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SYSTEM FOR ASSESSING TRUSTWORTHINESS IN AI MODEL INTERACTIONS

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SYSTEM FOR ASSESSING TRUSTWORTHINESS IN AI MODEL INTERACTIONS

ORDINARY APPLICATION

Published

date

Filed on 3 November 2024

Abstract

ABSTRACT SYSTEM FOR ASSESSING TRUSTWORTHINESS IN AI MODEL INTERACTIONS The present disclosure introduces a system for assessing trustworthiness in AI model interactions 100. The system comprises of trustworthiness assessment framework module 102 to evaluate AI across transparency, fairness, accountability, and security, and dynamic feedback unit 104 for real-time adaptive adjustments based on user interactions. Bias detection and mitigation unit 106 addresses fairness by correcting biases, while explainability and visualization module 108 provide user-friendly insights into AI decision-making. Privacy-preserving techniques unit 112 secures user data, and user-centric interaction design module 114 fosters intuitive user engagement. Customizable evaluation metrics module 116 enables tailored trust assessments, and automated reporting and compliance monitoring module 118 generates compliance reports. Additional components include cross-model trust assessment module 120, multi-layered trust scoring system module 122, context-aware trust assessment unit 124, and AI model auditing tools module 144 to ensure consistent, ethical, and reliable AI interactions across applications. Reference Fig 1

Patent Information

Application ID202441083910
Invention FieldCOMPUTER SCIENCE
Date of Application03/11/2024
Publication Number45/2024

Inventors

NameAddressCountryNationality
Amrutha BiradarAnurag University,Venkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, IndiaIndiaIndia

Applicants

NameAddressCountryNationality
Anurag UniversityVenkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, IndiaIndiaIndia

Specification

Description:SYSTEM FOR ASSESSING TRUSTWORTHINESS IN AI MODEL INTERACTIONS
TECHNICAL FIELD
[0001] The present innovation relates to systems and methods for evaluating and enhancing the trustworthiness of AI model interactions with users and other AI systems.

BACKGROUND

[0002] The rapid integration of artificial intelligence (AI) systems across industries has transformed sectors like healthcare, finance, and education. However, as AI systems increasingly impact decision-making, concerns about their trustworthiness have emerged. Users often struggle to understand AI models, especially complex ones like deep learning networks, which operate as "black boxes," providing minimal insight into their decision processes. This opacity can lead to skepticism and distrust, particularly in high-stakes applications. Current approaches to improve AI trustworthiness, such as explainable AI (XAI) and fairness assessments, offer some transparency but are often limited in scope and lack a cohesive framework that addresses the full spectrum of trustworthiness factors, including accountability, fairness, and security. Many solutions fail to provide real-time updates, cross-system evaluations, and adaptive feedback, resulting in limited usability and effectiveness.

[0003] This invention overcomes these limitations by introducing an integrated trustworthiness framework that assesses AI interactions across key dimensions: transparency, fairness, accountability, security, and user engagement. The invention's distinguishing feature is its comprehensive approach, utilizing advanced algorithms, dynamic feedback mechanisms, and customizable evaluation metrics to ensure robust, adaptive trust assessments. Unlike existing solutions, this system also includes multi-layered trust scoring, cross-model assessment capabilities, and privacy-preserving techniques to protect user data. A real-time feedback loop continuously refines the AI model based on user interactions and emerging trust standards, ensuring ongoing adaptability and relevance.


[0004] The novelty of the invention lies in its unified framework, which aggregates diverse trustworthiness metrics into a single, user-friendly system. By enabling customizable metrics and automated reporting, it offers users and stakeholders a clear, reliable view of AI trustworthiness, empowering them to make informed decisions and build greater confidence in AI-driven outcomes.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance the trustworthiness of AI model interactions by providing a comprehensive evaluation framework for assessing transparency, fairness, accountability, security, and user engagement.

