Real
Observed or sourced
A source link, timestamp, runtime event, or model output exists. The interface can point back to where it came from.
AI safety
Understand AI safety risks
with sources attached.
This page explains alignment, governance, frontier risk, and job transition pressure in plain language. Claims are tied to source registry entries or clearly marked as conceptual examples.
glossary for termspublic safety interface
source-linked / conceptual labels / no fabricated incidents
SIGNALWATCH translates AI safety concepts into practical views: what is being watched, where the evidence comes from, and why uncertainty matters before systems are deployed in the world.
alignment
whether systems pursue what humans intended, not only what was measured
governance
how frontier capability is evaluated, constrained, and deployed
labor transition
how task automation changes work before entire occupations disappear
perception safety
how vision systems behave when real-world inputs degrade
before reading safety claims
plain language / trust boundary
Derived
Calculated from real inputs
A summary, grouping, or trace built from source activity, telemetry, or model-output history. It should still show its inputs.
Conceptual
Explanation, not measurement
A teaching example that explains a risk or system behavior without claiming it happened in a deployed system.
Simulated
Controlled demonstration
A parameter-driven demo. Useful for learning, but not evidence about the outside world unless real inputs are attached.
safety monitoring context
illustrative photos / not model evidence
These photos ground the safety page in real operational settings: human review rooms, specialized sensors, and camera coverage. They help non-specialist readers see where AI safety work becomes operational, but they are visual references only, not claims about a deployed SIGNALWATCH run.
visual role
operational context
source status
externally attributed
evidence boundary
not inference output
Human monitoring context
monitoring
Observability tools are useful because humans need clear evidence, not hidden model state.
Fuente: Wikimedia Commons derivative / SIGNALWATCH grade
Sensor calibration boundary
sensor boundary
Different sensors expose different failure surfaces, calibration needs, and operational blind spots.
Fuente: Wikimedia Commons derivative / SIGNALWATCH grade
Coverage mesh deployment
coverage
Coverage, angle, mounting, and maintenance shape what an observability system can actually know.
Fuente: Wikimedia Commons derivative / SIGNALWATCH gradewhy this matters
Autonomous systems become difficult to supervise when capability growth exceeds evaluation and interpretability progress.
trace / OpenAI / Anthropic frameworks
why this matters
AI may automate tasks before entire occupations, increasing transition pressure in some sectors.
trace / OECD / Stanford AI Index
why this matters
Vision systems can fail under degraded environmental conditions despite strong benchmark performance.
trace / conceptual perception safety
alignment
0 conceptsfrontier risk frameworks
0 categoriesjob displacement
sources attachedunaligned AI risk
conceptual examplesAI systems can become dangerous when the measured objective differs from what humans actually intended.
Conceptual failure modes include specification gaming, reward hacking, deceptive behavior risk, autonomous tool use, evaluation gaps, and distribution shift.
These examples are educational abstractions, not claims about a specific deployed system.
source registry
0 evidence objects