CS207 Study Guide

10a. Discuss ethical considerations in ML, including bias and fairness

• What are the five pillars of ethical AI identified in industry frameworks?
• How can algorithmic bias emerge from training data, and what societal impacts can result?
• Why is transparency crucial for ethical ML systems, and how does it relate to accountability?
• What techniques can mitigate discriminatory outcomes in high-stakes domains like hiring or lending?

Creating ethical machine learning systems involves respecting five foundational principles: fairness, transparency, accountability, privacy, and social benefit. These pillars are emphasized in global AI guidelines to ensure responsible development and deployment. Algorithmic bias arises when training data encodes historical inequalities, such as underrepresenting certain demographic groups, leading to unjust outcomes. For example, facial recognition systems may misidentify individuals with darker skin due to biased datasets, which can have serious societal impacts, which are the broader consequences and effects that technology has on communities, institutions, and social structures, from wrongful arrests to loan denials.

Fairness (the principle of ensuring that machine learning systems treat all individuals and groups equitably without discrimination or bias) is promoted through methods like disparate impact analysis (a statistical method for measuring whether an algorithm's decisions disproportionately affect certain demographic groups compared to others) and reweighing training data to correct imbalances. Transparency (the openness and explainability of machine learning systems, allowing stakeholders to understand how decisions are made) requires documentation of data sources, feature selection, and decision logic so that end-users and regulators can understand and audit model behavior. This supports accountability (the principle that organizations and individuals must take responsibility for the consequences and outcomes of their AI systems), which ensures that institutions, not just algorithms, are held responsible when harm occurs. Privacy is another cornerstone, requiring safeguards like differential privacy to protect individuals' data during training and deployment. Finally, social benefit emphasizes that AI applications should align with human welfare and avoid harmful uses, such as surveillance abuse or autonomous weapons.

Many people often conflate technical performance (like "95% accuracy") with ethical soundness, but a model can be accurate and still systematically discriminate against certain groups. Ethical ML is not a one-time checkbox but a continuous process involving bias detection tools (such as AI Fairness 360), regular audits, and diverse stakeholder input.

To review, see:

• Ethical Considerations in ML

10b. Apply basic techniques to detect and mitigate bias in ML models, such as fairness metrics and simple mitigation methods like balanced datasets and re-weighting data

• What are fairness metrics like demographic parity and equal opportunity, and how do they quantify different types of bias?
• How can balanced dataset creation through oversampling or undersampling reduce representation bias?
• What is re-weighting data in model training, and how does it adjust for underrepresented groups?
• Why is disaggregated evaluation critical for identifying hidden bias in subgroups?

Mitigating bias in machine learning requires measurable definitions of fairness and deliberate changes to data and modeling strategies. Fairness metrics provide quantitative tools for bias detection. 

Demographic parity ensures that the selection rate (such as for job offers or loan approvals) is equal across demographic groups. For instance, if Group A receives 60% approvals and Group B only 30%, demographic parity is violated. Equal opportunity focuses on ensuring similar true positive rates, meaning qualified individuals across all groups have an equal chance of being correctly selected (for example, to ensure that equally capable candidates are being hired).

To reduce representation bias, practitioners can balance datasets. Oversampling involves duplicating data from minority groups, while undersampling reduces data from overrepresented groups. Both aim to create a fairer learning process, although oversampling can risk overfitting. Synthetic data generation tools like SMOTE (Synthetic Minority Oversampling Technique) intelligently produce new minority-class examples to preserve variability.

Re-weighting is another strategy, where training gives higher importance (loss weight) to underrepresented samples. For example, in credit models, data points from rural applicants might be weighted more heavily to compensate for their lower representation. Finally, disaggregated evaluation, measuring model performance separately for each subgroup (such as precision for different ethnicities or genders), helps uncover disparities masked by overall averages.

Many people often treat accuracy as sufficient. Fairness requires auditing using fairness metrics, balancing datasets when differences exceed thresholds (such as when there is a higher than 10% difference between groups), and retraining with re-weighting. Regular disaggregated analysis ensures that improvements are meaningful across all groups.

To review, see:

• Responsible AI Practices

Unit 10 Vocabulary

This vocabulary list includes terms you will need to know to successfully complete the final exam.

• accountability
• algorithmic bias
• demographic parity
• disaggregated evaluation
• disparate impact analysis
• fairness
• fairness metric
• oversampling
• privacy
• re-weighting
• social benefit
• societal impact
• transparency
• undersampling