Explore the importance of incorporating diversity and inclusion principles in AI system development through tailored requirements engineering.

Explore the operationalization of diversity and inclusion in AI requirements and the role of GPT-4 in enhancing this process.

Explore the trade-offs and responsibilities in integrating diversity and inclusion in AI systems.

This section presents an evaluation of the proposed OOD detection method using benchmark datasets MNIST and CIFAR10, as well as the GTSRB industrial dataset.

This paper proposes a novel framework for out-of-distribution (OOD) data detection, combining deep learning with statistical measures.

This section outlines a new methodology for out-of-distribution (OOD) detection using deep autoencoders for feature learning and dimensionality reduction

This section provides an in-depth look at various statistical measures for out-of-distribution (OOD) detection

Explore advanced methods for detecting out-of-distribution (OOD) data in AI quality management (AIQM).

Explore the ethical challenges in AI development, from data privacy to bias, and discover how decentralized platforms like ICP are tackling these issues.

Discover the hidden biases in AI systems that reinforce inequality in everyday life. Learn how data-driven technology can perpetuate discrimination.

WEF’s Børge Brende proposes an “algorithm above algorithms” for AI ethics, urging US-China cooperation to tackle cybercrime, inequality, and global challenges.