book a consultation session to discuss implementing ethical AI practices in your organization.

Your 4-Week Action Plan

Week 1

Audit Current Systems

Evaluate existing AI for bias using the metrics you've learned.

Week 2

Implement Monitoring

Set up fairness dashboards and automated bias detection.

Week 3

Documentation Sprint

Create model cards and datasheets for all production models.

Week 4

Governance Setup

Establish ethics review board and decision-making processes.

Downloadable Resources

📄 AI Ethics Checklist (PDF) 2.3 MB 📊 Fairness Metrics Toolkit 4.1 MB 📝 Model Card Template 890 KB 📚 Regulatory Compliance Guide 5.7 MB