XMANAI

Exciting News Alert! Join us for the XMANAI Webinar: XMANAI XAI Platform! Are you ready to unlock the power of Explainable Artificial Intelligence? Dive deep into the novel XMANAI Platform and witness firsthand its potential for shaping the future of AI.

By advocating "glass box" models, XMANAI enables an understanding of AI reasoning, revolutionizing the value chain with Explainable AI while upholding European values. #xmanai #artificialintelligence #explainableai #xai #ai

The XMANAI project envisions AI as a transparent ally collaborating with humans. At our final review meeting in Milan, attended by European Commission representatives, we celebrated advancements in creating a human-centric, trustworthy AI for manufacturing.

New video on our YouTube channel! In this video, explore CNH's participation in the XMANAI project, alongside discussions on engaging with maintenance engineers and maintainers to enhance operational efficiency! #xmanai #artificialintelligence #ai #xai #cnh

New video on our YouTube channel! In this video, Enrica Bosani, manufacturing innovation project manager at Whirlpool presents a general overview of Whirlpool's developments and main achievements in the XMANAI project! #xmanai #artificialintelligence #ai #xai #whirlpool

Our focus on Explainable AI highlights its potential to drive human progress in the manufacturing sector. Curious to learn more about artificial intelligence and explainable AI?  Visit our website now: ai4manufacturing.eu #XMANAI #AI #IoT #Manufacturing #ExplainableAI #IESA

Exciting news! The XMANAI project recently participated in the I-ESA event, showcasing groundbreaking research on the integration of AI, IoT, and data-driven strategies in manufacturing.

Evaluate explainers on various levels with fitting validation methods, considering operational context. lnkd.in/d_KRDSKw #xmanai #artificialintelligence #ai #xai #explainableai

Observations: 1) inconsistent XAI evaluation, 2) context neglect, 3) oversimplified human processes, 4) undivided XAI system focus, 5) model quality ignored. Framework separates models, explanations, interfaces, and protocols.

What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A sociotechnical utility-based evaluation framework for XAI is proposed in this paper, starting from main observations derived from the state of the art of XAI evaluation.

Challenges include tech reliance, deskilling, and learning needs, emphasizing a blend of human and AI tasks. lnkd.in/d23eZAXv #xmanai #artificialintelligence #ai #xai #explainableai

A Research Framework Focused on AI and Humans instead of AI versus Humans. This paper examines AI's future, contrasting AI versus Humans with AI and Humans approaches. It advocates for human-centered design, balancing tech advancement with human needs for inclusive societies.

How computational argumentation be applied to enhance the AI explainability tool's effectiveness? An interesting update from the XAIworld conference 2024 in Malta this July: lnkd.in/dCjM-G-n #xmanai #artificialintelligence #ai #xai #explainableai #xai

They propose research strategies to tackle these, mobilizing stakeholders. Learn more here: lnkd.in/dcFZpjJn #xmanai #artificialintelligence #ai #xai #explainableai

Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Authors seek to advance XAI through collaboration, identifying 28 dilemmas across 9 categories in a manifesto.

It proposes Calibrated-XAI, a framework for creating Human-Centred XAI interfaces to minimize such errors. Learn more here: lnkd.in/dcCxgVtj #xmanai #artificialintelligence #ai #xai #explainableai

C-XAI: A conceptual framework for designing XAI tools that support trust calibration The article highlights trust calibration errors in human-AI collaboration and advocates for addressing them in design.

New video on our YouTube channel! If you missed our last webinar about the XMANAI Platform, here's your chance to watch it! lnkd.in/dAy_JCk5 #xmanai #artificialintelligence #ai #xai #webinar #xmanaiplatform

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence | Cognitive Computation (springer.com) #xmanai #artificialintelligence #ai #xai #explainableai

This symbiosis enables understanding AI decisions and refining performance through feedback. If you are interested in going deeper into the root causes that lead to the need for XAI, please read the following:

Why Explainability Matters? XAI tackles the opacity of AI models, striving for explainability and trust. It fosters collaboration between humans and machines, leveraging each other's strengths: human intuition and AI's analytical power.