18061689. CHARACTERIZATION FOR ERRONEOUS ARTIFICIAL INTELLIGENCE OUTPUTS simplified abstract (Capital One Services, LLC)
Contents
CHARACTERIZATION FOR ERRONEOUS ARTIFICIAL INTELLIGENCE OUTPUTS
Organization Name
Inventor(s)
Galen Rafferty of Mahomet IL (US)
Samuel Sharpe of Cambridge MA (US)
Brian Barr of Schenectady NY (US)
Jeremy Goodsitt of Champaign IL (US)
Austin Walters of Savoy IL (US)
CHARACTERIZATION FOR ERRONEOUS ARTIFICIAL INTELLIGENCE OUTPUTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18061689 titled 'CHARACTERIZATION FOR ERRONEOUS ARTIFICIAL INTELLIGENCE OUTPUTS
The abstract describes a system where a device can analyze data on reparations issued by an entity utilizing artificial intelligence for providing outputs to users. The device uses a machine learning model to determine an artificial intelligence reparation characterization for the entity, predicting the amount of reparations expected for erroneous outputs. This model is trained to make these determinations based on the data collected, and the device can transmit information regarding the artificial intelligence reparation characterization.
- The device obtains data on reparations issued by an entity using artificial intelligence.
- It uses a machine learning model to determine an artificial intelligence reparation characterization for the entity.
- The characterization predicts the amount of reparations expected for erroneous outputs.
- The machine learning model is trained to make these determinations based on the collected data.
- The device can transmit information about the artificial intelligence reparation characterization.
Potential Applications: - Quality control in artificial intelligence systems - Risk management for entities utilizing artificial intelligence - Compliance monitoring for AI outputs
Problems Solved: - Addressing erroneous artificial intelligence outputs - Providing a predictive model for reparations in AI systems
Benefits: - Improved accuracy in AI systems - Cost savings for entities through proactive reparations management
Commercial Applications: Title: "Predictive AI Reparations System for Error Management" This technology can be used in industries such as finance, healthcare, and customer service to ensure accurate and reliable AI outputs, reducing the risk of costly errors and improving overall performance.
Prior Art: No specific prior art information is provided in the abstract.
Frequently Updated Research: There may be ongoing research in the field of machine learning models for predicting reparations in artificial intelligence systems.
Questions about the technology: 1. How does the machine learning model determine the artificial intelligence reparation characterization? 2. What are the potential implications of inaccurately predicting reparations in AI systems?
Original Abstract Submitted
In some implementations, a device may obtain data indicating reparations issued by an entity that uses artificial intelligence to provide artificial intelligence outputs in connection with users, the reparations being issued for one or more of the artificial intelligence outputs being erroneous. The device may determine, using a machine learning model, an artificial intelligence reparation characterization for the entity. The artificial intelligence reparation characterization determined using the machine learning model may be indicative of an amount of reparations predicted for the entity in connection with uses of artificial intelligence by the entity. The machine learning model may be trained to determine the artificial intelligence reparation characterization based on the data. The device may transmit information indicating the artificial intelligence reparation characterization.
