Survey experiment finds that people apply more deontological standards to AI described as human-programmed and to the programmers themselves than to unaided humans or unprogrammed robots in a moral dilemma.
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A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
Annotators' competence in recognizing social influence techniques increases during the annotation process, more pronounced in experts, visibly affecting LLM performance on the resulting data.
citing papers explorer
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The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers
Survey experiment finds that people apply more deontological standards to AI described as human-programmed and to the programmers themselves than to unaided humans or unprogrammed robots in a moral dilemma.
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Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
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How Annotation Trains Annotators: Competence Development in Social Influence Recognition
Annotators' competence in recognizing social influence techniques increases during the annotation process, more pronounced in experts, visibly affecting LLM performance on the resulting data.