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Found 10 papers, 2 papers with code
Toward General Design Principles for Generative AI Applications
We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.
A Case Study in Engineering a Conversational Programming Assistant's Persona
The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor.
Investigating Explainability of Generative AI for Code through Scenario-based Design
Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.
Using Document Similarity Methods to create Parallel Datasets for Code Translation
However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
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.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
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.
