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Found 6 papers, 4 papers with code
Responsible Design Patterns for Machine Learning Pipelines
In this paper, we propose a comprehensive framework incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical development of AI systems.
An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks
In this paper, we present a qualitative and quantitative analysis of the most frequent dl libraries combination, the distribution of dl library dependencies across the ml workflow, and formulate a set of recommendations to (i) hardware builders for more optimized accelerators and (ii) library builder for more refined future releases.
Reliable Malware Analysis and Detection using Topology Data Analysis
Next, we compare the different TDA techniques (i. e., persistence homology, tomato, TDA Mapper) and existing techniques (i. e., PCA, UMAP, t-SNE) using different classifiers including random forest, decision tree, xgboost, and lightgbm.
Threat Assessment in Machine Learning based Systems
Attacks from the AI Incident Database and the literature are used to identify vulnerabilities and new types of threats that were not documented in ATLAS.
Never trust, always verify : a roadmap for Trustworthy AI?
In this paper, we examine trust in the context of AI-based systems to understand what it means for an AI system to be trustworthy and identify actions that need to be undertaken to ensure that AI systems are trustworthy.
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis
Next, we analyze the current level of AI readiness and current implementations of ethical AI principles in different countries, to identify gaps in the implementation of AI principles and their causes.
