topic subject gender 
Marktechpost
FEBRUARY 23, 2024
Despite the extensive efforts to address biases concerning gender, race, and religion, the geographic dimension has remained relatively underexplored. These biases manifest vividly in predictions related to subjective topics such as attractiveness and morality, where areas like Africa and parts of Asia were systematically undervalued.
AssemblyAI
AUGUST 3, 2023
From the large-scale proliferation of biased or false information to risks of psychological distress for chatbot users, the potential for misuse of language models is a subject of intense debate. It can produce fluent responses on a wide range of topics. There is a wealth of interconnected topics waiting to be explored.
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Unite.AI
DECEMBER 1, 2023
Similarly, LLMs in hiring processes may inadvertently perpetuate gender bi ases. Bias and Knowledge Gaps LLMs' processing of vast training data is subject to the limitations imposed by their algorithms and model architectures. Also, an LLM's proficiency in niche topics could be misleading, leading to overconfident, incorrect outputs.
NVIDIA
MARCH 1, 2024
Topical guardrails ensure that chatbots stick to specific subjects. Racial and gender bias in data are well-known, but other considerations include cultural bias and bias introduced during data labeling. Safety guardrails set limits on the language and data sources the apps use in their responses.
Hal Daumé III
DECEMBER 12, 2016
And yet, despite this recent interest in ethics-related topics, none of the major organizations that I'm involved in have a Code of Ethics, namely: the ACL , the NIPS foundation nor the IMLS. The LSA code and CIL code are related but cover slightly different topics. should this go elsewhere?)
PyImageSearch
JANUARY 30, 2023
This technology, “smart” classrooms, and other immersive educational experiences provide new and more effective ways to teach science, geography, and other subjects. Such a framework of ethics needs to ensure that gender, socio-economic, and ability biases are not introduced at the development level.
Kavita Ganesan
MARCH 29, 2023
This is why large language models (LLMs) are not free from biases when they’re quizzed on subjective topics. Even using a snapshot of the Web to train models can mean you’ve learned the biases in that snapshot.
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