Building AI Confidence at Scale: Why Cognitive Automation Needs a Trust-First Architecture
(Authored by Shashi Bhushan, Chairman of the Board, Stellar Innovations)
The Rise of Cognitive Automation in Enterprises
#TycoonWorld
#AITrust
#CognitiveAutomation
#ResponsibleAI
#EnterpriseAI
#AIConfidence
#TrustFirst
#AIGovernance
#ExplainableAI
#EthicalAI
#AISecurity
#AutomationAtScale
#DigitalTransformation
#IntelligentEnterprises
#AIArchitecture
#FutureOfWork
🤖 Wir können viele (Routine-)Aufgaben an KI-Systeme abgeben.
⚠️ Die Verantwortung für die Ergebnisse aber nicht.
Welche Voraussetzungen verantwortungsvoller Gebrauch von #KI hat und warum menschliche Expertise weiterhin gefragt ist, erklärt Matthias Peissner, Fraunhofer IAO und Mitglied der Plattform @LernendeSysteme, im Video.
➡️ https://youtube.com/shorts/WyXxwEbnXHU?feature=share
🎥 Zur Langversion des Interviews zu Job-Profilen im Wandel durch KI:
We want AI to explain itself, but today’s explainable AI mostly offers post-hoc rationalizations, not real insight into how decisions are made. True explainability still remains elusive, especially in complex models.
A fascinating long-term research project from the University of Zurich: “The Canon of Latent Spaces: How Large AI Models Encode Art and Culture.”
It investigates how multimodal AI models—trained on millions of image-text pairs—encode cultural memory, reproduce biases, and shape future artistic and cultural production. A much-needed analytical and critical perspective on the aesthetics, politics, and epistemology of latent spaces.
#DigitalArtHistory #ExplainableAI
https://latentcanon.github.io/
Explainable > black box. ACE could give #longevity its first regulator-friendly ageing readout: pathway-level features, pre-specified Context of Use, and a credible validation plan. Our take
https://biotech.industryexaminer.com/explainable-aging-clock-ace-regulatory-biomarker-roadmap/
#TechNews #biomarkers #ExplainableAI #RegulatoryScience #Biotech
A consortium of 17 partners is creating human-centered AI grounded in fairness, accountability, transparency, and ethics. 🤝
A clear mission, with a precise solution.
Find out our vision for human-centered AI in our first official press release: https://aixpert-project.eu/2025/11/03/eu-funded-project-aixpert-launches-building-trustworthy-transparent-and-ethical-ai-for-real-world-applications/
#AIXPERT #AIethics #ExplainableAI #HumanCentricAI
#HaDEA #HorizonEurope #AI
#ITByte: #ExplainableAI (#XAI), often known as Interpretable AI, or Explainable Machine Learning (XML), either refers to an AI system over which it is possible for humans to retain intellectual oversight, or to the methods to achieve this.
The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent.
https://knowledgezone.co.in/trends/explorer?topic=Explainable-AI
🧠“Explainability is not a luxury – it’s a competitive advantage.”
In connect professional, our Head of Artificial Intelligence, Prof. Wojciech Samek, explains why #ExplainableAI is key to improving systems, building trust & driving innovation.
đź”— https://www.connect-professional.de/software-services/erklaerbare-ki-403880.html
🤖 What does it take to make AI trustworthy, explainable, and ethical?
đź’ˇ AIXPERT's newly launched website gives you our take on building AI that earns your trust, and so much more! From our mission, goals, and partners information, to application fields!
đź”— https://aixpert-project.eu/
We especially appreciated the evening talks:
➡️ Jan Rybicki (Jagiellonian University) asked whether #ChatGPT could imitate Hemingway,
➡️ Katherine Bode (Australian National University) explored the materiality of #computing,
➡️ Damien Garreau (University of Würzburg) introduced #explainableAI. (2/4)
The last paper presented was Hegde et al.'s "Model-Manipulation Attacks Against Black-Box Explanations," exploring vulnerabilities in explanation methods like LIME and highlighting the need for trustworthy alternatives. (https://www.acsac.org/2024/program/final/s304.html) 6/6
#TrustworthyAI #ExplainableAI
Two weeks left to submit your poster paper to our Symposium!
Call for Posters: DiTraRe Symposium on Digitalisation of Research 2025
Submission deadline: Oct 17, 2025
Submission link: https://easychair.org/my/conference?conf=ditraresymposium2025
Conference website: https://www.ditrare.de/en/symposium-2025
#AI #generativeAI #explainableAI #agenticAI #reliableAI #digitalisation #research #cfp @fiz_karlsruhe @fizise @KIT_Karlsruhe @ITAS @AnnaJacyszyn @Feelix #knowledgegraphs #semanticweb #datascience #science #legal #ethics #innovation #cfp
Three weeks left to submit your poster paper to our Symposium!
Call for Posters: DiTraRe Symposium on Digitalisation of Research 2025
Submission deadline: Oct 17, 2025
Submission link: https://easychair.org/my/conference?conf=ditraresymposium2025
Conference website: https://www.ditrare.de/en/symposium-2025
#AI #generativeAI #explainableAI #reliableAI #digitalisation #research #cfp @fiz_karlsruhe @fizise @KIT_Karlsruhe @ITAS @AnnaJacyszyn @Feelix #knowledgegraphs #semanticweb #datascience
PEAR: a novel loss term enhances interpretability and confidence in deep learning models for tabular data, hence boosting consensus in AI explainers. https://hackernoon.com/the-geeks-guide-to-ml-experimentation #explainableai
Discover how PEAR increases explainer consensus, paving the way for deep learning models that are fair, interpretable, and prepared for the future. https://hackernoon.com/can-pear-make-deep-learning-easier-to-trust #explainableai
Learn how PEAR exhibits optimal consensus when both loss terms are balanced, improves linearity, and preserves useful, non-trivial explanations. https://hackernoon.com/consensus-loss-proves-ai-can-be-both-accurate-and-transparent #explainableai
Learn how PEAR offers interpretability improvements with negligible accuracy tradeoffs by improving explainer agreement across measures and invisible explainers https://hackernoon.com/the-trade-off-between-accuracy-and-agreement-in-ai-models #explainableai
