Navigating AI Governance in Aerospace Industry

Akshat Gupta

Apr 3, 2024

As we usher into a new era of regulations, i.e., regulations on Artificial Intelligence, it becomes imperative to understand the governance landscape in each industry meticulously. AI is not a new technology and there are a lot of industries which have been using AI/ML, in some form, for more than a decade.  Aerospace industry is one of them. Moreover, due to a very low risk-appetite, Aerospace technology has witnessed and compiled with strict regulatory measures drawn over time, as the situation demanded. This article aims at understanding the regulatory landscape in the Aerospace industry, in terms of various governing bodies and acts, requirements , and solutions to those requirements. We aim to talk more about AI Compliance with regulations in this article, which caters to overall AI governance by itself.

What has been done so far ?

When it comes to understanding the regulations drafted or implemented so far, EASA ( European Union Aviation Safety Agency ) and FAA ( Federal Aviation Administration ) are the major governing bodies in the EU and the USA respectively. 

While the FAA is still in process to publish any concrete guidelines or restrictions on AI, EASA is way ahead with an extensive AI/ML regulation framework in place. 

EASA, under its AI Roadmap, released version 2 of a concept paper establishing the guiding principles for Level 1 and 2 Machine Learning applications. This paper provides a first set of usable objectives in Aviation AI compliance and also serves as a base for formal regulatory developments to come into force. The paper aims to develop principles and guidance which can be integrated into rules and acceptable means of compliance (AMC) later on.

Let’s take a look at the framework. 

The guidelines by EASA are so far the most extensive and well-defined set of steps published in any industry for ensuring a safe & ethical AI and regulatory compliance of that AI.

Source: EASA

The figure shows the building blocks for establishing trustworthy AI systems.

  1. AI Trustworthiness Analysis

  • The AI trustworthiness analysis serves as a gateway to three other technical building blocks by aligning with EU Ethical Guidelines.

  • It involves characterising the AI application, conducting an ethics-based assessment, and performing safety and security assessments.

  • These assessments are crucial for the development and approval of AI/ML systems, following existing mandatory practices in industries like aviation.

  • While safety and security assessments maintain their principles developed originally for software, they require additional guidance to accommodate AI techniques.

  1. AI Assurance

The AI assurance building block is intended to address the AI-specific guidance pertaining to the AIbased system. It encompasses three major topics - 
Learning Assurance - It covers the paradigm shift from programming to learning, as the existing development assurance methods are not adapted to cover learning processes specific to AI/ML.

Source: EASA

W-Shaped process for Learning Assurance

Development & post-ops explainability -  This deals with the capability to provide users with understandable, reliable and relevant information with the appropriate level of detail on how an AI/ML application produces its results. 

Data recording capabilities - This addresses two specific operational and post operational purposes: on the one hand the continuous monitoring of the safety of the AI-based system and on the other hand the support to incident or accident investigation.

  1. Human Factors for AI

This block introduces the necessary guidance to account for the specific human factors needs linked with the introduction of AI. 

Among other aspects, AI operational explainability deals with the capability to provide the end users with understandable, reliable and relevant information with the appropriate level of detail and with appropriate timing on how an AI/ML application produces its results. This block also introduces the concept of human-AI teaming to ensure adequate cooperation or collaboration between end users and AI-based systems to achieve certain goals.

  1. AI Safety and Risk Mitigation

This building block considers that we may not always be able to open the ‘AI

black box’ to satisfy the whole set of objectives defined for the AI assurance and the human factors for AI building blocks, and that the associated residual risk may need to be addressed to deal with the inherent uncertainty of AI.

How does RagaAI help ? 

As we understand, under the purview of these guidelines, enterprises working on developing AI systems for applications in different aspects of aviation must be able to manage the associated risks well. While the enforcement and adoption of these obligations comes into full effect ( already started ), RagaAI offers an invaluable approach on helping expedite this endeavour. With its comprehensive solutions, RagaAI helps businesses identify, break-down and and comply with the obligations that the regulators propose. These solutions work across all modalities of data.

