💼 💸 Day 12: What AI Should Not Do – Asambe! 💼 💸

22 AUGUST 2025

DAY 12

Day 12: What AI Should Not Do – Asambe!

Awe Digital Hustlers,

Stand Guard: Become an AI Ethics Champion for Africa's Future

⚖️ The Ethics Game

AI is powerful, but not always fair.

Bias, discrimination, and deepfakes are already causing real problems across South Africa and beyond.

From hiring algorithms that discriminate against African names to facial recognition systems that struggle with darker skin tones, technology that promises to open doors often ends up reinforcing old inequalities.

As AI becomes more embedded in banking, healthcare, education, and justice systems, getting this right matters more than ever.

You'll dive into a real-world AI ethics case from South Africa and propose your own solution.

These aren’t just theoretical issues; they’re happening now, affecting lives, jobs, and futures.

You’ll analyse incidents, weigh complex trade-offs, and develop frameworks to make ethical AI decisions that protect vulnerable groups while encouraging innovation.

If we’re building the future, shouldn’t it be one worth living in?

By the end of today, you’ll have your own AI ethics framework, a clearer understanding of African challenges, and tools to advocate for responsible AI.

🎯 Today's Challenge: Build Your AI Ethics Compass

 Mission: Analyse & Solve Real AI Ethics Dilemmas

Case Study Analysis - Examine actual South African AI ethics incidents
Stakeholder Impact Assessment - Understand who gets hurt and how
Solution Framework - Develop practical recommendations for responsible AI

The FAIR Method for AI Ethics:

F - Fairness: Does the AI treat all groups equitably?
A - Accountability: Who takes responsibility when things go wrong?
I - Inclusion: Are marginalised voices included in development?
R - Rights: Does the AI respect fundamental human rights and dignity?

🔍 Real South African AI Ethics Case Studies

📱 Case Study #1: Banking Algorithm Bias

 The Situation:
A major South African bank used an AI lending algorithm to speed up loan approvals and cut human bias.

But data showed it systematically rejected applications from historically disadvantaged areas, keeping apartheid-era geographic discrimination alive.

The Numbers:
67% rejection rate in traditionally African townships
23% rejection rate in traditionally white suburbs
Same income levels, same credit scores

The Impact:
Over 15 000 families denied home loans in 18 months
Spatial inequality deepened
Trust in financial institutions eroded

Your Analysis Task:

1. What went wrong with the AI system?

2. How could historical data bias influence AI decisions?

3. How would you fix this system?

4. How can banks prevent this in the future?

📷 Case Study #2: Facial Recognition in Schools

 The Situation:
A prestigious Johannesburg private school installed AI facial recognition for security and attendance.

The system was 95% accurate for white students but only 68% for Black students.

This caused Black students to be locked out, marked absent, and disciplined unfairly.

The Technical Problem:
Training data focused mostly on lighter skin tones
Testing didn’t cover South Africa’s diverse population
No real consideration of local demographics

The Human Cost:
Students felt discriminated against and unwelcome
Parents doubted the school’s commitment to inclusion
Attendance issues hurt academic performance

Your Analysis Task:

1. What are the technical and social causes of this bias?

2. How does this impact students' rights and dignity?

3. What testing should have been done before deployment?

4. How should schools implement AI ethically?

🗳️ Case Study #3: Political Deepfakes in Elections

 The Situation:
During recent elections, deepfake videos showed candidates making inflammatory statements they never said.

These spread fast on WhatsApp and Facebook, shaping voter opinions and possibly influencing results.

The Challenge:
Videos fooled 90% of average viewers
Detection tools weren’t accessible to the public
Spread outpaced fact-checking
Traditional media couldn’t keep up with corrections

The Stakes:
Democracy’s integrity was at risk
Public trust in politics eroded
Social divisions worsened

Your Analysis Task:

1. Who should detect and remove deepfakes?

2. How can voters learn about AI manipulation?

3. What laws should govern political AI use?

4. How do we balance free speech with stopping manipulation?

🌍 African-Centred AI Ethics Framework

 🎯 Ubuntu Principles for AI Development:

Community Over Individual:
Focus on collective benefit, not just individual gain
Consider how AI impacts extended families and communities
Value consensus in AI decisions

Respect for Elders and Tradition:
Include traditional knowledge in AI training
Respect cultural practices in AI applications
Make sure AI doesn’t undermine community structures

Interconnectedness:
Remember, AI’s effects ripple through communities
Think about environmental and social sustainability
Build AI that strengthens social bonds, not weakens them

Dignity and Respect:
Ensure AI preserves human dignity
Protect vulnerable groups from exploitation
Honour diverse African cultures and languages

🔧 Practical Ethics Checklist:

Before Implementing AI:

• Have we tested across all demographic groups?

• Are marginalised voices part of the development team?

• Have we considered cultural sensitivity?

• Is there clear accountability for AI decisions?

• Can communities understand and challenge AI decisions?

During AI Operations:

• Are we monitoring bias regularly?

• Do users know when they interact with AI?

• Can humans override AI decisions?

• Are we collecting feedback from affected communities?

When Problems Arise:

• Do we have quick response plans for harmful AI?

• Are remedies accessible to those harmed?

