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5 results for “human-in-the-loop”
Human-AI Collaboration: A tool to enable AI model generation with human-in-the-loop
<p>Human-AI collaboration enables domain experts to contribute their expertise with the goal of enhancing the knowledge learned by the AI models from the patterns in the data. This enables the integration of domain-specific knowledge to enrich the data for further improvement of the models through retraining. The human-AI collaboration is composed of multiple sub-components and interfaces that enables communication with external systems such as data sources, model repositories, machine configurations and decision support systems.</p> <p>Human-AI Collaboration component is developed using Python programming language. The frontend is developed using Streamlit1. The backend is developed using python and the API is implemented using FastAPI2. The choice of the programming language was made because of its wide usage and vast user base. The frameworks Streamlit and FastAPI are chosen because of the rich features for functionality and documentation as well as suitability for data analysis tasks. The applications are packaged as docker images for deployment. The application runs as a web application served by nginx for reverseproxying and users can access it via client applications such as web browsers or REST clients like Postman.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery
<p>The datasets used in this study are provided in 7 folders:</p> <ol> <li> <p>"dataset A", containing data from Sandwich Town Neck Beach on Cape Cod, Massachusetts. These images are published as a USGS data series (Sherwood et al., 2021) are publicly available at <a href="https://doi.org/10.5066/P9BFD3YH">https://doi.org/10.5066/P9BFD3YH</a></p> </li> <li> <p>"dataset B", containing data from North and South Carolina collected immediately after Hurricane Florence in October 2018. National Geodetic Survey emergency response imagery courtesy of the National Oceanic and Atmospheric Administration, available at <a href="https://storms.ngs.noaa.gov/">https://storms.ngs.noaa.gov</a></p> </li> <li> <p>"dataset C", containing some examples of shoreline environments captured by a low-altitude aircraft. These images are published as a USGS data series (Kranenburg et al., 2020) are publicly available at <a href="https://doi.org/10.5066/P9CA3D8P">https://doi.org/10.5066/P9CA3D8P</a></p> </li> <li> <p>"dataset D", containing data collected from the Pearl River and its tributary the Bogue Chitto, and from the Chickasawhay, Buoy and Leaf tributaries of the Pascagoula River, in spring 2021. Used with permission from U.S. Fish and Wildlife Service</p> </li> <li> <p>"dataset E", containing Sentinel-2 satellite images of coastal lagoon environments in Salinas Rivermouth Natural Preserve and National Wildlife Refuge in Monterey, California. Sentinel-2 imagery courtesy of European Space Agency (ESA)</p> </li> <li> <p>"dataset F", containing Landsat-8 of Cape Hatteras, Cape Hatteras National Seashore, North Carolina. Landsat-8 imagery is courtesy of U.S. Geological Surve</p> </li> <li> <p>"code", containing a version of the code used to generate the results contained in this data repository. Full details about this code can be obtained from the github code repository (<a href="https://github.com/dbuscombe-usgs/dash_doodler">https://github.com/dbuscombe-usgs/dash_doodler</a>) and website (<a href="https://dbuscombe-usgs.github.io/dash_doodler/">https://dbuscombe-usgs.github.io/dash_doodler/</a>).</p> </li> </ol>
Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery
Open the record for dataset details and reuse information.
Knowledge Graph Triple Validation by LLMs and Human-in-the-Loop
<p>Suplementary material for the sumbitted article to the IPM Special issue on<strong> </strong><em>Large Language Models and Data Quality for Knowledge Graphs. </em></p> <p>The dataset is an extension of [1] and includes the following columns:</p> <ul> <li><em>subj</em> the subject/head of the triple</li> <li><em>rel</em> the predicate of the triple</li> <li><em>obj</em> the object/tail of the triple</li> <li><em>support-level </em>indicating the reliability of the triple</li> <li><em>ann-random</em>[1: valid, 0: invalid], randomly selected annotation from the expert annotations avaialble in [1] </li> <li><em>ann-new</em> [1: valid, 0: invalid], junior expert annotation </li> <li><em>gpt-4o-1 </em>[1: valid, 0: invalid], response from 1st GPT prompt</li> <li><em>gpt-4o-2 </em>[1: valid, 0: invalid], response from 2nd GPT prompt</li> <li><em>gpt-4o-3</em> [1: valid, 0: invalid], response from 3rd GPT prompt</li> <li><em>gpt-4o-majority </em>[1: valid, 0: invalid], GPT annotation, computed as majority vote of gpt-4o-1,gpt-4o-2,gpt-4o-3</li> <li><em>claude-1 </em>[1: valid, 0: invalid], response from 1st <em>claude</em> prompt</li> <li><em>claude-2 </em>[1: valid, 0: invalid], response from 2nd <em>claude</em> prompt</li> <li><em>claude-3</em> [1: valid, 0: invalid], response from 3rd <em>claude</em> prompt</li> <li><em>claude-majority </em>[1: valid, 0: invalid], <em>claude</em> annotation, computed as majority vote of <em>claude</em>-1,<em>claude</em>-2,<em>claude</em>-3</li> <li><em>llama-1 </em>[1: valid, 0: invalid], response from 1st <em>llama</em> prompt</li> <li><em>llama-2 </em>[1: valid, 0: invalid], response from 2nd <em>llama</em> prompt</li> <li><em>llama-3</em> [1: valid, 0: invalid], response from 3rd <em>llama</em> prompt</li> <li><em>llama-majority </em>[1: valid, 0: invalid], <em>llama</em> annotation, computed as majority vote of <em>llama</em>-1,<em>llama</em>-2,<em>llama</em>-3</li> </ul> <p>[1] https://github.com/danilo-dessi/SKG-pipeline/tree/main/eval </p>
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