[0006] Another object of the invention is to empower users and stakeholders with clear insights into AI model decision-making processes through user-friendly explainability and visualization tools.

[0007] Another object of the invention is to improve the fairness of AI systems by incorporating bias detection and mitigation algorithms, promoting equitable outcomes across diverse demographic groups.

[0008] Another object of the invention is to establish clear accountability protocols that define the roles and responsibilities of stakeholders involved in AI deployment and operation.

[0009] Another object of the invention is to safeguard user data and ensure privacy through secure data handling, encryption, and privacy-preserving techniques during AI interactions.

[00010] Another object of the invention is to foster intuitive engagement with AI models by providing interactive interfaces that offer real-time feedback and contextual prompts.

[00011] Another object of the invention is to enhance adaptability and relevance by incorporating a dynamic feedback loop that continuously updates AI systems based on evolving user interactions and trust standards.

[00012] Another object of the invention is to offer customizable evaluation metrics, allowing users to tailor trustworthiness assessments to their specific requirements and application domains.

[00013] Another object of the invention is to facilitate compliance with regulatory standards through automated reporting and compliance monitoring tools that highlight trustworthiness and areas for improvement.

[00014] Another object of the invention is to build community trust in AI systems by enabling collaborative trust evaluation mechanisms that incorporate feedback from diverse users and stakeholders

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, system for assessing trustworthiness in AI model interactions is presented. This system provides a comprehensive framework for assessing the trustworthiness of AI model interactions, addressing essential factors like transparency, fairness, accountability, security, and user engagement. It includes advanced algorithms for bias detection, secure data handling, and dynamic feedback mechanisms for real-time adaptability. The framework offers customizable metrics and reporting tools for regulatory compliance, enabling users to confidently integrate AI systems. By combining multi-dimensional evaluation and interactive design, the invention empowers stakeholders to build reliable and ethical AI applications across various domains.

[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 system for assessing trustworthiness in AI model interactions.

[00021] FIG 2 is working methodology of system for assessing trustworthiness in AI model interactions.

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 system for assessing trustworthiness in AI model interactions 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, system for assessing trustworthiness in AI model interactions 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of trustworthiness assessment framework module 102, dynamic feedback unit 104, bias detection and mitigation unit 106, explainability and visualization module 108, accountability assignment protocol module 110, privacy-preserving techniques unit 112, user-centric interaction design module 114, customizable evaluation metrics module 116, automated reporting and compliance monitoring module 118, cross-model trust assessment module 120, multi-layered trust scoring system module 122, context-aware trust assessment unit 124, collaborative trust evaluation mechanism module 126, historical trust data repository module 128, automated compliance alerts unit 130, ethical AI guidelines integration module 132, simulated trust scenarios module 134, stakeholder feedback integration unit 136, AI trustworthiness certification system module 138, user personalization settings module 140, multi-modal interaction support unit 142, AI model auditing tools module 144

[00029] Referring to Fig. 1, the present disclosure provides details of a system for assessing trustworthiness in AI model interactions 100. It is a comprehensive framework designed to evaluate and enhance trust in AI systems through transparency, fairness, accountability, security, and user engagement. The system includes core components such as trustworthiness assessment framework module 102, dynamic feedback unit 104, and bias detection and mitigation unit 106 to support continuous trust evaluation and improvement. In one embodiment, explainability and visualization module 108 and accountability assignment protocol module 110 provide users with transparency and accountability insights. Privacy-preserving techniques unit 112 and user-centric interaction design module 114 safeguard data while promoting intuitive AI engagement. Additional components, including automated reporting and compliance monitoring module 118 and multi-layered trust scoring system module 122, enable regulatory compliance and comprehensive trust assessments, fostering reliable AI interactions across applications.


