Explainable AI Tools

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

LLM applications are frustratingly difficult to test due to their probabilistic nature. However, testing is crucial for customer-facing applications to ensure the reliability of generated answers. So, how does one effectively test an LLM app? Enter Confident AI's DeepEval: a comprehensive open-source LLM evaluation framework with excellent developer experience. Key features of DeepEval: - Ease of use: Very similar to writing unit tests with pytest. - Comprehensive suite of metrics: 14+ research-backed metrics for relevancy, hallucination, etc., including label-less standard metrics, which can quantify your bot's performance even without labeled ground truth! All you need is input and output from the bot. See the list of metrics and required data in the image below! - Custom Metrics: Tailor your evaluation process by defining your custom metrics as your business requires. - Synthetic data generator: Create an evaluation dataset synthetically to bootstrap your tests My recommendations for LLM evaluation: - Use OpenAI GPT4 as the metric model as much as possible. - Test Dataset Generation: Use the DeepEval Synthesizer to generate a comprehensive set of realistic questions! Bulk Evaluation: If you are running multiple metrics on multiple questions, generate the responses once, store them in a pandas data frame, and calculate all the metrics in bulk with parallelization. - Quantify hallucination: I love the faithfulness metric, which indicates how much of the generated output is factually consistent with the context provided by the retriever in RAG! CI/CD: Run these tests automatically in your CI/CD pipeline to ensure every code change and prompt change doesn't break anything. - Guardrails: Some high-speed tests can be run on every API call in a post-processor before responding to the user. Leave the slower tests for CI/CD. 🌟 DeepEval GitHub: https://lnkd.in/g9VzqPqZ 🔗 DeepEval Bulk evaluation: https://lnkd.in/g8DQ9JAh Let me know in the comments if you have other ways to test LLM output systematically! Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

If you’re an AI engineer working on fine-tuning LLMs for multi-domain tasks, you need to understand RLVR. One of the biggest challenges with LLMs today isn’t just performance in a single domain, it’s generalization across domains. Most reward models tend to overfit. They learn patterns, not reasoning. And that’s where things break when you switch context. That’s why this new technique, RLVR with Cross-Domain Verifier, caught my eye. It builds on Microsoft’s recent work, and it’s one of the cleanest approaches I’ve seen for domain-agnostic reasoning. Here’s how it works, step by step 👇 ➡️ First, you train a base model with RLVR, using a dataset of reasoning samples (x, a), and a teacher grader to help verify whether the answers are logically valid. This step builds a verifier model that understands reasoning quality within a specific domain. ➡️ Then, you use that verifier to evaluate exploration data - which includes the input, the model’s reasoning steps, and a final conclusion. These scores become the basis for training a reward model that focuses on reasoning quality, not just surface-level output. The key here is that this reward model becomes robust across domains. ➡️ Finally, you take a new reasoning dataset and train your final policy using both the reward model and RLVR again - this time guiding the model not just on task completion, but on step-wise logic that holds up across use cases. 💡 The result is a model that isn’t just trained to guess the answer, it’s trained to reason through it. That’s a game-changer for use cases like multi-hop QA, agentic workflows, and any system that needs consistent logic across varied tasks. ⚠️ Most traditional pipelines confuse fluency with correctness. RLVR fixes that by explicitly verifying each reasoning path. 🔁 Most reward models get brittle across domains. This one learns from the logic itself. 〰️〰️〰️〰️ ♻️ Share this with your network 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

Can your AI system explain why it rejected benefits claims? If not, it's not going into production. Multiple central government departments are now requiring new AI systems, especially those used in decision making like benefit fraud detection. This is being done to pass a formal AI Explainability gate before production rollout. This elevates technical risk and transparency from a compliance checklist to a crucial delivery step. The change responds to both the EU AI Act's ripple effects and local GenAI pilot failures that exposed the risks of deploying black box systems in public facing services. This is the governance maturity the sector needs. Building AI systems is straightforward. Explaining how they reach decisions is significantly harder. The timing matters. Too many pilots have failed not because the technology didn't work, but because nobody could justify the decisions it made. What this means for delivery: → Explainability must be designed in from day one → Technical teams need to document decision logic in plain language for non-technical colleagues → Procurement specifications must now include explainability requirements upfront The departments getting this right are treating explainability as a core architectural requirement, not a final compliance hurdle. This gate will slow some projects initially. But it prevents the far costlier problem of deploying AI systems that make decisions nobody can defend. How is your organisation building explainability into AI systems from the start? #AI #PublicSector #AIGovernance

