Explore tweets tagged as #explainability
⬛ Have you heard of Black Box AI?. AI's exist on a spectrum of explainability, where black box AI has internal working invisible to the user. ➡️ Learn more about explainability and predictive power:
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We are delighted to share that Dr. Vandana Bharti, Assistant Professor, CSE, presented and published the paper "When Explainability Meets Vision AI: Analyzing CNNs, Transformers, and State-Space Models in Healthcare" at the 38th International Joint Conference on Neural Networks
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🚀 Open Science in action!. A new study on deep learning for cancer detection (PCam dataset, PyTorch, Grad-CAM explainability) is now published on NeuronovAI Academic — with full reproducible code. NeuronovAI empowers researchers to share, collaborate, and build the future of
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What does explainability in AI image interpretation really mean—and is it even what physicians want?. Happy to share this collab in Nature @SciReports, led by @AndreaStoras & @kelkalot, re: explainability in GI endoscopy, w/ lessons for other fields.
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This paper shows how to make AI explain itself across the whole pipeline, start to finish. This work extends explainability to cover data choices, training decisions, checks on quality, and how results are presented. Introduces Holistic Explainable Artificial Intelligence
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Latest papers: Francisco Cruz & André Mata investigate lay perceptions of explainability of psychological phenomena through two studies
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More people should know about Captum. They are a model interpretability library for PyTorch with a crazy collection of explainability algorithms - gradient based, SHAP-based, LIME-based. It's actually insane how cool this is.
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⬛ Have you heard of Black Box AI?. AI's exist on a spectrum of explainability, where black box AI has internal working invisible to the user. ➡️ Learn more about explainability and predictive power:
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Check out the latest article in my newsletter: AI Explainability, Interpretability, and Transparency | AI Policy Overview for Builders, Part 5 via @LinkedIn .#AI #ResponsibleAI #AITransparency #ExplainableAI #InterpretableAI #AIRegulation #EthicalAI.
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Web3 promised transparency. AI promised intelligence. @OpenLedgerAI merges both. Models aren’t locked away they’re deployed on-chain with explainability, clear provenance, and a reward layer that ensures contributors aren’t left behind. This isn’t just AI infra, it’s
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The Explainability Illusion: Why AI Transparency Requirements Miss the Point #artificialintelligence #communications #AI @joneswalker.
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의료인분들은 Explainability를 중요하게 여기시는군 🥰.
오늘 아침에 회의 갔다가 잠깐 나왔던 디스커션.- 의료 데이터로 ML 예측모델 만들어서 feature importance를 분석하면 어떤 feature가 예측에 얼마나 기여하는지 알 수 있고, 대개 병인에도 관련되어있는 것이 나옴.- NLP를 이용해 free text로 뭔가 예측/분류를 하는 것이 가능한데 비슷하게 되나?.
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The Attribution Engine makes AI transparent by proving where every token comes from. 1/ Transparent inference – Every output is traceable. 2/ Contributor credits – Realtime rewards for data. 3/ On-chain explainability – Immutable audit trail. @OpenledgerHQ is the AI you can
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I have question to ask from @OpenledgerHQ will team member answer this question ?. 1: How does OpenLedger AI Studio’s focus on explainability and attribution improve trust compared to typical AI models?. 2: How might OpenLedger’s approach to rewarding contributors transform
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Improving Explainability and Integrability of Medical AI to Promote Health Care Professional Acceptance and Use: Mixed Systematic Review
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How much explainability do you want (why you see an item)? (None, short, detailed).
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Generic LLMs often hallucinate and miss context. We rely on AI every day, but not all models are built for your world. @OpenledgerHQ AI Studio offers a new way forward. Build specialized models trained on your own data, with full explainability, attribution, and traceability.
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New Research: Explainability in machine learning: a pedagogical perspective #FrontiersIn #Education.
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