[00030] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with trustworthiness assessment framework module 102, which is the core module that evaluates AI interactions across dimensions of transparency, fairness, accountability, security, and user engagement. The module 102 aggregates data from other components such as dynamic feedback unit 104 and bias detection and mitigation unit 106 to produce comprehensive trust assessments. By working with explainability and visualization module 108, it provides a cohesive evaluation, ensuring that AI decisions align with ethical standards and user expectations.

[00031] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with dynamic feedback unit 104, which enables real-time data collection and feedback, allowing continuous updates to AI models based on user interactions and trust standards. This unit 104 interacts with trustworthiness assessment framework module 102 to adaptively refine the AI's performance and reliability. It also collaborates with customizable evaluation metrics module 116 to adjust the AI's trust assessments based on specific user needs, ensuring adaptability and accuracy.

[00032] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with bias detection and mitigation unit 106, designed to identify and mitigate biases in AI interactions, promoting fairness across demographic groups. This unit 106 uses statistical and machine learning techniques, working closely with the customizable evaluation metrics module 116 to address any detected biases dynamically. It also collaborates with the automated reporting and compliance monitoring module 118 to document and communicate fairness standards, enhancing the overall ethical reliability of the system.

[00033] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with explainability and visualization module 108, which provides visual insights and explanations of AI model decision-making processes, improving transparency for users. The module 108 translates complex outputs from the AI into understandable formats, working alongside user-centric interaction design module 114 to foster user trust. It directly interfaces with the trustworthiness assessment framework module 102 to ensure transparent communication of AI's decision logic.


[00034] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with accountability assignment protocol module 110, which clarifies and assigns accountability among stakeholders in the AI lifecycle, such as developers, deployers, and end-users. This module 110 works in tandem with automated compliance alerts unit 130 to notify stakeholders when accountability thresholds are not met. It also collaborates with privacy-preserving techniques unit 112 to ensure responsible data usage, upholding both ethical and regulatory standards in AI deployment.

[00035] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with privacy-preserving techniques unit 112, which ensures data security and user privacy through methods like encryption, anonymization, and secure data handling. The unit 112 works closely with trustworthiness assessment framework module 102 to safeguard user information throughout AI interactions. It also collaborates with user-centric interaction design module 114 to provide users with control over their data, reinforcing trust by upholding privacy standards during engagement with AI systems.


[00036] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with user-centric interaction design module 114, which prioritizes user experience by offering real-time feedback, contextual prompts, and user-friendly interfaces. This module 114 works alongside explainability and visualization module 108 to create a transparent, accessible interface for users. It further integrates with dynamic feedback unit 104 to gather user inputs and refine the interaction experience, fostering deeper engagement and confidence in AI-driven decisions.

[00037] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with customizable evaluation metrics module 116, which enables tailored trust assessments based on application-specific needs and user-defined criteria. This module 116 interacts with bias detection and mitigation unit 106 to apply relevant fairness standards and works with automated reporting and compliance monitoring module 118 to generate customized, regulation-compliant trust reports. The flexibility of 116 allows users to focus on trustworthiness factors most relevant to their domain.

[00038] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with automated reporting and compliance monitoring module 118, which automates the generation of trustworthiness reports and ensures adherence to regulatory standards. This module 118 interacts with cross-model trust assessment module 120 to consolidate trust data from various AI models and communicates directly with stakeholders through compliance alerts. By working with accountability assignment protocol module 110, it ensures transparent governance and consistent compliance monitoring.

[00039] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with cross-model trust assessment module 120, which assesses trustworthiness across interactions between multiple AI models, ensuring consistent reliability and interoperability. This module 120 collaborates with trustworthiness assessment framework module 102 to provide holistic assessments and integrates with multi-layered trust scoring system module 122 to compare trust scores across different models, ensuring robust trust evaluations in complex, multi-AI environments.

[00040] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with multi-layered trust scoring system module 122, which evaluates trustworthiness across multiple layers, such as performance, user satisfaction, and ethical compliance, to create an aggregate trust score. This module 122 integrates with context-aware trust assessment unit 124 to apply scores relevant to specific applications and works alongside automated compliance alerts unit 130 to monitor trustworthiness against threshold levels, supporting transparent decision-making.

