Search Results for author:
Found 9 papers, 2 papers with code
Quantitative AI Risk Assessments: Opportunities and Challenges
Although AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified.
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.
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.
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.
