Python libraries that implement agnostic global explainability methods 👇  . #machinelearning #interpretability #datascience #mlmodels #python #dataengineering #globalmodel

During the last couple of years, we have read a lot of papers on explainability and often felt that something was fundamentally missing🤔. This led us to write a position paper (accepted at #ICML2025) that attempts to identify the problem and to propose a solution. Introducing:

Hybrid AI architectures with improved explainability and interpretability, and the predictive model will provide industry:. Reduction of downtime by 30%.Human/machine cooperation.Highly skilled jobs.Increasing decision power & safety.📍ULTIMATE project.

Excited to share our paper: "Chain-of-Thought Is Not Explainability"! . We unpack a critical misconception in AI: models explaining their Chain-of-Thought (CoT) steps aren't necessarily revealing their true reasoning. Spoiler: transparency of CoT can be an illusion. (1/9) 🧵

Another exciting day at #SIGIR2025! ⚡️ Lightning talks are done—now it’s poster time:.📌 GINGER in action at the LiveRAG Challenge .📌 Explainability in conversational IR at WExIR (w/ @damiano10, @JTrippas, @krisztianbalog).Catch us at the poster session!

The Trust Problem with Analytics: Explainability➡ #Data #Analytics #Statistics #DataScience #MachineLearning #AdvancedAnalytics #AI #BI #DataQuality #DataManagement #DataGovernance #AnalyticsGovernance #AIGovernance.

[LG] Chain-of-Thought Is Not Explainability.F Barez, T-Y Wu, I Arcuschin, M Lan. [Oxford & WhiteBox] (2025).

The keynote on Responsible AI at #AIS2025 sparked urgent conversations on bias mitigation, transparency, and explainability.

After attending the panel at the Explainability for IR workshop, now attending my student's talk at the IR4RAG workshop. This paper is a first step towards building an adaptive multi-agent RAG pipeline. @craig_macdonald @DanielTian97

Key Difference: btw Mira Network and other AI verifiable solutions. @Mira_Network ensures trust through decentralization and blockchain, . While other-AI relies on explainability and human-guided models. tbh I'm going all into Mira . This is why I'm very interested in the

How important is explainability in AI tools?.

In this clip from "At the Forefront", Martina Rejsjö of Eventus and Vinod Jain of @DatosInsights discuss probabilistic vs. deterministic models, and why explainability matters more than ever. Listen to the full episode: #AI #fintech #compliance #regtech

Algorithmic integrity is the hot topic at #AIS2025. Day 1 brought sharp focus to fairness, transparency, and explainability.

Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models

AI trust & reliability concepts:. Confidence Score.Explainability (XAI).Model Rationale / Chain-of-Thought.Source Attribution.Uncertainty Quantification.Anomaly / Risk Flags.Ethical Alignment Score.Performance Monitoring Metrics.Prompt Traceability.Data Lineage / Provenance

What if radiologists could use AI systems as a reliable diagnostic assistant? Mila student @Amar_kumar_1's new paper (Oral 1.B at MIDL) uses generative AI to improve the explainability of AI models for medical imaging. Read his blog post:

We don’t trust systems because they get everything right. We trust them because their behavior is predictable and makes sense over time. Explainability isn’t UX polish, it’s a systems problem. Most people assume explainability means transparency. That if an agent can’t

Transparency and Explainability:. Prompt: "A medical AI system recommends a specific treatment for a patient, but the doctor doesn't understand why the AI made that recommendation. How would you design the AI to provide more transparent and explainable outputs to build trust with.

As AI adoption rises, trust & quality matter more than ever. AssureAi helps you assess AI accuracy, robustness, explainability & performance in 6-10 weeks. Get your clear quality score + tailored insights.Learn more with our one-pager: #SoftwareTesting