Assignment Week 7: Ethical AI Analysis & Implementation Guide

This repository contains a comprehensive exploration of ethical AI principles, case studies, practical audits, and policy recommendations. Below is a structured summary of all sections, including code, reports, and guidelines.

Project Lead:

Emmanuella Aimalohi Ileogben - [email protected]

Table of Contents

• Part 1: Theoretical Understanding
• Part 2: Case Study Analysis
• Part 3: Practical Audit
• Part 4: Ethical Reflection
• Bonus: Policy Proposal
• Usage & Requirements
• Contributing

Part 1: Theoretical Understanding

Key Topics Covered

• Algorithmic Bias: Definition and examples (e.g., hiring algorithms, facial recognition).
• Transparency vs. Explainability: Why both are critical for trust and compliance (GDPR).
• GDPR Impact: How EU regulations shape AI development (data minimization, right to explanation).
• Ethical Principles Matching: Justice, non-maleficence, autonomy, and sustainability.

Deliverables

• Short answers to theoretical questions.
• Matched ethical principles to definitions.

Part 2: Case Study Analysis

Case 1: Amazon’s Biased Hiring Tool Source of Bias: Skewed training data, gendered feature weighting.

Proposed Fixes:

• Debiased datasets.
• Fairness-aware algorithms (e.g., adversarial debiasing).
• Human-in-the-loop validation.

Fairness Metrics: Disparate impact ratio, predictive parity.

Case 2: Facial Recognition in Policing Ethical Risks: Wrongful arrests, privacy violations, systemic bias.

Policy Recommendations:

• Legislative bans in high-risk contexts.
• Mandatory third-party audits.
• Community engagement in deployment.

Deliverables

• Detailed case study reports with actionable solutions.

Part 3: Practical Audit

COMPAS Recidivism Dataset Analysis Goal: Audit racial bias in risk scores using Python and AIF360.

Key Steps:

• Data preprocessing (pandas).
• Bias metric calculation (false positive rates, disparate impact).
• Visualization (matplotlib, seaborn).

Findings: Higher false positives for Black defendants. Remediation: Reweighing, adversarial debiasing.

Deliverables

• Jupyter Notebook with full code.
• 300-word audit report.

Part 4: Ethical Reflection

Personal Project: AI Resume Screener

Ethical Safeguards:

• Fairness: Debiasing training data (AIF360).
• Transparency: SHAP explanations for rejections.
• Privacy: Anonymization and data minimization.

Quote: "Ethics is a design constraint—like gravity in engineering."

Deliverables 300-word reflection.

Bonus: Policy Proposal

Ethical AI in Healthcare Guidelines

• Patient Consent: Opt-in protocols, right to opt-out.
• Bias Mitigation: Diverse datasets, quarterly audits.
• Transparency: Plain-language explanations, algorithm disclosure.
• Accountability: Human oversight, error reporting.

Deliverables 1-page PDF policy draft.

Usage & Requirements

Dependencies

• Python 3.8+
• Libraries: pandas, matplotlib, seaborn, aif360
• Dataset: COMPAS

Run This Audit:

sh git clone [repo_url] cd ethical-ai-audit pip install -r requirements.txt jupyter notebook COMPAS_Analysis.ipynb

Contributing

• Issues: Report bugs or suggest enhancements.
• Pull Requests: Submit fixes/additions with clear documentation.
• License: MIT

References:

• ProPublica’s COMPAS investigation.
• IBM’s AIF360 toolkit.
• GDPR/WHO guidelines.
• Author: Ileogben Emmanuella Aimalohi | Date: 25/07/2025

Project (Fairscore Comapass Dashboard) info

URL: https://lovable.dev/projects/4831259a-95a2-4b81-8f7e-70627a7dc49c Use Lovable

• Simply visit the Lovable Project and start prompting.
• Changes made via Lovable will be committed automatically to this repo.

Use your preferred IDE

• If you want to work locally using your own IDE, you can clone this repo and push changes. Pushed changes will also be reflected in Lovable.
• The only requirement is having Node.js & npm installed - install with nvm

Follow these steps:

# Step 1: Clone the repository using the project's Git URL.
git clone <YOUR_GIT_URL>

# Step 2: Navigate to the project directory.
cd <YOUR_PROJECT_NAME>

# Step 3: Install the necessary dependencies.
npm i

# Step 4: Start the development server with auto-reloading and an instant preview.
npm run dev

What technologies are used for this project?

This project is built with:

• Vite
• TypeScript
• React
• shadcn-ui
• Tailwind CSS

How can I deploy this project?

Simply open Lovable and click on Share -> Publish.

Can I connect a custom domain to my Lovable project?

Yes, you can! To connect a domain, navigate to Project > Settings > Domains and click Connect Domain. Read more here: Setting up a custom domain

✨Ethical AI isn't the future, it's the foundation!