TRUST MANAGEMENT FRAMEWORK FOR AI/ML PIPELINES WITH EXPLAINABILITY FACTOR

Abstract

ABSTRACT TRUST MANAGEMENT FRAMEWORK FOR AI/ML PIPELINES WITH EXPLAINABILITY FACTOR The present disclosure introduces trust management framework for AI/ML pipelines with explainability factor 100 which enhances transparency, accountability, and ethical compliance across the AI/ML lifecycle. It comprises of key components like explainability assessment module 102 for evaluating model transparency, dynamic transparency visualization dashboard 104 for real-time insights into model behavior, and stakeholder-centric explainability interfaces 106 for presenting tailored information to different users. The automated bias detection and mitigation mechanism 108 ensures fairness, while the context-aware user consent framework 110 manages data privacy dynamically. The collaborative development environment 112 facilitates co-creation with stakeholders, supported by the continuous learning feedback loop 114 for integrating real-world feedback. Cross-platform compliance audit tools 116 ensure regulatory adherence, and the explainability-centric model selection algorithms 118 prioritize interpretability in model recommendations. Together, these components promote responsible AI development by fostering trust, enabling dynamic adaptation, mitigating bias, ensuring compliance, and supporting user control and engagement. Reference Fig 1

Specification

Description:Trust Management Framework for AI/ML Pipelines with Explainability Factor
TECHNICAL FIELD
[0001] The present innovation relates to trust management framework for AI/ML pipelines that integrates explainability factor to enhance transparency, accountability, and ethical compliance throughout the AI/ML lifecycle.

BACKGROUND

[0002] The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across industries such as healthcare, finance, education, and public services has brought about significant advancements. However, the increasing reliance on AI/ML systems presents challenges related to trust, transparency, and accountability. A common issue is the "black box" nature of many models, especially deep learning systems, where stakeholders struggle to interpret or understand how decisions are made. This opacity leads to bias, erosion of trust, and potential regulatory non-compliance. Current solutions offer some degree of transparency, such as explainable models, model documentation, and fairness audits, but these approaches are either limited in scope, overly technical, or inconsistently applied, making it difficult to ensure long-term trust in AI/ML systems.
[0003] Existing explainable AI (XAI) tools primarily focus on model-level transparency but often lack mechanisms for bias detection, continuous monitoring, stakeholder engagement, and ethical compliance. Additionally, users are burdened with the task of manually interpreting complex reports, limiting accessibility for non-technical stakeholders. These challenges highlight the need for a comprehensive trust management framework that not only emphasizes explainability factor but also integrates ethical guidelines, user consent mechanisms, and collaborative features throughout the AI/ML pipeline.
[0004] The proposed Trust Management Framework for AI/ML Pipelines with Explainability Factor addresses these limitations by embedding transparency, fairness, and accountability across the entire AI/ML lifecycle. It offers dynamic dashboards, stakeholder-specific interfaces, automated bias detection, real-time monitoring, and context-aware consent management. The framework ensures compliance with regulatory standards and provides a collaborative environment for multi-stakeholder engagement. What sets this invention apart is the integration of adaptive learning policies and cross-domain explainability transfer, allowing the framework to dynamically adjust explanations and insights across different applications. This comprehensive approach enhances trust and ensures the responsible deployment of AI/ML systems, promoting a sustainable and ethical AI ecosystem.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance trust in AI/ML systems by providing a comprehensive framework that integrates explainability factor throughout the lifecycle.

[0006] Another object of the invention is to promote transparency and accountability by offering real-time monitoring tools and dynamic dashboards for model behavior visualization.

[0007] Another object of the invention is to ensure fairness and prevent bias by incorporating automated bias detection and mitigation mechanisms within the framework.

[0008] Another object of the invention is to facilitate regulatory compliance by aligning with data privacy standards and offering context-aware consent management.

[0009] Another object of the invention is to support stakeholder engagement through multi-stakeholder collaboration tools, ensuring diverse input throughout the AI/ML development and evaluation process.

[00010] Another object of the invention is to improve accessibility by providing tailored, stakeholder-specific interfaces that present model explanations in user-friendly formats.

[00011] Another object of the invention is to enable adaptive learning through feedback loops, allowing AI/ML models to evolve and improve based on real-world user interactions and reported issues.

[00012] Another object of the invention is to foster ethical AI development by embedding ethical guidelines and standards into each phase of the AI/ML pipeline, from model design to deployment.

[00013] Another object of the invention is to increase interoperability by offering cross-domain explainability transfer, enabling the reuse of explanatory principles across different industries and applications.