RagaAI provides comprehensive tests catering to the requirements of the act (laid out objectively), using cutting-edge methods, concrete frameworks and extensive visualisation techniques. 

Source: RagaAI

Users can track overall compliance status with global standards put in place by various regulators and policies.

Source: RagaAI

The figure shows a sample of Raga AI framework for mapping various EASA objectives to RagaAI evaluation tests for achieving compliance to the objectives. 
The website docs enlist and meticulously present the various tests which have been designed to comply with different aspects of AI regulatory regimes published by Aviation regulators.

Conclusion

This article only covers the AI Compliance landscape in aviation from the lens of EASA. Moreover, it does not get into details of HOW to ensure fulfilment of these vast sets of requirements and obligations. As the aerospace industry is a high-risk sector, the technology deployed at every step needs to be safe and repairable. EASA provides the best first step possible to fundamentally understand all the aspects of AI governance in action. Being highly comprehensive, it can also be extrapolated to other industries with ease. 

We know there are tons of questions in your mind after reading this, and unfortunately we cannot cover all ground in this article. But we do give an open invitation to share our expertise with whoever wants to take this ride of AI governance in Aviation.

Get in touch with our Experts. 

Want to know more ? Get in touch with our experts!

As we usher into a new era of regulations, i.e., regulations on Artificial Intelligence, it becomes imperative to understand the governance landscape in each industry meticulously. AI is not a new technology and there are a lot of industries which have been using AI/ML, in some form, for more than a decade.  Aerospace industry is one of them. Moreover, due to a very low risk-appetite, Aerospace technology has witnessed and compiled with strict regulatory measures drawn over time, as the situation demanded. This article aims at understanding the regulatory landscape in the Aerospace industry, in terms of various governing bodies and acts, requirements , and solutions to those requirements. We aim to talk more about AI Compliance with regulations in this article, which caters to overall AI governance by itself.

What has been done so far ?

When it comes to understanding the regulations drafted or implemented so far, EASA ( European Union Aviation Safety Agency ) and FAA ( Federal Aviation Administration ) are the major governing bodies in the EU and the USA respectively. 

While the FAA is still in process to publish any concrete guidelines or restrictions on AI, EASA is way ahead with an extensive AI/ML regulation framework in place. 

EASA, under its AI Roadmap, released version 2 of a concept paper establishing the guiding principles for Level 1 and 2 Machine Learning applications. This paper provides a first set of usable objectives in Aviation AI compliance and also serves as a base for formal regulatory developments to come into force. The paper aims to develop principles and guidance which can be integrated into rules and acceptable means of compliance (AMC) later on.

Let’s take a look at the framework. 

The guidelines by EASA are so far the most extensive and well-defined set of steps published in any industry for ensuring a safe & ethical AI and regulatory compliance of that AI.

Source: EASA

The figure shows the building blocks for establishing trustworthy AI systems.

  1. AI Trustworthiness Analysis

  • The AI trustworthiness analysis serves as a gateway to three other technical building blocks by aligning with EU Ethical Guidelines.

  • It involves characterising the AI application, conducting an ethics-based assessment, and performing safety and security assessments.

  • These assessments are crucial for the development and approval of AI/ML systems, following existing mandatory practices in industries like aviation.

  • While safety and security assessments maintain their principles developed originally for software, they require additional guidance to accommodate AI techniques.

  1. AI Assurance

The AI assurance building block is intended to address the AI-specific guidance pertaining to the AIbased system. It encompasses three major topics - 
Learning Assurance - It covers the paradigm shift from programming to learning, as the existing development assurance methods are not adapted to cover learning processes specific to AI/ML.

Source: EASA

W-Shaped process for Learning Assurance

Development & post-ops explainability -  This deals with the capability to provide users with understandable, reliable and relevant information with the appropriate level of detail on how an AI/ML application produces its results. 

Data recording capabilities - This addresses two specific operational and post operational purposes: on the one hand the continuous monitoring of the safety of the AI-based system and on the other hand the support to incident or accident investigation.