Product recommendations play a critical role in helping customers discover relevant items and drive engagement and conversion. Even small improvements in recommendation quality can compound at scale, especially in large retail platforms. In a recent tech blog, data scientists from CVS Health shared how they enhanced their existing “You May Also Like” recommendation module by integrating large language models into the system. - At a high level, the recommendation approach is grounded in “similarity”. For any given product, the goal is to identify other products that are similar based on rich product attributes. The overall workflow follows a familiar pattern: generate product embeddings, measure similarity between products, and surface recommendations accordingly. - The key challenge, however, lies in data quality. Not every product comes with a well-written title or detailed description that can be used to generate meaningful embeddings. Some products have extremely short or uninformative metadata, which limits the effectiveness of traditional embedding-based methods. - This is where LLMs add value. For products with sparse or low-quality text, the team leveraged ChatGPT to generate a roughly 200-word product summary, expanding on the product’s purpose, usage, ingredients, and key attributes. These enriched summaries provide higher-quality inputs for embedding generation, improving both coverage and recommendation accuracy. While rebuilding an entire recommendation engine around GenAI can be appealing, this case is a good reminder that incremental, well-targeted improvements often deliver real impact. Integrating LLMs to strengthen weak points in existing systems can be both practical and powerful—and this example is a nice one to keep in mind. #DataScience #MachineLearning #GenAI #LLM #ChatGPT #Recommendation #IncrementalImprovement #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/g7Xv3tG4

Exciting Research Alert: LLM-powered Agents Transforming Recommender Systems! Just came across a fascinating survey paper on how Large Language Model (LLM)-powered agents are revolutionizing recommender systems. This comprehensive review by researchers from Tianjin University and Du Xiaoman Financial Technology identifies three key paradigms reshaping the field: 1. Recommender-oriented approaches - These leverage intelligent agents with enhanced planning, reasoning, and memory capabilities to generate strategic recommendations directly from user historical behaviors. 2. Interaction-oriented methods - Enabling natural language conversations and providing interpretable recommendations through human-like dialogues that explain the reasoning behind suggestions. 3. Simulation-oriented methods - Creating authentic replications of user behaviors through sophisticated simulation techniques that model realistic user responses to recommendations. The paper introduces a unified architectural framework with four essential modules: - Profile Module: Constructs dynamic user/item representations by analyzing behavioral patterns - Memory Module: Manages historical interactions and contextual information for more informed decisions - Planning Module: Designs multi-step action plans balancing immediate satisfaction with long-term engagement - Action Module: Transforms decisions into concrete recommendations through systematic execution What's particularly valuable is the comprehensive analysis of datasets (Amazon, MovieLens, Steam, etc.) and evaluation methodologies ranging from standard metrics like NDCG@K to custom indicators for conversational efficiency. The authors highlight promising future directions including architectural optimization, evaluation framework refinement, and security enhancement for recommender systems. This research demonstrates how LLM agents can understand complex user preferences, facilitate multi-turn conversations, and revolutionize user behavior simulation - addressing key limitations of traditional recommendation approaches.

Do you just trust anyone at their word? Then why trust a model's explanation? This was something that came up in a conversation recently. I don’t disagree. When I first thought about explainability as an AI risk management control, I thought it was hopeless. Even for simpler machine learning models, established post-hoc methods like SHAP and LIME can be unstable. Unfaithful to what the model actually does. Sometimes outright misleading. While there are interpretable machine learning models, you don’t always get to choose. And once we move to deep learning models, Generative AI or AI agents, the black box now looks more like a black hole. But as time passed, I realized there was another way of looking at this. Explainability isn't meant to stand alone. No control for AI risk management is, whether it’s ISO 42001, NIST AI Risk Management Framework, or Singapore’s AI risk management guidelines that I wrote. Think about how you actually trust someone at work. You don't just take their word. You check if their reasoning makes sense for the decision at hand. You notice if they ignore evidence that contradicts them. You watch whether their judgment holds up over time. It’s the same when it comes to looking at AI risk management. Just having explainability is not the be all and end all. Most guidelines have additional provisions that interlock with explainability. The key ones for explainability (in my view). 1️⃣Fit for purpose.  An explanation isn't good or bad in the abstract. It depends on what you need. A fraud analyst needs something different from a customer asking why they got declined. AI used for internal process automation may not need any explanation at all. Same model, different audiences, different standards. Like how you'd explain a medical diagnosis differently to a fellow doctor versus your worried parent. 2️⃣Selected carefully.  When we choose a model or data for a problem, the appropriate explainability method is part of the selection process. Even selecting the right features in your data is part of the process. You wouldn't design a building and think about the fire escape as an afterthought. It's part of the architecture. Same here. How to explain isn't an add-on. It's a design choice. 3️⃣Evaluated and tested.  Explainability is part of the system. You evaluate and test whether it actually works in your context, not just whether it produces output. A smoke detector that beeps isn't the same as one that detects smoke. You test the thing, not just that it makes noise. And there's more, such as the right capability to interpret. But that's another post about human oversight, which also interlocks. The black hole doesn't disappear. But you're no longer staring into the abyss. What other AI risk controls seem hopeless in isolation? I’ll dive into them. #AIRiskManagement #Explainability #AIGovernance

