Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.
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Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
API misuses in data-centric libraries share key characteristics with deep learning misuses and occur regardless of whether documentation directives are present.
citing papers explorer
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Ethics Testing: Proactive Identification of Generative AI System Harms
Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.
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Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
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An Empirical Study of API Misuses of Data-Centric Libraries
API misuses in data-centric libraries share key characteristics with deep learning misuses and occur regardless of whether documentation directives are present.