We hypothesize that transparent design reduces cognitive load required to predict system behavior.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• System transparency and familiarity lower the probability of failure when users customizeclaim0.792
  If customization logic resembles familiar structures (spreadsheets, before-after chains), users are more likely to successfully modify the system.
• Constraints and biases of human intelligence are best avoided by ‘designing for emergence’ rather than implementing a pre-existing theory of intelligence.claim0.755
  Recommendation for creating non-anthropocentric machine intelligence.
• Scaling intelligence via expansion of cognitive boundaries through inclusion of others' stress-reduction in one's own homeostatic loops.claim0.747
  Central thesis: expanding an agent's sensors and goals outward to include others' states creates bidirectional feedback loop that scales intelligence and increases compassion.
• S suggests practical diagnostics for prompt design, retrieval, and light fine-tuning without additional training infrastructure.claim0.746
  Applied contribution.
• AI systems which possess more of the indicator properties are more likely to be conscious.claim0.742
  Graded claim about the rubric.
• Good interface design reveals the underlying computational architecture to the user.claim0.741
  Core interpretive claim about the relationship between transparency and usability.
• Cognitive functions, including sentience, can potentially be achieved by very different systems and their disparate substrates.claim0.740
  Central multiple-realizability claim of the paper, from abstract and §2.
• What makes design so very difficult for most people is that they do not use a generative sequence; instead they try to design all at once, in a jumble.claim0.736
  Diagnosis of why conventional design methods produce struggle and chaos, contrasted with the ease of proper sequence.