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Found 10 papers, 0 papers with code
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
Quantitative AI Risk Assessments: Opportunities and Challenges
These risks have led to proposed regulations, litigation, and general societal concerns.
Evaluating a Methodology for Increasing AI Transparency: A Case Study
The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases.
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team.
Towards evaluating and eliciting high-quality documentation for intelligent systems
In this paper, we propose and evaluate a set of quality dimensions to identify in what ways this type of documentation falls short.
A Methodology for Creating AI FactSheets
This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets.
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers.
Detecting Egregious Conversations between Customers and Virtual Agents
In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction.