  1. Human Factors for AI

This block introduces the necessary guidance to account for the specific human factors needs linked with the introduction of AI. 

Among other aspects, AI operational explainability deals with the capability to provide the end users with understandable, reliable and relevant information with the appropriate level of detail and with appropriate timing on how an AI/ML application produces its results. This block also introduces the concept of human-AI teaming to ensure adequate cooperation or collaboration between end users and AI-based systems to achieve certain goals.

  1. AI Safety and Risk Mitigation

This building block considers that we may not always be able to open the ‘AI

black box’ to satisfy the whole set of objectives defined for the AI assurance and the human factors for AI building blocks, and that the associated residual risk may need to be addressed to deal with the inherent uncertainty of AI.

How does RagaAI help ? 

As we understand, under the purview of these guidelines, enterprises working on developing AI systems for applications in different aspects of aviation must be able to manage the associated risks well. While the enforcement and adoption of these obligations comes into full effect ( already started ), RagaAI offers an invaluable approach on helping expedite this endeavour. With its comprehensive solutions, RagaAI helps businesses identify, break-down and and comply with the obligations that the regulators propose. These solutions work across all modalities of data.

RagaAI provides comprehensive tests catering to the requirements of the act (laid out objectively), using cutting-edge methods, concrete frameworks and extensive visualisation techniques. 

Source: RagaAI

Users can track overall compliance status with global standards put in place by various regulators and policies.

Source: RagaAI

The figure shows a sample of Raga AI framework for mapping various EASA objectives to RagaAI evaluation tests for achieving compliance to the objectives. 
The website docs enlist and meticulously present the various tests which have been designed to comply with different aspects of AI regulatory regimes published by Aviation regulators.

Conclusion

This article only covers the AI Compliance landscape in aviation from the lens of EASA. Moreover, it does not get into details of HOW to ensure fulfilment of these vast sets of requirements and obligations. As the aerospace industry is a high-risk sector, the technology deployed at every step needs to be safe and repairable. EASA provides the best first step possible to fundamentally understand all the aspects of AI governance in action. Being highly comprehensive, it can also be extrapolated to other industries with ease. 

We know there are tons of questions in your mind after reading this, and unfortunately we cannot cover all ground in this article. But we do give an open invitation to share our expertise with whoever wants to take this ride of AI governance in Aviation.

Get in touch with our Experts. 

Want to know more ? Get in touch with our experts!

As we usher into a new era of regulations, i.e., regulations on Artificial Intelligence, it becomes imperative to understand the governance landscape in each industry meticulously. AI is not a new technology and there are a lot of industries which have been using AI/ML, in some form, for more than a decade.  Aerospace industry is one of them. Moreover, due to a very low risk-appetite, Aerospace technology has witnessed and compiled with strict regulatory measures drawn over time, as the situation demanded. This article aims at understanding the regulatory landscape in the Aerospace industry, in terms of various governing bodies and acts, requirements , and solutions to those requirements. We aim to talk more about AI Compliance with regulations in this article, which caters to overall AI governance by itself.

What has been done so far ?

When it comes to understanding the regulations drafted or implemented so far, EASA ( European Union Aviation Safety Agency ) and FAA ( Federal Aviation Administration ) are the major governing bodies in the EU and the USA respectively. 

While the FAA is still in process to publish any concrete guidelines or restrictions on AI, EASA is way ahead with an extensive AI/ML regulation framework in place. 

EASA, under its AI Roadmap, released version 2 of a concept paper establishing the guiding principles for Level 1 and 2 Machine Learning applications. This paper provides a first set of usable objectives in Aviation AI compliance and also serves as a base for formal regulatory developments to come into force. The paper aims to develop principles and guidance which can be integrated into rules and acceptable means of compliance (AMC) later on.

Let’s take a look at the framework. 

The guidelines by EASA are so far the most extensive and well-defined set of steps published in any industry for ensuring a safe & ethical AI and regulatory compliance of that AI.

