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5 results for “AI Incidents”
HawaiiCoast_GT: Curated AIS for Hawaii's coast correlated with ground truth incidents
<p>Because of the high-risk nature of emergencies and illegal activities at sea, it is critical that algorithms designed to detect anomalies from maritime traffic data be robust. However, there exist no publicly available maritime traffic datasets with real-world labelled anomalies. As a result, most anomaly detection algorithms for maritime traffic are validated without ground truth. We introduce the HawaiiCoast_GT dataset, the first ever publicly available automatic identification system dataset with a large corresponding set of true anomalous incidents. This dataset—cleaned and curated from Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration (NOAA) automatic identification system (AIS) data--covers Hawaii’s coastal waters for four years (2017-2020) and contains 88,749,176 AIS points for a total of 2,622 unique vessels. 208 tracks are labelled corresponding to 154 labelled real-world incidents. The codebase used to curate the original AIS data is being made openly available on GitHub.</p>
Diversity and Inclusion (D&I)- Related AI Incidents Repository
<p>This is a repository of Diversity and Inclusion (D&I) related AI incidents. This repository is proposed in our recently submitted paper titled "AI for All: Identifying AI incidents Related to Diversity and Inclusion".</p>
Diversity and Inclusion (D&I)- Related AI Incidents Repository
<p>This repository is a part of the research "AI for All: Identifying Diversity and Inclusion Related AI incidents".</p> <p>We developed the first version of D&I-related AI incidents repository based on the AIID database. Each record in this repository encompasses details such as the incident ID, title, description, date, alleged deployer of AI system, alleged developer of AI system, and alleged harmed or nearly harmed parties. Each incident ID is linked to a corresponding article for a more detailed account of the incident. Further, in the repository, we added the status in relation to D&I, diversity attributes such as age, gender, ethnicity, race and so on, and the reason supporting our determination. We have outlined three categories under the "status" column to categorize the incidents: related to D&I (R), not related to D&I (NR), and more information required (MIR).</p>
AI-Enabled Electrocardiogram-Guided Guideline-Directed Medical Therapy on Incident Left Ventricular Dysfunction: A Target Trial Emulation Study
ClinicalTrials.gov study NCT07355023. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease
ClinicalTrials.gov study NCT06695273. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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