Resources for Interpretable Machine Learning

I am thrilled to share our latest algorithm, "SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Regression and Classification." SPINEX was inspired by how we analyze engineering experimental data via scatter plots! Key Highlights about SPINEX: 1. Inherently interpretable (i.e., self explainable). 2. Can handle high-dimensional and imbalanced data. 3. Can be applied to regression and classification problems with ease. 4. We are making its source code available online. I'm incredibly proud of the collaborative effort that went into this side project (with my PhD student, Mohammad AL-Bashiti, M.Sc, EIT, and my brother and newly minted Dr., A.Z. Naser) and is eager to see how SPINEX will influence future developments.   SPINEX can be easily installed as (give it a try): pip install SPINEX from SPINEX import SPINEXRegressor from SPINEX import SPINEXClassifer Link to paper: https://lnkd.in/e4mgAW33 Link to Github source code(s): https://lnkd.in/e_c5EmY4 Link to pypi: https://lnkd.in/eJNRECmH Link to Python scripts: https://lnkd.in/eZ6gxpBe #MachineLearning #AI #DataScience #Algorithm #InterpretableAI

🔎 ⬛ 𝗢𝗽𝗲𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗯𝗹𝗮𝗰𝗸 𝗯𝗼𝘅 𝗼𝗳 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜. Researchers from the University of Washington and Stanford University directed AI algorithms specialized in dermatology to classify images of skin lesions as either potentially malignant or likely benign. Next, they trained a generative AI model linked with each dermatology AI to produce thousands of altered images of lesions, making them appear either "more benign" or "more malignant" according to the algorithm's judgment. Subsequently, two human dermatologists reviewed these images to identify the characteristics the AI used in its decision-making process. This allowed the researchers to identify the features that led the AI to change its classification from benign to malignant. 𝗧𝗵𝗲 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 Their method established a framework – which can be adapted to various medical specialties – for auditing AI decision-making processes, making it more interpretable to humans. 𝗧𝗵𝗲 𝗩𝗮𝗹𝘂𝗲 Such advancements in explainable AI (XAI) within healthcare allow developers to identify and address any inaccuracies or unreliable correlations learned during the AI's training phase, prior to their application in clinical settings. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 XAI is crucial for enhancing the reliability, efficacy, and trustworthiness of AI systems in medical diagnostics. (Links to academic and practitioner sources in the comments.)

Machine learning is as much about developing models as it is about validating them. Here is the #PiML roadmap for machine learning model validation, encapsulating eight pivotal facets that span from data quality, model conceptual soundness to outcome analysis. 🔍 Data Quality: rigorous checks like data integrity, outlier detection, and distribution drift analysis. 🔢 Variable Selection: techniques such as correlation analysis, surrogated model-based feature importance, and conditional independence. 🧮 Model Explainability: model-agnostic explanation tools for feature importance, partial dependence, and local explainability. 📐 Interpretable Benchmarking: adoption of inherently interpretable models for benchmarking both predictive performance and model explainability. 🔬 Weakness Detection: tools like segmented metrics, underfitting and overfitting region detection to diagnose model vulnerabilities. ⚖️ Reliability Test: prediction uncertainty quantification based on conformal prediction, reliability diagrams, and Venn-Abers prediction. 🛡️ Robustness Test: analysis of model performance degradation by input noise perturbation and comparison with benchmark models. 📊 Resilience Test: monitoring distribution drift and identifying sensitive features according to prescribed resilient scenarios. Link: https://lnkd.in/gA7YdzHx