Source: EASA

The figure shows the building blocks for establishing trustworthy AI systems.

  1. AI Trustworthiness Analysis

  • The AI trustworthiness analysis serves as a gateway to three other technical building blocks by aligning with EU Ethical Guidelines.

  • It involves characterising the AI application, conducting an ethics-based assessment, and performing safety and security assessments.

  • These assessments are crucial for the development and approval of AI/ML systems, following existing mandatory practices in industries like aviation.

  • While safety and security assessments maintain their principles developed originally for software, they require additional guidance to accommodate AI techniques.

  1. AI Assurance

The AI assurance building block is intended to address the AI-specific guidance pertaining to the AIbased system. It encompasses three major topics - 
Learning Assurance - It covers the paradigm shift from programming to learning, as the existing development assurance methods are not adapted to cover learning processes specific to AI/ML.

Source: EASA

W-Shaped process for Learning Assurance

Development & post-ops explainability -  This deals with the capability to provide users with understandable, reliable and relevant information with the appropriate level of detail on how an AI/ML application produces its results. 

Data recording capabilities - This addresses two specific operational and post operational purposes: on the one hand the continuous monitoring of the safety of the AI-based system and on the other hand the support to incident or accident investigation.

  1. Human Factors for AI

This block introduces the necessary guidance to account for the specific human factors needs linked with the introduction of AI. 

Among other aspects, AI operational explainability deals with the capability to provide the end users with understandable, reliable and relevant information with the appropriate level of detail and with appropriate timing on how an AI/ML application produces its results. This block also introduces the concept of human-AI teaming to ensure adequate cooperation or collaboration between end users and AI-based systems to achieve certain goals.

  1. AI Safety and Risk Mitigation

This building block considers that we may not always be able to open the ‘AI

black box’ to satisfy the whole set of objectives defined for the AI assurance and the human factors for AI building blocks, and that the associated residual risk may need to be addressed to deal with the inherent uncertainty of AI.

How does RagaAI help ? 

As we understand, under the purview of these guidelines, enterprises working on developing AI systems for applications in different aspects of aviation must be able to manage the associated risks well. While the enforcement and adoption of these obligations comes into full effect ( already started ), RagaAI offers an invaluable approach on helping expedite this endeavour. With its comprehensive solutions, RagaAI helps businesses identify, break-down and and comply with the obligations that the regulators propose. These solutions work across all modalities of data.

RagaAI provides comprehensive tests catering to the requirements of the act (laid out objectively), using cutting-edge methods, concrete frameworks and extensive visualisation techniques. 

Source: RagaAI

Users can track overall compliance status with global standards put in place by various regulators and policies.

Source: RagaAI

The figure shows a sample of Raga AI framework for mapping various EASA objectives to RagaAI evaluation tests for achieving compliance to the objectives. 
The website docs enlist and meticulously present the various tests which have been designed to comply with different aspects of AI regulatory regimes published by Aviation regulators.

Conclusion

This article only covers the AI Compliance landscape in aviation from the lens of EASA. Moreover, it does not get into details of HOW to ensure fulfilment of these vast sets of requirements and obligations. As the aerospace industry is a high-risk sector, the technology deployed at every step needs to be safe and repairable. EASA provides the best first step possible to fundamentally understand all the aspects of AI governance in action. Being highly comprehensive, it can also be extrapolated to other industries with ease. 

We know there are tons of questions in your mind after reading this, and unfortunately we cannot cover all ground in this article. But we do give an open invitation to share our expertise with whoever wants to take this ride of AI governance in Aviation.

Get in touch with our Experts. 

Want to know more ? Get in touch with our experts!

As we usher into a new era of regulations, i.e., regulations on Artificial Intelligence, it becomes imperative to understand the governance landscape in each industry meticulously. AI is not a new technology and there are a lot of industries which have been using AI/ML, in some form, for more than a decade.  Aerospace industry is one of them. Moreover, due to a very low risk-appetite, Aerospace technology has witnessed and compiled with strict regulatory measures drawn over time, as the situation demanded. This article aims at understanding the regulatory landscape in the Aerospace industry, in terms of various governing bodies and acts, requirements , and solutions to those requirements. We aim to talk more about AI Compliance with regulations in this article, which caters to overall AI governance by itself.

