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Found 14 papers, 8 papers with code
A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services.
FairPrism: Evaluating Fairness-Related Harms in Text Generation
It is critical to measure and mitigate fairness- related harms caused by AI text generation systems, including stereotyping and demeaning harms.
A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
Harmful content detection models tend to have higher false positive rates for content from marginalized groups.
On Measures of Biases and Harms in NLP
Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality.
``Nice Try, Kiddo'': Investigating Ad Hominems in Dialogue Responses
Ad hominem attacks are those that target some feature of a person{'}s character instead of the position the person is maintaining.
Societal Biases in Language Generation: Progress and Challenges
Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner.
Revealing Persona Biases in Dialogue Systems
Dialogue systems in the form of chatbots and personal assistants are being increasingly integrated into people's lives.
Investigating Societal Biases in a Poetry Composition System
There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored.
"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses
Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining.
Towards Controllable Biases in Language Generation
We present a general approach towards controllable societal biases in natural language generation (NLG).
The Woman Worked as a Babysitter: On Biases in Language Generation
We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups.
A Byte-sized Approach to Named Entity Recognition
In biomedical literature, it is common for entity boundaries to not align with word boundaries.
Structured Generation of Technical Reading Lists
Learners need to find suitable documents to read and prioritize them in an appropriate order.
An Investigation into the Pedagogical Features of Documents
We refer to this learning utility as the "pedagogical value" of the document to the learner.
