17956857. EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS simplified abstract (International Business Machines Corporation)

EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS

Organization Name

International Business Machines Corporation

Inventor(s)

MATTIA Rigotti of BASEL (CH)

IOANA Giurgiu of ZURICH (CH)

THOMAS Gschwind of ZURICH (CH)

CHRISTOPH ADRIAN Miksovic Czasch of ZURICH (CH)

PAOLO Scotton of RUESCHLIKON (CH)

EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17956857 titled 'EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS

Simplified Explanation

The abstract describes a patent application for a neural network model that generates explainable prediction outputs for input data samples. The model includes a cross-attention module and a prediction module to produce sample embeddings and prediction outputs, respectively.

• Training dataset provided with data samples and prediction labels
• Concept vectors defined for information content characterization
• Input vectors produced from each data sample
• Neural network model trained with cross-attention and prediction modules

Potential Applications

This technology could be applied in various fields such as healthcare, finance, and marketing for making accurate and interpretable predictions based on input data samples.

Problems Solved

1. Lack of transparency in traditional neural network models 2. Difficulty in understanding the reasoning behind predictions

Benefits

1. Improved interpretability of prediction outputs 2. Enhanced trust in the model's decisions 3. Better insights into the features influencing predictions

Potential Commercial Applications

Optimizing marketing strategies, personalized healthcare recommendations, fraud detection in financial transactions

Possible Prior Art

One possible prior art could be the use of attention mechanisms in neural networks for improving model performance and interpretability.

How does this technology compare to other explainable AI models in terms of performance and interpretability?

This article does not provide a direct comparison with other explainable AI models, leaving the reader to wonder about the relative advantages and disadvantages of this specific technology.

What are the specific limitations or constraints of implementing this neural network model in real-world applications?

The article does not address the potential challenges or limitations that may arise when implementing this technology in practical scenarios, leaving room for speculation on the feasibility and scalability of the model.

Original Abstract Submitted

Generating a neural network model for producing explainable prediction outputs for input data samples is provided. Training dataset of data samples are provided, each having a prediction label indicating a desired prediction output from the model for that sample, and a set of concept vectors are defined comprising a plurality of concept vectors which are associated with respective predefined concepts characterizing information content of the data samples. A set of input vectors are produced from each data sample. A neural network model is trained that includes a cross-attention module for producing a sample embedding for a data sample and a prediction module for producing a prediction output from the sample embedding.