What has been done so far ?

When it comes to understanding the regulations drafted or implemented so far, EASA ( European Union Aviation Safety Agency ) and FAA ( Federal Aviation Administration ) are the major governing bodies in the EU and the USA respectively. 

While the FAA is still in process to publish any concrete guidelines or restrictions on AI, EASA is way ahead with an extensive AI/ML regulation framework in place. 

EASA, under its AI Roadmap, released version 2 of a concept paper establishing the guiding principles for Level 1 and 2 Machine Learning applications. This paper provides a first set of usable objectives in Aviation AI compliance and also serves as a base for formal regulatory developments to come into force. The paper aims to develop principles and guidance which can be integrated into rules and acceptable means of compliance (AMC) later on.

Let’s take a look at the framework. 

The guidelines by EASA are so far the most extensive and well-defined set of steps published in any industry for ensuring a safe & ethical AI and regulatory compliance of that AI.

Source: EASA

The figure shows the building blocks for establishing trustworthy AI systems.

  1. AI Trustworthiness Analysis

  • The AI trustworthiness analysis serves as a gateway to three other technical building blocks by aligning with EU Ethical Guidelines.

  • It involves characterising the AI application, conducting an ethics-based assessment, and performing safety and security assessments.

  • These assessments are crucial for the development and approval of AI/ML systems, following existing mandatory practices in industries like aviation.

  • While safety and security assessments maintain their principles developed originally for software, they require additional guidance to accommodate AI techniques.

  1. AI Assurance

The AI assurance building block is intended to address the AI-specific guidance pertaining to the AIbased system. It encompasses three major topics - 
Learning Assurance - It covers the paradigm shift from programming to learning, as the existing development assurance methods are not adapted to cover learning processes specific to AI/ML.

Source: EASA

W-Shaped process for Learning Assurance

Development & post-ops explainability -  This deals with the capability to provide users with understandable, reliable and relevant information with the appropriate level of detail on how an AI/ML application produces its results. 

Data recording capabilities - This addresses two specific operational and post operational purposes: on the one hand the continuous monitoring of the safety of the AI-based system and on the other hand the support to incident or accident investigation.

  1. Human Factors for AI

This block introduces the necessary guidance to account for the specific human factors needs linked with the introduction of AI. 

Among other aspects, AI operational explainability deals with the capability to provide the end users with understandable, reliable and relevant information with the appropriate level of detail and with appropriate timing on how an AI/ML application produces its results. This block also introduces the concept of human-AI teaming to ensure adequate cooperation or collaboration between end users and AI-based systems to achieve certain goals.

  1. AI Safety and Risk Mitigation

This building block considers that we may not always be able to open the ‘AI

black box’ to satisfy the whole set of objectives defined for the AI assurance and the human factors for AI building blocks, and that the associated residual risk may need to be addressed to deal with the inherent uncertainty of AI.

How does RagaAI help ? 

As we understand, under the purview of these guidelines, enterprises working on developing AI systems for applications in different aspects of aviation must be able to manage the associated risks well. While the enforcement and adoption of these obligations comes into full effect ( already started ), RagaAI offers an invaluable approach on helping expedite this endeavour. With its comprehensive solutions, RagaAI helps businesses identify, break-down and and comply with the obligations that the regulators propose. These solutions work across all modalities of data.

RagaAI provides comprehensive tests catering to the requirements of the act (laid out objectively), using cutting-edge methods, concrete frameworks and extensive visualisation techniques. 

Source: RagaAI

Users can track overall compliance status with global standards put in place by various regulators and policies.

Source: RagaAI

The figure shows a sample of Raga AI framework for mapping various EASA objectives to RagaAI evaluation tests for achieving compliance to the objectives. 
The website docs enlist and meticulously present the various tests which have been designed to comply with different aspects of AI regulatory regimes published by Aviation regulators.

