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US-20260129485-A1 - METHODS FOR EXPLAINABLE AI BASED PERFORMANCE MONITORING

US20260129485A1US 20260129485 A1US20260129485 A1US 20260129485A1US-20260129485-A1

Abstract

A wireless transmit/receive unit (WTRU) may receive configuration information. The configuration information may include criteria for an explainable artificial intelligence (XAI) model to determine a root cause failure (RCF) of a first model. The configuration information may include reporting configuration information and/or a condition associated with at least one XAI metric to determine a RCF type associated with the first model. The WTRU may determine the at least one XAI metric based on an evaluation of the first model. The WTRU may determine the RCF type associated with the first model based on the at least one XAI metric. The WTRU may determine one or more actions based on the determined RCF type. The WTRU may send a report in accordance with the reporting configuration information. The report may indicate the RCF type.

Inventors

  • Mohamed Salah Ibrahim
  • Yugeswar Deenoo Narayanan Thangaraj
  • Mihaela Beluri
  • AKSHAY MALHOTRA

Assignees

  • INTERDIGITAL PATENT HOLDINGS, INC.

Dates

Publication Date
20260507
Application Date
20241104

Claims (20)

  1. 1 . A wireless transmit/receive unit (WTRU) comprising: a processor configured to: receive configuration information, the configuration information comprising criteria for an explainable artificial intelligence (XAI) model to determine a root cause failure (RCF) of a first model, wherein the configuration information comprises reporting configuration information and a condition associated with at least one XAI metric to determine a RCF type associated with the first model; determine the at least one XAI metric based on an evaluation of the first model; determine the RCF type associated with the first model based on the at least one XAI metric; and and send a report in accordance with the reporting configuration information, wherein the report indicates the RCF type.
  2. 2 . The WTRU of claim 1 , wherein the condition associated with the at least one XAI metric comprises one or more of: a minimum score associated with the at least one XAI metric; a maximum score associated with the at least one XAI metric; a rank associated with an impact of one or more input features of the first model; a count of a number of input features of the first model that have a score greater than or less than a first threshold; a mean score associated with one or more input features of the first model; or a variance of the scores associated with one or more input features of the first model.
  3. 3 . The WTRU of claim 1 , wherein the processor is configured to determine one or more actions based on the determined RCF type, and wherein the determined RCF type corresponds to one of a first RCF type or a second RCF type, wherein the first RCF type is associated with a failure due to one or more input features of the first model causing performance degradation of the first model, and wherein the second RCF type is associated with a failure due to the first model causing performance degradation of the first model.
  4. 4 . The WTRU of claim 3 , when the RCF type is the first RCF type, wherein the one or more actions comprises masking or replacing one or more input features associated with the first model.
  5. 5 . The WTRU of claim 1 , wherein the XAI determined XAI metric corresponds to a value determined by the XAI model based on one or more outputs from the first model.
  6. 6 . The WTRU of claim 5 , wherein the at least one XAI metric comprises a respective score determined for each of a plurality of input features for the first model.
  7. 7 . The WTRU of claim 1 , wherein the evaluation of the first model comprises the processor being configured to perform one or more XAI measurements associated with the first model, based on the criteria, to determine the one or more XAI outcomes.
  8. 8 . The WTRU of claim 1 , wherein the at least one XAI metric is determined based on the criteria, wherein the criteria comprises one or more of: performance conditions, AI/ML inference model conditions, time-based conditions, a confidence level of the first model, or an indication from a network.
  9. 9 . The WTRU of claim 1 , wherein the configuration information comprises an indication of one or more trigger conditions that cause the WTRU to execute the XAI model on the first model, and the processor is configured to determine the at least one XAI metric based on the one or more trigger conditions being satisfied.
  10. 10 . The WTRU of claim 1 , wherein the processor being configured to send the report comprises the processor being configured to send an indication of the one or more actions.
  11. 11 . A method performed by a wireless transmit/receive unit (WTRU), the method comprising: receiving configuration information, the configuration information comprising criteria for an explainable artificial intelligence (XAI) model to determine a root cause failure (RCF) of a first model, wherein the configuration information comprises reporting configuration information and a condition associated with at least one XAI metric to determine a RCF type associated with the first model; determining the at least one XAI metric based on an evaluation of the first model; determining the RCF type associated with the first model based on the at least one XAI metric; and sending a report in accordance with the reporting configuration information, wherein the report indicates the RCF type.
  12. 12 . The method of claim 11 , wherein the condition associated with the at least one XAI metric comprises one or more of: a minimum score associated with the at least one XAI metric; a maximum score associated with the at least one XAI metric; a rank associated with an impact of one or more input features of the first model; a count of a number of input features of the first model that have a score greater than or less than a first threshold; a mean score associated with one or more input features of the first model; or a variance of the scores associated with one or more input features of the first model.
  13. 13 . The method of claim 1 , further comprising determining one or more actions based on the determined RCF type, wherein the determined RCF type corresponds to one of a first RCF type or a second RCF type, wherein the first RCF type is associated with a failure due to one or more input features of the first model causing performance degradation of the first model, and wherein the second RCF type is associated with a failure due to the first model causing performance degradation of the first model.
  14. 14 . The method of claim 13 , when the RCF type is the first RCF type, wherein the one or more actions comprises masking or replacing one or more input features associated with the first model.
  15. 15 . The method of claim 11 , wherein the determined XAI metric corresponds to a value determined by the XAI model based on one or more outputs from the first model.
  16. 16 . The method of claim 15 , wherein the at least one XAI metric comprises a respective score determined for each of a plurality of input features for the first model.
  17. 17 . The method of claim 11 , wherein the evaluation of the first model comprises performing one or more XAI measurements associated with the first model, based on the criteria, to determine the one or more XAI outcomes.
  18. 18 . The method of claim 11 , wherein the at least one XAI metric is determined based on the criteria, wherein the criteria comprises one or more of: performance conditions, AI/ML inference model conditions, time-based conditions, a confidence level of the first model, or an indication from a network.
  19. 19 . The method of claim 11 , wherein the configuration information comprises an indication of one or more trigger conditions that cause the WTRU to execute the XAI model on the first model, the at least one XAI metric is determined based on the one or more trigger conditions being satisfied.
  20. 20 . The method of claim 11 , wherein sending the report comprises sending an indication of the one or more actions.