[00014] Another object of the invention is to enhance resilience and reliability by incorporating continuous performance tracking and compliance audits, ensuring long-term effectiveness and ethical compliance of AI/ML systems.


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, trust management framework for AI/ML pipelines with explainability factor is presented. It integrates explainability, transparency, and ethical compliance throughout the system lifecycle. It addresses challenges like model opacity, bias, and regulatory non-compliance by offering tools for real-time monitoring, bias detection, and stakeholder-specific explanations. The framework ensures multi-stakeholder collaboration and provides adaptive feedback loops to improve performance over time. It supports compliance with privacy laws through context-aware consent management and offers cross-domain explainability for diverse applications. This invention promotes trust, accountability, and responsible AI development across industries.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for trust management framework for AI/ML pipelines with explainability factor.

[00021] FIG 2 is working methodology of trust management framework for AI/ML pipelines with explainability factor.

DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of trust management framework for AI/ML pipelines with explainability factor and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, trust management framework for AI/ML pipelines with explainability factor 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of explainability assessment module 102, dynamic transparency visualization dashboard 104, stakeholder-centric explainability interfaces 106, automated bias detection and mitigation mechanism 108, context-aware user consent framework 110, collaborative development environment 112, continuous learning feedback loop 114, cross-platform compliance audit tools 116, explainability-centric model selection algorithms 118, adaptive learning policies 120, explainability rule engine 122, simulated scenario analysis module 124, integrated ethical compliance framework 126, real-time explainability adjustment mechanism 128, cross-domain explainability transfer system 130, anomaly detection and reporting system 132, model transparency certification process 134, user education and training modules 136, adaptive feedback collection framework 138, ethical decision-making toolkit 140.

[00029] Referring to Fig. 1, the present disclosure provides details of trust management framework for AI/ML pipelines with explainability factor 100. It enhances transparency, accountability, and fairness across the AI/ML lifecycle. It integrates features like real-time monitoring, adaptive learning, automated bias detection, and stakeholder-specific interfaces to ensure responsible AI deployment. Key components include the explainability assessment module 102, automated bias detection and mitigation mechanism 108, continuous learning feedback loop 114, and context-aware user consent framework 110. The dynamic transparency visualization dashboard 104 provides clear insights into model behavior, while the collaborative development environment 112 fosters stakeholder engagement. This framework also ensures regulatory compliance through cross-platform compliance audit tools 116 and ethical alignment with the integrated ethical compliance framework 126. These components work together to build trust in AI/ML technologies by promoting explainability, transparency, and ethical use

[00030] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with explainability assessment module 102, which evaluates the interpretability and stability of AI/ML models. It provides scores that reflect how well the models align with the required transparency standards. This module works in conjunction with dynamic transparency visualization dashboard 104 to display explainability metrics in a user-friendly manner, enabling stakeholders to make informed decisions about model deployment.

[00031] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with dynamic transparency visualization dashboard 104, which offers real-time insights into model behavior, data provenance, and decision-making pathways. This component allows both technical and non-technical users to explore how input data influences model outputs. It interacts closely with stakeholder-centric explainability interfaces 106 to present relevant information tailored to different user roles, enhancing understanding and engagement.

[00032] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with stakeholder-centric explainability interfaces 106, which deliver customized views and explanations for developers, regulators, and end-users. These interfaces adapt based on feedback and display model insights from dynamic transparency visualization dashboard 104. They enhance communication between stakeholders by providing explanations that are accessible and meaningful according to the user's expertise and needs.

[00033] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with automated bias detection and mitigation mechanism 108, which continuously scans data inputs and model outputs for potential biases. When bias is detected, the system applies corrective algorithms to ensure fairness. It operates alongside explainability assessment module 102 to ensure that the model remains both interpretable and unbiased throughout its operation.

[00034] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with context-aware user consent framework 110, which dynamically manages consent based on data usage context and compliance with privacy laws. It ensures users are informed about how their data is utilized, providing them with control and transparency. This framework integrates with collaborative development environment 112 to align consent management processes with evolving ethical standards during development and deployment.

[00035] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with collaborative development environment 112, which facilitates co-creation among stakeholders through version control, documentation sharing, and feedback loops. It ensures that ethical considerations are integrated from the beginning of the development process. This environment works seamlessly with continuous learning feedback loop 114 to incorporate real-time insights into model updates and improvements over time.

[00036] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with continuous learning feedback loop 114, which collects user feedback to refine and adapt models post-deployment. This feedback loop ensures that AI/ML systems evolve based on real-world interactions and challenges. It collaborates with automated bias detection and mitigation mechanism 108 to ensure that models stay fair and unbiased as they learn and improve.

