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Found 8 papers, 2 papers with code
LangBiTe: A Platform for Testing Bias in Large Language Models
LangBiTe enables development teams to tailor their test scenarios, and automatically generate and execute the test cases according to a set of user-defined ethical requirements.
A Framework to Model ML Engineering Processes
The development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets.
Near to Mid-term Risks and Opportunities of Open Source Generative AI
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education.
Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning
Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions for trustworthy AI, such as the provenance processes and social concerns.
On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning
To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models.
Towards the Automatic Generation of Conversational Interfaces to Facilitate the Exploration of Tabular Data
Tabular data is the most common format to publish and exchange structured data online.
A domain-specific language for describing machine learning datasets
So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets.
The Present and Future of Bots in Software Engineering
We are witnessing a massive adoption of software engineering bots, applications that react to events triggered by tools and messages posted by users and run automated tasks in response, in a variety of domains.