Giving users clear insight into how AI systems think is a smart business strategy that builds loyalty, reduces friction, and keeps people from feeling like they’re at the mercy of a mysterious black box. Explainable AI (XAI) enhances the transparency of AI decision-making, which is vital for customer trust—especially in sectors like finance or healthcare, where stakes are high. Tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) break down complex algorithms into interpretable outputs, helping users understand not just the “what” but the “why” behind decisions. Interactive dashboards translate this data into visual forms that are easier to digest, while personalized explanations align AI insights with individual user needs, reducing confusion and resistance. This approach supports more responsible deployment of AI and encourages wider adoption across industries. #AI #ExplainableAI #XAI #ArtificialIntelligence #DigitalTransformation #EthicalAI

3(+1) new things in #explainableAI for #tabular data Explainable AI #research might seem more interesting and better visualizable for computer vision or NLP models. Still, tables are as prominent as ever in businesses. So are #AI #models built on tabular data. Here are 3 methods that will enhance the toolbox of any tabular data scientist: ❇️ Decision Predicate Graphs turn ensembles of trees into #graphs and give you a new way to observe and measure feature importance, e.g., with centrality measures. I really appreciate that data scientists are the target audience of this method (end users can benefit too, but I think it’s way more exciting and fun in the development process). Paper: https://lnkd.in/eA478xyJ ❇️ CountARFactuals leverage Adversarial Random Forests to generate plausible #counterfactual explanations. The improvement in plausibility can make tree-based models and their explanations actionable. Still, the actionable plausible counterfactuals must be defined together with the stakeholders and validated for business accuracy. Paper: https://lnkd.in/emYaqdi7 ❇️ Deep symbolic #classification is a classification method that performs on par with classical tree-based models like Random Forests and XGBoost but generates decision rules as explanations. And, what’s probably most interesting about this model - it also solves the imbalanced data problem without oversampling techniques. Paper: https://lnkd.in/ekHKz9u8 ➕ My highlight is the interpretability approach for #TabPFN, which might not yet go directly into my toolbox but is definitely a research direction to watch regarding transformers for tabular data. Paper: https://lnkd.in/eK_RqCvP

AI's biggest winners aren't building models. They're building bridges across implementation gaps that kill 85% of projects. After analyzing hundreds of AI initiatives, I've observed a consistent pattern: three critical gaps determine success or failure—integration, interpretability, and indemnity. These gaps form a system with compound effects: → A model that can't be integrated never gets to prove its interpretability. → An uninterpretable model raises liability concerns. → Unclear liability prevents deployment in high-value workflows. This creates a vicious cycle where weakness in any dimension undermines the others. The pattern works in reverse too. Better integration generates more usage data to improve interpretability. Better interpretability reduces perceived risk. Clearer risk frameworks enable deployment in higher-value contexts. Here's what's fascinating: the biggest returns don't go to model creators but accrue to those who bridge these gaps. For early-stage founders, this reveals specific opportunities: 1️⃣ Integration value: Connector platforms, workflow automation tools, and orchestration systems are capturing increasing share as algorithms commoditize. The plumbing becomes more valuable than the water. 2️⃣ Interpretability value: Explanation services, trust frameworks, and audit capabilities command premium pricing because they unlock deployment in regulated industries where returns are highest. 3️⃣ Indemnity value: Risk exchanges, specialized insurance, and compliance automation tools convert uncertainty into priced risk—transforming "no" decisions into "yes, for a fee." This insight should reshape your GTM strategy: If you're building AI tools, position around gap-bridging capabilities rather than raw technical performance. If you're incorporating AI into your product, staff integration engineers first, UX researchers second, and legal/compliance specialists third. If you're investing, direct capital toward companies selling "shovels" for these trenches rather than the models themselves. For founders, the biggest AI opportunity isn't incorporating algorithms into your product—it's solving the integration, interpretability, and indemnity problems that prevent others from doing so. What I'm learning is that building AI businesses is fundamentally about reducing friction. Capabilities are secondary. #startups #founders #growth #ai