Displaying 7 resources
Case studies Case studies

Evaluation and test protocols

This is deliverable 4.1 of AI4REALNET project.

Category
human-centric ai, Data for AI, Experiment and Deployment, Systems, methodologies, hardware, and tools
Target audience
ADR Experts and Associations, professionals, Researchers and Academic
Source
Adra-e
Video/Webinars Video/Webinars

Webinar Towards Transparent, Safe and Trustworthy AI for critical infrastructures

This webinar focuses on the development of safe, explainable, and algorithmically transparent methods as part of the AI4REALNET project.

Category
Action and Interaction Technologies, Data for AI, Reasoning and decision-making Technologies, Sensing and perception technologies, Systems, methodologies, hardware, and tools
Target audience
ADR Experts and Associations, Policy Makers, Private Sector, Public Sector, Researchers and Academic
Source
Adra-e
Article/Books/eBooks Article/Books/eBooks

Don’t ask if AI is good or fair, ask how it shifts power

Opinion piece by Pratyusha Kalluri in Nature
Category
Support guidance in the responsible implementation of ADR, Understanding of the fundamental rights and values
Target audience
ADR Experts and Associations, Individual Citizens/Members of the Society, Policy Makers, Researchers and Academic
Source
Adra-e
Other Other

Is ethical AI possible?

An interview with Timnit Gebru, the founder of the Distributed AI Research Institute.
Category
Support guidance in the responsible implementation of ADR, Understanding of the fundamental rights and values
Target audience
ADR Experts and Associations, Individual Citizens/Members of the Society, Researchers and Academic
Source
Adra-e
Article/Books/eBooks Article/Books/eBooks

Pygmalion Displacement: When Humanising AI Dehumanises Women

Paper exploring the relationship between women and AI.
Category
Support guidance in the responsible implementation of ADR, Understanding of the fundamental rights and values
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Article/Books/eBooks Article/Books/eBooks

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

When deployed, machine-learning (ML) adoption depends on its ability to actually deliver the expected service safely, and to meet user expectations in terms of quality and continuity of service.
Category
Support guidance in the responsible implementation of ADR
Target audience
ADR Experts and Associations, Policy Makers, Private Sector, Public Sector, Researchers and Academic
Source
Adra-e