[00037] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with cross-platform compliance audit tools 116, which automate regulatory compliance checks. These tools ensure that AI/ML systems adhere to standards. They work closely with context-aware user consent framework 110 to align data privacy policies with legal requirements throughout the system's lifecycle.

[00038] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with explainability-centric model selection algorithms 118, which prioritize interpretability in model selection processes. These algorithms recommend models based on user-defined criteria and transparency benchmarks. They interoperate with explainability assessment module 102 to ensure selected models are both performant and explainable.

[00039] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with adaptive learning policies 120, which enable AI/ML models to adjust their strategies according to user behavior and demographics. These policies ensure models remain effective and relevant across diverse user groups. They leverage insights from continuous learning feedback loop 114 to dynamically modify model parameters based on evolving user needs.

[00040] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with explainability rule engine 122, which generates customized explanations based on predefined criteria. This rule engine applies different rules for various models and use cases to ensure the generated explanations are contextually appropriate. It works with stakeholder-centric explainability interfaces 106 to present the explanations in a way that aligns with the user's role and expertise.

[00041] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with simulated scenario analysis module 124, which allows users to conduct "what-if" simulations to explore how models respond under different conditions. This feature enhances confidence in model predictions by revealing behavior under varying inputs. It integrates with dynamic transparency visualization dashboard 104 to visually present the outcomes of simulated scenarios.

[00042] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with integrated ethical compliance framework 126, which ensures that AI/ML models align with ethical guidelines and best practices throughout their lifecycle. This framework monitors adherence to ethical standards and is supported by collaborative development environment 112, which embeds ethical considerations during development.

[00043] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with real-time explainability adjustment mechanism 128, which dynamically modifies explanations based on user feedback and interactions. It ensures that the clarity and relevance of explanations are maintained over time. This mechanism interacts closely with stakeholder-centric explainability interfaces 106 to enhance the user experience during model interaction.

[00044] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with cross-domain explainability transfer system 130, which allows explanations developed for one domain to be adapted and applied in other domains. This promotes knowledge transfer and best practices across industries. It leverages explainability rule engine 122 to ensure that the transferred explanations are meaningful and relevant in the new context.

[00045] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with anomaly detection and reporting system 132, which identifies unexpected model behaviors or outcomes and generates alerts for further investigation. It enhances system robustness by ensuring swift responses to anomalies. This system integrates with continuous learning feedback loop 114 to learn from identified anomalies and improve model performance.

[00046] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with model transparency certification process 134, which offers structured assessments and certifications for model transparency. This certification builds trust by providing a standardized evaluation of the model's trustworthiness. It operates alongside explainability assessment module 102 to certify that models meet transparency benchmarks.

[00047] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with user education and training modules 136, which empower users to understand AI/ML concepts, interpret model outputs, and engage with the system effectively. These modules promote user literacy in AI ethics and transparency, fostering informed feedback that supports continuous learning feedback loop 114.

[00048] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with adaptive feedback collection framework 138, which gathers user feedback through various channels, such as surveys and in-app prompts. This feedback helps identify areas for improvement and ensures that the system adapts to evolving user needs. It works closely with collaborative development environment 112 to incorporate feedback into future updates.

[00049] Referring to Fig 1, trust management framework for AI/ML pipelines with explainability factor 100 is provided with ethical decision-making toolkit 140, which offers resources and guidelines for making ethical decisions during the design and deployment of AI/ML systems. It supports developers and stakeholders in addressing complex ethical challenges and ensures alignment with integrated ethical compliance framework 126 throughout the system's operation


[00050] Referring to Fig 2, there is illustrated method 200 for trust management framework for AI/ML pipelines with explainability factor 100. The method comprises:

At step 202, method 200 includes user initiating the process by evaluating the AI/ML model using the explainability assessment module 102 to generate interpretability scores and identify transparency levels;

At step 204, method 200 includes system displaying the model behavior insights on the dynamic transparency visualization dashboard 104 to allow stakeholders to visually explore decision pathways, input-output relationships, and data provenance;

At step 206, method 200 includes the stakeholders interacting with the system through the stakeholder-centric explainability interfaces 106, which customize and present relevant model information to different users, such as developers, regulators, and end-users, in accessible formats;

At step 208, method 200 includes the automated bias detection and mitigation mechanism 108 continuously monitoring data inputs and outputs, identifying potential bias patterns, and applying corrective measures such as re-weighting or algorithmic adjustments to ensure fairness;