[00041] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with context-aware trust assessment unit 124, which evaluates trustworthiness based on contextual factors like user demographics and application domains. The unit 124 works closely with customizable evaluation metrics module 116 to adjust assessment parameters to relevant contexts. Additionally, it collaborates with historical trust data repository module 128 to account for historical trends in trustworthiness, providing a tailored, context-sensitive evaluation.

[00042] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with collaborative trust evaluation mechanism module 126, which enables trust assessments from multiple users and stakeholders, fostering a community-based approach to evaluating AI interactions. This module 126 interacts with stakeholder feedback integration unit 136 to gather collective input and collaborates with AI trustworthiness certification system module 138 to inform certification processes based on shared assessments, enhancing the trustworthiness framework.

[00043] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with historical trust data repository module 128, which stores longitudinal data on trust assessments for trend analysis and improvement tracking over time. This module 128 interacts with dynamic feedback unit 104 to incorporate historical insights into real-time adjustments and works alongside automated compliance alerts unit 130 to notify stakeholders of any deviations from trust trends, ensuring informed decision-making based on past data.

[00044] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with automated compliance alerts unit 130, which proactively alerts stakeholders if trust levels fall below defined thresholds or compliance issues arise. This unit 130 collaborates with accountability assignment protocol module 110 to direct responsibility for resolution and works in conjunction with trustworthiness assessment framework module 102 to ensure continuous compliance with trust standards, supporting timely corrective actions.

[00045] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with ethical AI guidelines integration module 132, which aligns trust assessments with established ethical AI standards, promoting adherence to recognized best practices. This module 132 interfaces with bias detection and mitigation unit 106 to uphold fairness and collaborates with AI model auditing tools module 144 to maintain ethical compliance in model operations, reinforcing the system's commitment to responsible AI.

[00046] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with simulated trust scenarios module 134, which allows AI models to be tested under varying simulated conditions, identifying potential trust issues before deployment. This module 134 integrates with trustworthiness assessment framework module 102 to evaluate model responses to simulated scenarios and collaborates with AI model auditing tools module 144 to refine model operations, enhancing pre-deployment reliability.

[00047] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with stakeholder feedback integration unit 136, which collects and incorporates feedback from users, experts, and other stakeholders, enriching the trust assessment process. This unit 136 interacts with collaborative trust evaluation mechanism module 126 to ensure diverse input in trust evaluations and works with dynamic feedback unit 104 to continuously adapt the AI model based on stakeholder perspectives.

[00048] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with AI trustworthiness certification system module 138, which formally certifies AI models based on trustworthiness assessments, enhancing credibility and marketability. This module 138 collaborates with collaborative trust evaluation mechanism module 126 to incorporate shared assessments in certification and works with multi-layered trust scoring system module 122 to provide an aggregate trustworthiness score, offering a reliable credential for AI systems.

[00049] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with user personalization settings module 140, which allows users to customize trust assessment preferences based on individual needs and priorities. This module 140 interfaces with customizable evaluation metrics module 116 to adapt trust assessments accordingly and collaborates with user-centric interaction design module 114 to ensure personalized user engagement, enhancing user trust through tailored evaluations.

[00050] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with multi-modal interaction support unit 142, which enables users to engage with AI systems through their preferred modes, such as voice, text, or visual inputs. This unit 142 integrates with user-centric interaction design module 114 to provide flexible engagement options and works with privacy-preserving techniques unit 112 to ensure secure data handling across interaction modes, promoting inclusivity and accessibility.

[00051] Referring to Fig. 1, system for assessing trustworthiness in AI model interactions 100 is provided with AI model auditing tools module 144, which facilitates periodic reviews of AI models to ensure ongoing compliance with trustworthiness standards. This module 144 works with ethical AI guidelines integration module 132 to uphold ethical standards in operations and collaborates with automated compliance alerts unit 130 to promptly identify and address any compliance issues, supporting long-term integrity in AI interactions.

