Artificial Intelligence & Data Science · 1.12

AI Ethics & Safety Researcher

Identify, measure, and mitigate risks in AI systems — covering alignment, fairness, robustness, and governance.

5–7 months5 phases25 skills

Foundations

Philosophy of ethics applied to AIutilitarianism (maximize overall well-being — trolley problem applied to self-driving cars), deontology (rule-based — certain actions are wrong regardless of outcome), virtue ethics (character-focused — what would a responsible AI developer do?), justice theory (Rawls — design AI as if you didn't know which group you'd be in)

Documented AI harms — case studies

Documented AI harms — case studiesAmazon's biased hiring tool (penalized resumes with "women's"), facial recognition errors (higher error rates for darker-skinned women per Buolamwini & Gebru), content moderation failures (amplifying extremism), autonomous vehicle incidents (Uber self-driving fatality), ChatGPT hallucinations and misinformation

Statistics for fairness

Statistics for fairnessconditional probability, base rate fallacy (why a 99% accurate test can still be mostly wrong — relates to algorithmic decisions about rare outcomes), causal vs correlational reasoning (correlation between zip code and creditworthiness may reflect historical redlining, not actual risk)

Python for ethics research

Python for ethics researchdata manipulation with Pandas, basic ML model training with Scikit-learn, visualization with Matplotlib — enough to run bias audits and build analysis pipelines

Phase 02 · 12h

Fairness & Bias

Bias taxonomy

Bias taxonomyhistorical bias (training data reflects past discrimination), representation bias (underrepresenting certain groups in data), measurement bias (features proxy for protected attributes), aggregation bias (one model for heterogeneous populations), evaluation bias (test sets not representative)

Fairness metrics

Fairness metricsdemographic parity (equal positive prediction rates across groups), equalized odds (equal true positive and false positive rates), calibration (predicted probabilities match observed frequencies), individual fairness (similar individuals receive similar predictions), counterfactual fairness (prediction doesn't change if protected attribute were different)

Bias detection tools

Bias detection toolsFairlearn (pip install fairlearn — MetricFrame for computing metrics across groups, ExponentiatedGradient for constrained optimization), AI Fairness 360 (IBM — comprehensive toolkit with 70+ fairness metrics), Google What-If Tool (interactive visualization of model fairness)

Intersectional analysis

Intersectional analysismeasuring bias across combinations of protected attributes (not just gender OR race, but gender AND race — e.g., Black women may face unique biases not captured by analyzing gender or race alone)

Data auditing

Data auditingrepresentation analysis (are all groups proportionally represented?), label quality assessment (are labels consistent across groups?), proxy variable detection (does zip code proxy for race? does name proxy for gender?)

Mitigation strategies

Mitigation strategiespre-processing (re-sampling to balance groups, re-weighting to adjust instance importance), in-processing (adversarial debiasing — train a model that can't predict the protected attribute from its representations, constrained optimization with Fairlearn), post-processing (threshold adjustment — use different classification thresholds per group to equalize error rates)

Phase 03 · 9h

Safety & Robustness

Adversarial attacks

Adversarial attacksevasion attacks (imperceptible perturbations fool classifiers — FGSM, PGD), data poisoning (inject malicious training examples), model inversion (reconstruct training data from model outputs), membership inference (determine if a specific record was in training data)

Red-teaming LLMs

Red-teaming LLMssystematic prompt injection testing (ignore previous instructions, jailbreak prompts), social engineering attacks (roleplay scenarios that bypass safety), harmful content generation testing, evaluating refusal quality (does the model refuse appropriately without being overly restrictive?)

Alignment

AlignmentRLHF (Reinforcement Learning from Human Feedback — train a reward model from human preferences, optimize the policy with PPO against the reward model), Constitutional AI (Anthropic's approach — self-supervised with a set of principles), DPO (Direct Preference Optimization — simpler alternative to RLHF), understanding current limitations (reward hacking, specification gaming, mesa-optimization)

Hallucination

Hallucinationcauses (training data memorization, distributional shift, lack of grounding), detection methods (self-consistency checking, retrieval-based fact verification, uncertainty quantification), grounding strategies (RAG, tool use, explicit citations)

Robustness testing

Robustness testingout-of-distribution (OOD) detection (model should know when it doesn't know), stress testing with edge cases, distribution shift simulation

Security

Securitymodel stealing (querying a model to train a clone), training data extraction from LLMs, supply chain risks (poisoned pretrained models, compromised libraries)

Phase 04 · 10h

Governance & Policy

Regulatory frameworks

Regulatory frameworksEU AI Act (risk-based classification — prohibited uses like social scoring, high-risk systems like hiring/credit requiring conformity assessments, limited risk requiring transparency, minimal risk with no requirements), NIST AI RMF (Govern, Map, Measure, Manage framework), UNESCO AI Ethics recommendation, ISO 42001 (AI management system standard)

Impact assessments

Impact assessmentsAlgorithmic Impact Assessments (AIAs — structured evaluation of potential harms before deployment), Data Protection Impact Assessments (DPIAs — required under GDPR for high-risk processing)

Transparency artifacts

Transparency artifactsmodel cards (Mitchell et al. — document intended use, training data, performance, limitations, ethical considerations), datasheets for datasets (Gebru et al. — document motivation, composition, collection process, preprocessing), system cards (for deployed AI systems — document the full system including human review processes)

Explainability

ExplainabilitySHAP (SHapley Additive exPlanations — show each feature's contribution to individual predictions, based on game theory), LIME (Local Interpretable Model-agnostic Explanations — approximate complex model locally with a simple interpretable model), attention visualization for transformers, concept-based explanations (TCAV — Testing with Concept Activation Vectors)

Organizational AI governance

Organizational AI governanceestablishing AI ethics review boards, designing review processes for high-risk AI systems, incident response plans for AI failures (discovery → containment → root cause analysis → remediation → communication)

Societal implications

Societal implicationslabor displacement (automation of cognitive tasks), surveillance (facial recognition, behavioral monitoring), deepfakes (detection methods, policy responses), environmental costs of AI (training compute carbon footprint — use ML CO2 Impact calculator)

Phase 05 · 14h

Capstone

Capstone Project 1

Capstone Project 1Full fairness audit of a deployed ML system — identify protected groups, compute fairness metrics across groups with Fairlearn, analyze intersectional disparities, propose mitigation strategies (pre/in/post-processing), measure trade-offs between fairness and accuracy, write a comprehensive audit report

Capstone Project 2

Capstone Project 2Red-team evaluation of an LLM application — design a red-teaming protocol with 100+ test cases across harm categories (misinformation, harmful instructions, bias, privacy leakage, prompt injection), execute tests, classify failures, write a safety report with severity ratings and remediation recommendations

Capstone Project 3

Capstone Project 3AI governance framework proposal — design an organizational AI governance structure (review board charter, risk classification rubric, review process flowchart, incident response plan, monitoring requirements), write policy documents, present to mock executive committee

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