AI Safety and Alignment
Learn ai safety and alignment through RunLocalAI's practical lens: safety, alignment, red teaming and interpretability, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
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Why this course matters
AI Safety and Alignment is for operators making local AI reliable, measurable and cheaper to run. It connects safety, alignment, red teaming, interpretability and guardrails to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?
What you will be able to do
By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.
How to use this course
Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as AI Safety Landscape, Threat Taxonomy, Adversarial dependableness and Jailbreak Attacks and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.
- 01AI Safety LandscapeLocal AI deployment shifts safety ownership to operators, requiring them to understand alignment principles, dependableness techniques, and interpretability methods—not just deploy models.15 min
- 02Threat TaxonomyThreats cluster into extraction, injection, jailbreaking, and poisoning categories. Mapping these to specific deployment architectures reveals where defensive effort yields the highest return.15 min
- 03Adversarial dependablenessAdversarial dependableness requires systematic testing across fuzzing, boundaries, and known attack patterns. Building dependableness into preprocessing, validation, and degradation strategies reduces vulnerability.20 min
- 04Jailbreak AttacksJailbreaks exploit safety layer weaknesses through role-play framing, context manipulation, and payload splitting. Multi-layered defense—input validation, output monitoring, and prompt hardening—reduces vulnerability.20 min
- 05Prompt Injection DefensePrompt injection exploits application architecture rather than model safety layers. Defensive strategies focus on instruction separation, structured input handling, and output verification rather than content filtering alone.20 min
- 06Red Teaming AutomationAutomated red teaming combines fuzzing for coverage, adversarial generation for targeted attacks, and continuous execution with analysis loops. This enables systematic vulnerability discovery that scales beyond manual testing.20 min
- 07Red Team ToolsRed team tools divide into attack generation, execution infrastructure, and analysis capabilities. Building modular tools that integrate enables flexible, scalable security testing.25 min
- 08Interpretability OverviewInterpretability operates at token, layer, and circuit levels. Each level provides different insights—token attribution reveals input influence, layer analysis shows representation structure, and circuit analysis identifies behavioral mechanisms.20 min
- 09Attention VisualizationAttention visualization transforms opaque model behavior into interpretable displays. Heatmaps, flow diagrams, and pattern analysis reveal safety-relevant behaviors, enabling systematic monitoring and targeted investigation.25 min
- 10Feature AttributionFeature attribution transforms opaque neural computations into human-interpretable rankings. No single method dominates—gradient methods are fast but imprecise, while SHAP values are principled but computationally expensive.15 min
- 11Activation PatchingActivation patching separates correlation from causation. By surgically replacing activations, you discover which circuits are genuinely responsible for an output versus which merely correlate with it.15 min
- 12Bias DetectionBias is multidimensional. No single metric captures all fairness concerns, and metrics can conflict. A thorough bias audit combines statistical tests, embedding analysis, and human evaluation.15 min
- 13Fairness MetricsFairness is not a binary property. Every fairness criterion makes implicit assumptions about which harms matter most. Document your choices and their consequences.15 min
- 14Safety GuardrailsGuardrails are a last line of defense, not a primary strategy. Over-reliance on output filtering creates adversarial incentives and can degrade legitimate use cases.15 min
- 15Constitutional AIConstitutional AI makes the alignment specification explicit and auditable. By writing principles in plain language, non-technical stakeholders can review and contest the model's values directly.15 min
- 16Output FilteringFiltering must balance safety with utility. Over-filtering frustrates legitimate users; under-filtering causes harm. Thresholds should be tunable per deployment context and monitored continuously.15 min
- 17Evaluation BenchmarksBenchmarks codify what "safe" means for a specific application. They must be updated as adversarial tactics evolve and as societal definitions of harm change.15 min
- 18Safety Evaluation Suite ProjectA mature safety evaluation system combines prompt filtering, generation monitoring, and output validation. No single technique suffices—defense in depth requires layering complementary methods that cover each other's failure modes.20 min