[00052] Referring to Fig 2, there is illustrated method 200 for system for assessing trustworthiness in AI model interactions 100. The method comprises:

At step 202, method 200 includes initiating trust assessment through the trustworthiness assessment framework module 102, which gathers data on transparency, fairness, accountability, security, and user engagement from various sources within the system;

At step 204, method 200 includes dynamic feedback unit 104 collecting real-time data from user interactions and sending this feedback to the trustworthiness assessment framework module 102 for adaptive model improvements;

At step 206, method 200 includes bias detection and mitigation unit 106 analyzing the data collected for any biases and collaborating with customizable evaluation metrics module 116 to ensure fairness standards are maintained;

At step 208, method 200 includes explainability and visualization module 108 generating user-friendly visualizations of the AI's decision-making process, which are displayed to the user through user-centric interaction design module 114;

At step 210, method 200 includes accountability assignment protocol module 110 designating responsibility among stakeholders based on interactions and decisions made by the AI, enhancing transparency and governance in the system;

At step 212, method 200 includes privacy-preserving techniques unit 112 encrypting and anonymizing user data collected during interactions to protect user privacy while interacting with the AI;

At step 214, method 200 includes customizable evaluation metrics module 116 adjusting assessment parameters based on the specific needs of the application domain, ensuring trust assessments are tailored to context-specific factors;

At step 216, method 200 includes automated reporting and compliance monitoring module 118 generating detailed trustworthiness reports based on the collected data and sharing these reports with stakeholders for review and compliance verification;

At step 218, method 200 includes cross-model trust assessment module 120 assessing trustworthiness across multiple AI models to ensure consistency and reliability in interactions between different AI systems;

At step 220, method 200 includes multi-layered trust scoring system module 122 evaluating trustworthiness across multiple layers and providing an aggregate trust score for quick reference;

At step 222, method 200 includes context-aware trust assessment unit 124 adjusting the trust assessment based on application-specific factors, such as user demographics or domain, for a more accurate evaluation;

At step 224, method 200 includes collaborative trust evaluation mechanism module 126 gathering input from multiple users and stakeholders, integrating this feedback to enhance the trustworthiness assessment;

At step 226, method 200 includes historical trust data repository module 128 storing longitudinal data on trust assessments, which is used to track trends and support future assessments;

At step 228, method 200 includes automated compliance alerts unit 130 notifying stakeholders if the trustworthiness of an AI system falls below defined thresholds, prompting timely corrective actions;

At step 230, method 200 includes ethical AI guidelines integration module 132 ensuring that all assessments are aligned with recognized ethical AI standards, promoting responsible AI use;

At step 232, method 200 includes simulated trust scenarios module 134 running AI models through simulated conditions to test reliability and identify potential trust issues before deployment;

At step 234, method 200 includes stakeholder feedback integration unit 136 incorporating feedback from diverse stakeholders, enriching the assessment with multiple perspectives;

At step 236, method 200 includes AI trustworthiness certification system module 138 formally certifying AI models based on trustworthiness assessments, enhancing market credibility and stakeholder confidence;

At step 238, method 200 includes user personalization settings module 140 enabling users to adjust trust assessment preferences, allowing for a customized approach to trustworthiness evaluation;

At step 240, method 200 includes multi-modal interaction support unit 142 providing users with options to interact with the AI through their preferred modes, such as voice or text, to enhance accessibility and engagement;

At step 242, method 200 includes AI model auditing tools module 144 performing regular audits to ensure ongoing compliance with trust standards, supporting long-term trust and accountability.


[00053] 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.

[00054] 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.