At step 210, method 200 includes the system managing user consent dynamically using the context-aware user consent framework 110, which aligns data usage with privacy regulations and provides users with control over their data interactions through transparent communication;

At step 212, method 200 includes collaborative development activities facilitated by the collaborative development environment 112, where stakeholders engage in co-creation through version control, documentation sharing, and feedback loops to ensure alignment with ethical guidelines during the development process;

At step 214, method 200 includes real-time feedback from users being integrated into the continuous learning feedback loop 114, allowing the AI/ML model to adapt, improve, and evolve based on real-world interactions, usage patterns, and reported issues;

At step 216, method 200 includes the system conducting automated compliance checks using the cross-platform compliance audit tools 116 to verify that the AI/ML model aligns with relevant privacy laws and regulatory standards;

At step 218, method 200 includes developers utilizing the explainability-centric model selection algorithms 118 to recommend interpretable models for deployment, based on transparency scores, user-defined criteria, and ethical considerations;

At step 220, method 200 includes the system generating context-appropriate explanations using the explainability rule engine 122, which applies predefined rules to generate customized explanations tailored for different models and stakeholders;

At step 222, method 200 includes stakeholders conducting "what-if" simulations through the simulated scenario analysis module 124 to explore how the AI/ML model behaves under various scenarios, enhancing trust and confidence in the system's predictions;

At step 224, method 200 includes the integrated ethical compliance framework 126 ensuring the AI/ML system follows ethical guidelines throughout its lifecycle by continuously evaluating adherence to ethical principles and best practices;

At step 226, method 200 includes the real-time explainability adjustment mechanism 128 dynamically modifying explanations based on user interactions and feedback, enhancing user understanding and trust during system operation;

At step 228, method 200 includes cross-domain knowledge transfer facilitated by the cross-domain explainability transfer system 130, which adapts explanatory principles developed in one domain for application in another, promoting best practices across industries;

At step 230, method 200 includes the anomaly detection and reporting system 132 identifying unexpected behavior or outliers in the AI/ML model's performance and generating alerts for immediate investigation;

At step 232, method 200 includes the model undergoing a transparency certification using the model transparency certification process 134, which evaluates and certifies the model's transparency features for increased stakeholder trust;

At step 234, method 200 includes users engaging with educational materials provided by the user education and training modules 136, empowering them to understand AI/ML concepts, system outputs, and the importance of transparency;

At step 236, method 200 includes the adaptive feedback collection framework 138 gathering user feedback through various channels, such as in-app prompts or surveys, prioritizing the feedback based on urgency and relevance for model improvements;

At step 238, method 200 includes developers and stakeholders utilizing the ethical decision-making toolkit 140 to address complex ethical dilemmas during the design, deployment, and operation of AI/ML systems, ensuring the system remains aligned with ethical guidelines and societal values.

[00051] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00052] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00053] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A trust management framework for AI/ML pipelines with explainability factor 100 comprising of
explainability assessment module 102 to evaluate the interpretability and transparency of ai/ml models;

dynamic transparency visualization dashboard 104 to display real-time insights into model behavior and decision pathways;

stakeholder-centric explainability interfaces 106 to present tailored explanations based on the roles of different users;

automated bias detection and mitigation mechanism 108 to monitor and correct biased patterns in data inputs and outputs;

context-aware user consent framework 110 to manage user consent dynamically based on data usage context and privacy regulations;

collaborative development environment 112 to facilitate co-creation, version control, and feedback sharing among stakeholders;

continuous learning feedback loop 114 to integrate real-time user feedback for ongoing model refinement and adaptation;

cross-platform compliance audit tools 116 to ensure adherence to privacy laws and regulatory standards;

explainability-centric model selection algorithms 118 to recommend interpretable models based on transparency criteria;

adaptive learning policies 120 to adjust model strategies according to user behavior and demographics;

explainability rule engine 122 to generate customized explanations using predefined rules for various models and stakeholders;

simulated scenario analysis module 124 to conduct "what-if" simulations to assess model behavior under different conditions;

integrated ethical compliance framework 126 to align ai/ml models with ethical principles and best practices throughout their lifecycle;

real-time explainability adjustment mechanism 128 to modify explanations dynamically based on user interactions;

cross-domain explainability transfer system 130 to adapt and apply explanatory principles across different industries;

anomaly detection and reporting system 132 to identify and report unexpected model behaviors for further investigation;

model transparency certification process 134 to certify the transparency and trustworthiness of ai/ml models;

user education and training modules 136 to empower users with knowledge of ai/ml concepts and system outputs;

adaptive feedback collection framework 138 to gather and prioritize user feedback through various channels;

ethical decision-making toolkit 140 to guide stakeholders in addressing complex ethical dilemmas during ai/ml development and deployment.

2. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein explainability assessment module 102 evaluates AI/ML model transparency and interpretability using standardized metrics to ensure informed decision-making.

3. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein dynamic transparency visualization dashboard 104 provides real-time insights into model behavior, decision pathways, and data provenance through interactive visualizations.

4. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein automated bias detection and mitigation mechanism 108 continuously monitors data inputs and outputs to identify, report, and correct biases, ensuring fairness throughout the AI/ML lifecycle.

5. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein context-aware user consent framework 110 dynamically manages user consent based on data usage context and regulatory compliance, empowering users with control over their data.

6. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein collaborative development environment 112 facilitates co-creation among stakeholders through version control, documentation sharing, and real-time feedback loops.

7. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein continuous learning feedback loop 114 integrates user feedback in real-time to refine and adapt AI/ML models based on real-world interactions and evolving requirements.

8. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein cross-platform compliance audit tools 116 automates regulatory compliance checks to ensure adherence to privacy laws, ethical standards, and industry guidelines.

9. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein explainability-centric model selection algorithms 118 recommends interpretable models by prioritizing transparency and aligning with predefined ethical and performance criteria.

10. The trust management framework for AI/ML pipelines with explainability factor 100 as claimed in claim 1, wherein method comprises of
user initiating the process by evaluating the AI/ML model using the explainability assessment module 102 to generate interpretability scores and identify transparency levels;
system displaying the model behavior insights on the dynamic transparency visualization dashboard 104 to allow stakeholders to visually explore decision pathways, input-output relationships, and data provenance;
stakeholders interacting with the system through the stakeholder-centric explainability interfaces 106, which customize and present relevant model information to different users, such as developers, regulators, and end-users, in accessible formats;
automated bias detection and mitigation mechanism 108 continuously monitoring data inputs and outputs, identifying potential bias patterns, and applying corrective measures such as re-weighting or algorithmic adjustments to ensure fairness;
system managing user consent dynamically using the context-aware user consent framework 110, which aligns data usage with privacy regulations and provides users with control over their data interactions through transparent communication;
collaborative development activities facilitated by the collaborative development environment 112, where stakeholders engage in co-creation through version control, documentation sharing, and feedback loops to ensure alignment with ethical guidelines during the development process;
real-time feedback from users being integrated into the continuous learning feedback loop 114, allowing the AI/ML model to adapt, improve, and evolve based on real-world interactions, usage patterns, and reported issues;
system conducting automated compliance checks using the cross-platform compliance audit tools 116 to verify that the AI/ML model aligns with relevant privacy laws and regulatory standards;
developers utilizing the explainability-centric model selection algorithms 118 to recommend interpretable models for deployment, based on transparency scores, user-defined criteria, and ethical considerations;
system generating context-appropriate explanations using the explainability rule engine 122, which applies predefined rules to generate customized explanations tailored for different models and stakeholders;
stakeholders conducting "what-if" simulations through the simulated scenario analysis module 124 to explore how the AI/ML model behaves under various scenarios, enhancing trust and confidence in the system's predictions;
integrated ethical compliance framework 126 ensuring the AI/ML system follows ethical guidelines throughout its lifecycle by continuously evaluating adherence to ethical principles and best practices;
real-time explainability adjustment mechanism 128 dynamically modifying explanations based on user interactions and feedback, enhancing user understanding and trust during system operation;
cross-domain knowledge transfer facilitated by the cross-domain explainability transfer system 130, which adapts explanatory principles developed in one domain for application in another, promoting best practices across industries;
anomaly detection and reporting system 132 identifying unexpected behavior or outliers in the AI/ML model's performance and generating alerts for immediate investigation;
model undergoing a transparency certification using the model transparency certification process 134, which evaluates and certifies the model's transparency features for increased stakeholder trust;
users engaging with educational materials provided by the user education and training modules 136, empowering them to understand AI/ML concepts, system outputs, and the importance of transparency;
adaptive feedback collection framework 138 gathering user feedback through various channels, such as in-app prompts or surveys, prioritizing the feedback based on urgency and relevance for model improvements;
developers and stakeholders utilizing the ethical decision-making toolkit 140 to address complex ethical dilemmas during the design, deployment, and operation of AI/ML systems, ensuring the system remains aligned with ethical guidelines and societal values

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