Course: Digital Development Ethics 101 Module: Algorithmic Bias and Fairness Duration: 75-90 minutes Format: Technical concepts with practical bias audit exercise
Learning Objectives
Identify sources of bias in AI systems used for development
Evaluate the impact of algorithmic decisions on marginalized communities
Design strategies for algorithmic accountability and fairness
Conduct basic bias audits of AI systems
Key Concepts
Types of Algorithmic Bias:
Historical Bias: Algorithms learn from past data that reflects historical discrimination. Example: Credit algorithms trained on data from when women couldn't open bank accounts independently.
Representation Bias: Certain groups are underrepresented in training data. Example: Facial recognition systems trained primarily on fair-skinned faces failing to recognize darker skin tones.
Measurement Bias: Different quality or types of data collected for different groups. Example: Rural populations having less digital footprint for credit scoring.
Aggregation Bias: Assuming one model works equally well for all subgroups. Example: Health algorithms calibrated for urban populations being applied to rural communities.
Case Study: Credit Scoring and Financial Inclusion in India
Context
Digital lending platforms in India increasingly use alternative data for credit scoring, including mobile usage patterns, social media activity, app usage, transaction history, and even location data. This promises to extend credit to the "thin-file" population without traditional credit histories.
How Alternative Credit Scoring Works:
Mobile data: Call patterns, SMS frequency, contact list analysis
Transaction data: Digital payment patterns, merchant categories
Social media: LinkedIn profiles, Facebook friend networks
Biased facial recognition: Bank KYC systems failing for certain ethnic groups
Location-based redlining: Entire neighborhoods marked as high-risk
Discussion Questions
How might seemingly "neutral" data like battery charging patterns actually encode social and economic bias?
What are the implications when credit algorithms penalize traditional joint family structures common in South Asia?
How do gender norms around technology use translate into discriminatory credit decisions?
When alternative data excludes people who choose not to use smartphones extensively, is this fair or discriminatory?
Activity: Bias Audit Framework
Scenario: You're auditing an AI system that determines eligibility for microfinance loans for women's self-help groups in rural Maharashtra. The system uses mobile data, transaction history, and social network analysis to make lending decisions.
Audit Steps (Follow These Systematically):
1. Data Audit: What data is collected? From whom? How?
What specific data points are collected?
How representative is the training data?
Are there systematic gaps in data collection?
How is missing data handled?
2. Model Audit: How are decisions made? What factors are weighted?
Which features have the highest weights in the model?
Are there proxy variables for protected characteristics?
How transparent is the decision-making process?
Can decisions be explained to affected individuals?
3. Outcome Audit: Who gets approved/rejected? At what rates?
What are approval rates by gender, caste, region, age?
Are there systematic patterns in rejections?
How do error rates differ across groups?
What are the consequences of false positives vs. false negatives?
4. Impact Audit: How do decisions affect different communities?
What are the long-term effects of credit decisions?
How do algorithmic decisions interact with existing inequalities?
Are there feedback loops that perpetuate bias?
Who benefits and who is harmed by the system?
Group Exercise (25 minutes):
Working in groups of 4-5, design fairness metrics for your local context. Consider:
Individual fairness: Similar people should be treated similarly
Group fairness: Equal outcomes across demographic groups
Counterfactual fairness: Decisions shouldn't depend on protected characteristics
Procedural fairness: Transparent and consistent decision-making process
Policy Solutions Discussion
Regulatory Approaches Globally:
EU GDPR Article 22: Right to explanation for automated decision-making
US Fair Credit Reporting Act: Requirements for credit algorithm transparency
Supreme Court privacy judgments: Foundation for algorithmic rights
Proposed Solutions:
Algorithmic transparency requirements: Mandatory disclosure of decision factors
Community-based auditing: Local organizations conducting fairness assessments
Diverse development teams: Inclusive design from the beginning
Regular bias testing: Ongoing monitoring of system performance
Appeals mechanisms: Human review for contested decisions
Reflection Questions
Fundamental Fairness: Can AI systems be "fair" in societies with deep structural inequality? What would fairness even mean in these contexts?
Audit Rights: Who should have the right to audit algorithms that affect development outcomes? What technical capacity is needed?
Innovation vs. Protection: How do we balance encouraging AI innovation with protecting marginalized communities from algorithmic harm?
Cultural Context: How should algorithmic fairness account for different cultural concepts of equity and justice?
Technical Tools for Bias Detection
IBM AI Fairness 360: Open-source toolkit for detecting and mitigating bias
Google What-If Tool: Visual interface for analyzing ML model behavior
Microsoft Fairlearn: Python library for algorithmic fairness assessment
Aequitas: Bias audit toolkit from University of Chicago
Assignment Options
Bias Case Study: Research a documented case of algorithmic bias in South Asia and analyze the technical, social, and policy dimensions.
Fairness Metrics Design: Develop context-appropriate fairness metrics for a specific development application (health, education, agriculture).
Regulatory Analysis: Compare algorithmic accountability laws across countries and propose adaptations for South Asian contexts.
Community Audit Protocol: Design a process for community organizations to audit AI systems affecting their members.
Further Reading & Resources
Technical: Barocas, S., Hardt, M., & Narayanan, A. "Fairness and Machine Learning" (online textbook)
Policy: Algorithmic Justice League reports and documentation
South Asian Context: IT for Change research on AI and development
Legal Analysis: Data Governance Network policy briefs
Industry Practice: Partnership on AI's algorithmic accountability reports
Digital Development Ethics 101 | ImpactMojo Knowledge Series
Licensed under CC BY-NC-ND 4.0 | For educational use with attribution
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