[00055] 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 system for assessing trustworthiness in AI model interactions 100 comprising of
trustworthiness assessment framework module 102 to evaluate AI interactions across transparency, fairness, accountability, security, and user engagement;
dynamic feedback unit 104 to collect real-time data from user interactions for adaptive improvements;
bias detection and mitigation unit 106 to identify and mitigate biases, ensuring fairness in AI interactions;
explainability and visualization module 108 to provide visual insights into AI decision-making;
accountability assignment protocol module 110 to designate responsibility among stakeholders for AI-driven decisions; privacy-preserving techniques unit 112 to secure and anonymize user data during AI interactions;
user-centric interaction design module 114 to enhance user experience with interactive, intuitive interfaces; customizable evaluation metrics module 116 to allow tailored trust assessments based on domain-specific requirements;
automated reporting and compliance monitoring module 118 to generate and share detailed compliance reports;
cross-model trust assessment module 120 to evaluate interactions between multiple AI models consistently;
multi-layered trust scoring system module 122 to provide a comprehensive trust score based on various evaluation layers;
context-aware trust assessment unit 124 to adjust trust assessments according to application-specific factors; collaborative trust evaluation mechanism module 126 to incorporate feedback from multiple users and stakeholders; historical trust data repository module 128 to store and track longitudinal trust assessment data;
automated compliance alerts unit 130 to notify stakeholders of any trust or compliance issues;
ethical AI guidelines integration module 132 to align trust assessments with recognized ethical AI standards;
simulated trust scenarios module 134 to test AI models in varied conditions for reliability;
stakeholder feedback integration unit 136 to gather and apply feedback from diverse stakeholders;
AI trustworthiness certification system module 138 to certify AI models based on trust assessments;
user personalization settings module 140 to allow users to adjust trust preferences to individual needs;
multi-modal interaction support unit 142 to enable various interaction modes like voice and text; and
AI model auditing tools module 144 to perform regular audits ensuring ongoing compliance with trust standards.

2. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein trustworthiness assessment framework module 102 is configured to evaluate AI interactions across dimensions of transparency, fairness, accountability, security, and user engagement, integrating data from various components to produce comprehensive trust assessments, ensuring the AI's alignment with ethical standards.
3. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein dynamic feedback unit 104 is configured to collect real-time user interaction data and communicate this feedback to trustworthiness assessment framework module 102 for adaptive adjustments, enabling continuous refinement of AI performance and reliability based on user experience.
4. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein bias detection and mitigation unit 106 is configured to identify, quantify, and mitigate biases in AI interactions using statistical and machine learning techniques, ensuring equitable treatment across demographic groups, in coordination with customizable evaluation metrics module 116.
5. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein explainability and visualization module 108 is configured to provide visual explanations of the AI's decision-making process, translating complex model outputs into user-friendly formats to enhance transparency and foster user trust, and is displayed through user-centric interaction design module 114.
6. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein privacy-preserving techniques unit 112 is configured to protect user data through encryption, anonymization, and secure handling practices, ensuring privacy during AI interactions while supporting compliance with data protection standards.
7. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein customizable evaluation metrics module 116 is configured to provide adaptable metrics and criteria for assessing trustworthiness based on domain-specific requirements, allowing stakeholders to personalize evaluation frameworks to meet diverse application needs.
8. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein automated reporting and compliance monitoring module 118 is configured to generate detailed trustworthiness reports for regulatory compliance, sharing insights with stakeholders to ensure AI systems meet industry standards and maintain transparency.
9. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein multi-layered trust scoring system module 122 is configured to aggregate trustworthiness scores across various evaluation layers, including performance, user satisfaction, and ethical compliance, providing stakeholders with an overall trust score to facilitate informed decision-making.
10. The system for assessing trustworthiness in AI model interactions 100 as claimed in claim 1, wherein method comprises of
trustworthiness assessment framework module 102 initiating trust assessment by gathering data on transparency, fairness, accountability, security, and user engagement from various sources within the system;
dynamic feedback unit 104 collecting real-time data from user interactions and sending this feedback to the trustworthiness assessment framework module 102 for adaptive model improvements;
bias detection and mitigation unit 106 analyzing the data collected for any biases and collaborating with customizable evaluation metrics module 116 to ensure fairness standards are maintained;
explainability and visualization module 108 generating user-friendly visualizations of the AI's decision-making process, which are displayed to the user through user-centric interaction design module 114;
accountability assignment protocol module 110 designating responsibility among stakeholders based on interactions and decisions made by the AI, enhancing transparency and governance in the system;
privacy-preserving techniques unit 112 encrypting and anonymizing user data collected during interactions to protect user privacy while interacting with the AI;
customizable evaluation metrics module 116 adjusting assessment parameters based on the specific needs of the application domain;
automated reporting and compliance monitoring module 118 generating detailed trustworthiness reports based on the collected data and sharing these reports with stakeholders for review and compliance verification;
cross-model trust assessment module 120 assessing trustworthiness across multiple AI models to ensure consistency and reliability in interactions between different AI systems;
multi-layered trust scoring system module 122 evaluating trustworthiness across multiple layers and providing an aggregate trust score for quick reference;
context-aware trust assessment unit 124 adjusting the trust assessment based on application-specific factors, such as user demographics or domain, for a more accurate evaluation;
collaborative trust evaluation mechanism module 126 gathering input from multiple users and stakeholders, integrating this feedback to enhance the trustworthiness assessment;
historical trust data repository module 128 storing longitudinal data on trust assessments, which is used to track trends and support future assessments;
automated compliance alerts unit 130 notifying stakeholders if the trustworthiness of an AI system falls below defined thresholds, prompting timely corrective actions;
ethical AI guidelines integration module 132 ensuring that all assessments are aligned with recognized ethical AI standards, promoting responsible AI use;
simulated trust scenarios module 134 running AI models through simulated conditions to test reliability and identify potential trust issues before deployment;
stakeholder feedback integration unit 136 incorporating feedback from diverse stakeholders, enriching the assessment with multiple perspectives;
AI trustworthiness certification system module 138 formally certifying AI models based on trustworthiness assessments, enhancing market credibility and stakeholder confidence;
user personalization settings module 140 enabling users to adjust trust assessment preferences, allowing for a customized approach to trustworthiness evaluation;
multi-modal interaction support unit 142 providing users with options to interact with the AI through their preferred modes, such as voice or text, to enhance accessibility and engagement;
AI model auditing tools module 144 performing regular audits to ensure ongoing compliance with trust standards, supporting long-term trust and accountability.

Documents

NameDate
202441083910-COMPLETE SPECIFICATION [03-11-2024(online)].pdf03/11/2024
202441083910-DECLARATION OF INVENTORSHIP (FORM 5) [03-11-2024(online)].pdf03/11/2024
202441083910-DRAWINGS [03-11-2024(online)].pdf03/11/2024
202441083910-EDUCATIONAL INSTITUTION(S) [03-11-2024(online)].pdf03/11/2024
202441083910-EVIDENCE FOR REGISTRATION UNDER SSI [03-11-2024(online)].pdf03/11/2024
202441083910-EVIDENCE FOR REGISTRATION UNDER SSI(FORM-28) [03-11-2024(online)].pdf03/11/2024
202441083910-FIGURE OF ABSTRACT [03-11-2024(online)].pdf03/11/2024
202441083910-FORM 1 [03-11-2024(online)].pdf03/11/2024
202441083910-FORM FOR SMALL ENTITY(FORM-28) [03-11-2024(online)].pdf03/11/2024
202441083910-FORM-9 [03-11-2024(online)].pdf03/11/2024
202441083910-POWER OF AUTHORITY [03-11-2024(online)].pdf03/11/2024
202441083910-REQUEST FOR EARLY PUBLICATION(FORM-9) [03-11-2024(online)].pdf03/11/2024

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