Description

BACKGROUND Development of AI/ML framework may include one or more use cases (e.g. channel state information (CSI) feedback enhancements, beam management, positioning). Model life cycle management (LCM) may be a function included in the AI/ML functional framework (e.g., for NR air interface). LCM may include AI/ML model development, deployment, and/or management during the life cycle. SUMMARY A wireless transmit/receive unit (WTRU) may receive configuration information for explainable artificial intelligence (XAI)-based model performance monitoring. A WTRU may activate an XAI model based on one or more satisfied triggers. A WTRU may determine the root-cause failure (RCF) of the inference model. A WTRU may determine a recommended recovery action. The WTRU may transmit a XAI report. A wireless transmit/receive unit (WTRU) may receive configuration information. The configuration information may include criteria for an explainable artificial intelligence (XAI) model to determine a root cause failure (RCF) of a first model. For example, the XAI-based model may be configured to monitor performance of the first model to determine the RCF of the first model. The configuration information may include reporting configuration information and/or a condition associated with at least one XAI metric to determine a RCF type associated with the first model. The WTRU may determine the at least one XAI metric based on an evaluation of the first model. The WTRU may determine the RCF type associated with the first model based on the at least one XAI metric. The WTRU may determine one or more actions based on the determined RCF type. The WTRU may send a report in accordance with the reporting configuration information. The report may indicate the RCF type. The condition associated with the at least one XAI metric may include one or more of: a minimum score associated with the at least one XAI metric; a maximum score associated with the at least one XAI metric; a rank associated with an impact of one or more input features of the first model; a count of a number of input features of the first model that have a score greater than or less than a first threshold; a mean score associated with one or more input features of the first model; and/or a variance of the scores associated with one or more input features of the first model. The determined RCF type may correspond to one of a first RCF type or a second RCF type. The first RCF type may be associated with a failure due to one or more input features of the first model causing performance degradation of the first model. The second RCF type may be associated with a failure due to the first model causing performance degradation of the first model. When the RCF type is the first RCF type, the one or more actions may include masking and/or replacing one or more input features associated with the first model. The determined XAI metric may correspond to a value determined by the XAI model based on one or more outputs from the first model. The at least one XAI metric may include a respective score determined for each of a plurality of input features for the first model. The evaluation of the first model may include performing one or more XAI measurements associated with the first model, based on the criteria, to determine the one or more XAI outcomes. The at least one XAI metric may be determined based on the criteria. The criteria may include one or more of: performance conditions, AI/ML inference model conditions, time-based conditions, a confidence level of the first model, and/or an indication from a network. The configuration information may include an indication of one or more trigger conditions that cause the WTRU to execute the XAI model on the first model. The WTRU may determine the at least one XAI metric based on the one or more trigger conditions being satisfied. Sending the report may include sending an indication of the one or more actions. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1A is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented. FIG. 1B is a system diagram illustrating an example wireless transmit/receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to an embodiment. FIG. 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communications system illustrated in FIG. 1A according to an embodiment. FIG. 1D is a system diagram illustrating a further example RAN and a further example CN that may be used within the communications system illustrated in FIG. 1A according to an embodiment. FIG. 2 depicts an example flow chart diagram. DETAILED DESCRIPTION FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such