# explainability Published articles for explainability. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The benefits of medical AI assistance vary based on user expertise DevFeed: [The benefits of medical AI assistance vary based on user expertise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-08-04T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Development](), [AI Chat]() Tags: [ai](), [artificial-intelligence](), [bias](), [computer-science-and-technology](), [decision-making](), [dermatological-diagnosis](), [diagnosing-skin-disease](), [diagnostics](), [electrical-engineering-and-computer-science-eecs](), [explainability](), [explainable-ai](), [health-care](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [jameel-clinic](), [laboratory-for-information-and-decision-systems-lids](), [large-language-model](), [machine-learning](), [marzyeh-ghassemi](), [medicine](), [research](), [technology-and-society](), [users]() ### AI overview A study found that AI assistance improved skin-disease diagnosis for non-experts and clinicians, but explainability affected users differently. Non-experts often deferred to LLM-based explanations even when the AI was wrong, while clinicians performed best with the model's prediction alone. ### Source excerpt Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors. ## CAUSAL CONCEPT-BASED EXPLANATIONS DevFeed: [CAUSAL CONCEPT-BASED EXPLANATIONS]() Original publisher: [Read original article]() Author: Jacopo Bono Published: 2025-10-03T13:09:07Z Content type: article Language: en Sources: [Feedzai]() Topics: [Machine Learning & Artificial Intelligence](), [Machine learning](), [AI Development]() Tags: [ai](), [blog-post](), [causality](), [concept-learning](), [deep-learning](), [explainability](), [machine-learning](), [research](), [technical]() ### AI overview The article introduces causal concept-based explanations for machine learning models. It argues that existing feature-attribution explanations can be too complex for humans and usually do not support reasoning about hypothetical alternatives. The proposed method aims to provide more human-understandable, reasoning-oriented explanations, with technical details deferred to a publication at the 2024 CLeaR conference. ### Source excerpt Introduction Over the years, we have evolved from using simple, often rule-based algorithms to sophisticated machine learning models. These models are incredibly good at finding patterns in large datasets, but due to their complexity it is frequently challenging for a human to understand why a certain input leads to its respective output. This is especially problematic in areas where high-stakes decisions are being made and where human-AI collaboration is critical. This is why model explainability has gained traction in recent years. The aim of explainability methods is to shed light on what properties of the data contribute to a machine learning model's output. Ideally, such explanations should be similar to how a human would explain its decisions to a peer. In particular, humans make use of high-level concepts that are easy to grasp for other people. For example, a human would explain, "there was a sudden burst of transactions in a short time" as opposed to, "the average delta time feature is much smaller in a recent short time-window compared to a long time-window, and the count of transactions feature increased substantially in the same recent time-window." Moreover, when explaining choices and decisions, humans are free to reflect on these decisions by reasoning about hypothetical alternatives. For example, we could ask, "if this email address would be considered suspicious, would I trust this request?" Current explainability methods fail to incorporate both properties: the explanations are often much more complex than typical concepts humans would use; and they don't allow for reasoning. As a result, these limitations impede the usefulness of current explainability methods in many practical applications. In this blog post, we will describe a new explainability method that addresses these issues. We'll try to refrain from discussing too many technical details and focus on the intuition behind the method, but readers interested in the technical justifications ar ## What is Machine Learning? DevFeed: [What is Machine Learning?]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-14T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Machine learning](), [Algorithms](), [data](), [Training AI Models]() Tags: [algorithm](), [data](), [explainability](), [machine-learning](), [neural-network](), [training-data]() ### AI overview This tutorial defines machine learning as a technology and as the study of algorithms and statistical models that learn from labeled training data to perform tasks or make predictions without explicit instructions. It explains the roles of corpora, labeling, training data, data-driven pattern recognition, and neural networks. ### Source excerpt I have written a number of posts about different aspects of Machine Learning but, it occurs to me, not a single one that actually says "Machine Learning is xyz and here are the definitions" so here goes. Machine Learning is a technology that allows a computer ("the Machine") to take a "corpus" of "labeled" "training data" and then "learn" how to do something that the training data teaches the computer. So the above is my definition but let's look at Wikipedia: Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.Wikipedia And, while it is a simplification, the underlying thing idea driving machine learning is that of data driven pattern recognition. Basically the Machine Learning process amounts to: Take a bunch of data and construct a set of examples that illustrate what you want the machine to learn. This is called Training Data and the overall collection amounts to a Corpus. Add metadata to these examples in a way that represent what you want the machine to learn. Examples could be blocks of text, images, video files or whatever. I personally work mostly in text but I have machine learning buddies who do things learning on images of cancerous masses. This is called Labeling. Labeling might consist of different things like adding Yes / No / Abstain values to a tweet or even extracting information from a recipe like the ingredie ## Adding Explainability to Machine Learning Routines via Runtime Execution of Labeling Functions DevFeed: [Adding Explainability to Machine Learning Routines via Runtime Execution of Labeling Functions]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-10T00:00:00Z Content type: article Language: en Sources: [Scott Johnson]() Topics: [Machine Learning & Artificial Intelligence](), [Neural Network](), [data](), [Kafka]() Tags: [data](), [explainability](), [kafka](), [machine-learning](), [neural-network](), [python](), [snorkel](), [supervised-learning](), [training]() ### AI overview The article discusses the black-box nature of machine-learning outputs and introduces runtime execution of labeling functions as an approach to adding explainability. It also explains basic concepts including supervised learning, labeled datasets, neural networks, training programs, and models. ### Source excerpt I am spending much of my time these days in the world of machine learning and I'm finding that it is profoundly interesting. One of the most interesting aspects of machine learning, for me, is the black box nature of machine learning's output. What we typically get from a machine learning model what the model thinks but not how it arrived at that result. And this lack of explainability is true for machine learning whether the underlying technology is Tensor Flow for image segmentation or Bert / Roberta / Albert for text processing. Note: Bert / Roberta / Albert are machine learning tools that generate linguistically savvy models that understand English language content. As a side note, given how hard it is for most people to understand why they think what they think, is the black box nature of machine learning actually all that surprising? Machine Learning Basics for the Newbie The world of machine learning, which appears intimidating as hell to the newbie, actually isn't that hard but the terms can be terrifying. Someone recently asked me what I was doing and this was my answer: "I am working on a weekly supervised learning model for an anti Semitic classifier that will be used in a Kafka data pipeline. " Here are the very basics of machine learning: Machine Learning is just that - a way for a machine to learn from data. The underlying idea of machine learning is pretty much just pattern recognition based on data fed through a neural network. To make machine learning work you "label" a data set which means you tell the machine that for Example X, the answer you want is Foo and for Example Y, the answer is Bar. And you do this over and over and over (sometimes you have to label 50,000 examples; that's what my current project uses). You give a program, generally called a training program, this labeled data and then it thinks on this for a while and creates what is called a model. The model is a trained neural network which understands how to process data like what yo