International business machines corporation (20240111995). PREDICTING FUTURE POSSIBILITY OF BIAS IN AN ARTIFICIAL INTELLIGENCE MODEL simplified abstract

PREDICTING FUTURE POSSIBILITY OF BIAS IN AN ARTIFICIAL INTELLIGENCE MODEL

Organization Name

international business machines corporation

Inventor(s)

Manish Anand Bhide of Hyderabad (IN)

Prateek Goyal of Indore (IN)

PREDICTING FUTURE POSSIBILITY OF BIAS IN AN ARTIFICIAL INTELLIGENCE MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240111995 titled 'PREDICTING FUTURE POSSIBILITY OF BIAS IN AN ARTIFICIAL INTELLIGENCE MODEL

Simplified Explanation

The patent application abstract describes a system for predicting bias in an artificial intelligence model by generating test data and alerting users of potential biased outputs.

• The system includes a memory for storing computer executable components and a processor for executing these components.
• The data generation component generates structured test data to test the likelihood of biased outputs from the AI model based on payload logging data analysis.
• The alerting component notifies users of the likelihood of biased outputs and generates alerts when a defined threshold of biased records is approached.

Potential Applications

This technology could be applied in various industries where AI models are used, such as finance, healthcare, and marketing, to ensure fair and unbiased decision-making processes.

Problems Solved

This technology addresses the issue of bias in AI models, which can lead to unfair outcomes and discrimination. By predicting bias and alerting users, it helps mitigate potential harm caused by biased AI outputs.

Benefits

The system helps improve the transparency and accountability of AI models by proactively identifying and addressing bias. This can lead to more ethical and trustworthy AI applications.

Potential Commercial Applications

One potential commercial application of this technology could be in AI auditing services, where companies can use the system to assess and mitigate bias in their AI models to comply with regulations and ethical standards.

Possible Prior Art

One possible prior art could be existing bias detection tools for AI models that focus on analyzing training data for biases rather than generating test data based on payload logging data analysis.

What are the limitations of this technology in predicting bias in AI models?

The technology may not be able to capture all forms of bias, especially those that are subtle or complex and may require more nuanced analysis beyond structured test data generation.

How can users interpret and act upon the alerts generated by the system effectively?

Users may need guidance or training on how to interpret the alerts and take appropriate actions to address bias in their AI models. Providing resources or best practices for bias mitigation could enhance the effectiveness of the system.

Original Abstract Submitted

one or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to predicting bias in an artificial intelligence (ai) model. a system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components can comprise a data generation component that can generate a set of structured test data to test likelihood of an ai model generating biased outputs, based on analysis of payload logging data; and an alerting component that can alert a user of likelihood that the ai model will generate the biased outputs, wherein the alerting component can generate an alert in response to at least a first set of records approaching a defined threshold.