• Are we learning and improving systems?

• Are we transparent about issues and fixes?

💼 AI Ethics in South African Industries

🏥 Healthcare: Life and Death Decisions

 Ethical Challenges:
AI diagnostic tools are mostly trained on European/American data
Language barriers in health AI
Privacy concerns with health data
Inequality between private and public healthcare

Best Practices:
Include diverse African populations in training data
Develop multilingual health AI interfaces
Ensure culturally appropriate informed consent
Keep human oversight on critical decisions

👮‍♂️ Criminal Justice: Fairness in the System

 Ethical Challenges:
Predictive policing may reinforce racial profiling
AI bail and sentencing tools affect marginalised groups
Facial recognition struggles with accuracy in surveillance
Digital divides limit access to AI legal tools

Best Practices:
Regular bias audits of justice AI systems
Community oversight of police AI use
Transparency in AI-assisted judicial decisions
Equal access to AI tools for defence

🎓 Education: Shaping Future Generations

 Ethical Challenges:
AI tutoring may reinforce educational gaps
Language barriers in educational AI
Privacy worries over student data
Digital access disparities affect AI learning

Best Practices:
Create AI tools in local languages
Ensure equitable access across income groups
Protect student privacy and data rights
Include diverse cultural perspectives in AI curricula

🛡️ Building Ethical AI Solutions

 🎯 Your Personal AI Ethics Framework:

Step 1: Stakeholder Mapping
Primary Stakeholders: Who uses or benefits from the AI?
Secondary Stakeholders: Who is affected indirectly?
Vulnerable Groups: Who might be harmed or excluded?
Power Holders: Who makes AI decisions?

Step 2: Rights Impact Assessment
Privacy Rights: How does AI collect and use personal data?
Dignity Rights: Does AI treat people respectfully?
Equality Rights: Does AI discriminate against any group?
Participation Rights: Can affected people influence AI decisions?

Step 3: Cultural Sensitivity Check
Language Inclusion: Does AI work in local languages?
Cultural Appropriateness: Does AI respect customs?
Community Values: Does AI align with community priorities?
Traditional Knowledge: Does AI include local wisdom?

Step 4: Accountability Mechanisms
Transparency: Can people understand how AI works?
Appeal Process: Can people challenge AI decisions?
Remedy Availability: Can harm be fixed?
Continuous Improvement: Is AI updated based on feedback?

🎁 Take Action: Ethics Advocacy Toolkit

🗣️ Advocating for Ethical AI:

In Your Workplace:
Propose AI ethics training for your team
Suggest diverse hiring for AI roles
Push for bias testing before AI launch
Create feedback channels for users

In Your Community:
Educate others about AI bias
Join or start local digital rights groups
Attend meetings on government AI use
Support ethical AI businesses

Online and Social Media:
Share accurate info on AI ethics
Challenge AI-generated misinformation
Support journalism covering AI ethics
Promote diverse voices in AI discussions

📋 Ethical AI Evaluation Template:

 For Any AI System You Encounter:

1. Purpose: What’s the AI supposed to do?

2. Data: What info does it use?

3. Bias: Could it discriminate?

4. Transparency: Can users understand decisions?

5. Accountability: Who’s responsible for harm?

6. Fairness: Does it treat everyone fairly?

7. Privacy: How does it protect personal info?

8. Human Control: Can humans override decisions?

📚 Resources for Continued Learning

🌍 African AI Ethics Organisations:
AI4D Africa - Research and advocacy network
Deep Learning Indaba - African ML/AI community
Research ICT Africa - Digital policy research
Mozilla Foundation - Internet health and AI ethics

📖 Essential Reading:
"Race After Technology" by Ruha Benjamin – on algorithmic bias
"Weapons of Math Destruction" by Cathy O'Neil – AI harm in society
"The Ethical Algorithm" by Kearns & Roth – fairness in AI
"Ubuntu and AI Ethics" by various authors – African perspectives

🎓 Free Online Courses:
MIT: Introduction to Machine Learning Ethics (edX)
University of Helsinki: Ethics in AI Design (Coursera)
Stanford: Human-Computer Interaction Design (Online)
Elements of AI – Basic AI concepts and ethics

🏁 Success Checklist

• Analysed all three South African AI ethics case studies

• Applied the FAIR method to each situation

• Developed personal positions on each dilemma

• Created your own AI ethics evaluation framework

• Identified potential AI ethics issues in your community

• Researched at least one AI ethics organisation or resource

• Practised using the ethical AI evaluation template

• Planned one concrete action to promote ethical AI

🎓 Key Takeaways

AI bias reflects and can amplify social inequalities
African perspectives and Ubuntu principles offer strong ethical frameworks
Technical fixes must go hand-in-hand with social and legal safeguards
Everyone has a role in pushing for ethical AI
Ethical AI isn’t just about avoiding harm; it’s about creating real positive change

👉 [Day 12 – Your Ethics Challenge]
Ready to stand guard for ethical AI? The future depends on the choices we make today!

ASAMBE!

Click the link below to join our 21-Day AI Tools & Trends Challenge WhatsApp Community: 

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NB: Please note the group is temporary; it will be deleted after the challenge.

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