Conclusion

This article only covers the AI Compliance landscape in aviation from the lens of EASA. Moreover, it does not get into details of HOW to ensure fulfilment of these vast sets of requirements and obligations. As the aerospace industry is a high-risk sector, the technology deployed at every step needs to be safe and repairable. EASA provides the best first step possible to fundamentally understand all the aspects of AI governance in action. Being highly comprehensive, it can also be extrapolated to other industries with ease. 

We know there are tons of questions in your mind after reading this, and unfortunately we cannot cover all ground in this article. But we do give an open invitation to share our expertise with whoever wants to take this ride of AI governance in Aviation.

Get in touch with our Experts. 

Want to know more ? Get in touch with our experts!

As we usher into a new era of regulations, i.e., regulations on Artificial Intelligence, it becomes imperative to understand the governance landscape in each industry meticulously. AI is not a new technology and there are a lot of industries which have been using AI/ML, in some form, for more than a decade.  Aerospace industry is one of them. Moreover, due to a very low risk-appetite, Aerospace technology has witnessed and compiled with strict regulatory measures drawn over time, as the situation demanded. This article aims at understanding the regulatory landscape in the Aerospace industry, in terms of various governing bodies and acts, requirements , and solutions to those requirements. We aim to talk more about AI Compliance with regulations in this article, which caters to overall AI governance by itself.

What has been done so far ?

When it comes to understanding the regulations drafted or implemented so far, EASA ( European Union Aviation Safety Agency ) and FAA ( Federal Aviation Administration ) are the major governing bodies in the EU and the USA respectively. 

While the FAA is still in process to publish any concrete guidelines or restrictions on AI, EASA is way ahead with an extensive AI/ML regulation framework in place. 

EASA, under its AI Roadmap, released version 2 of a concept paper establishing the guiding principles for Level 1 and 2 Machine Learning applications. This paper provides a first set of usable objectives in Aviation AI compliance and also serves as a base for formal regulatory developments to come into force. The paper aims to develop principles and guidance which can be integrated into rules and acceptable means of compliance (AMC) later on.

Let’s take a look at the framework. 

The guidelines by EASA are so far the most extensive and well-defined set of steps published in any industry for ensuring a safe & ethical AI and regulatory compliance of that AI.

Source: EASA

The figure shows the building blocks for establishing trustworthy AI systems.

  1. AI Trustworthiness Analysis

  • The AI trustworthiness analysis serves as a gateway to three other technical building blocks by aligning with EU Ethical Guidelines.

  • It involves characterising the AI application, conducting an ethics-based assessment, and performing safety and security assessments.

  • These assessments are crucial for the development and approval of AI/ML systems, following existing mandatory practices in industries like aviation.

  • While safety and security assessments maintain their principles developed originally for software, they require additional guidance to accommodate AI techniques.

  1. AI Assurance

The AI assurance building block is intended to address the AI-specific guidance pertaining to the AIbased system. It encompasses three major topics - 
Learning Assurance - It covers the paradigm shift from programming to learning, as the existing development assurance methods are not adapted to cover learning processes specific to AI/ML.

Source: EASA

W-Shaped process for Learning Assurance

Development & post-ops explainability -  This deals with the capability to provide users with understandable, reliable and relevant information with the appropriate level of detail on how an AI/ML application produces its results. 

Data recording capabilities - This addresses two specific operational and post operational purposes: on the one hand the continuous monitoring of the safety of the AI-based system and on the other hand the support to incident or accident investigation.

  1. Human Factors for AI

This block introduces the necessary guidance to account for the specific human factors needs linked with the introduction of AI. 

Among other aspects, AI operational explainability deals with the capability to provide the end users with understandable, reliable and relevant information with the appropriate level of detail and with appropriate timing on how an AI/ML application produces its results. This block also introduces the concept of human-AI teaming to ensure adequate cooperation or collaboration between end users and AI-based systems to achieve certain goals.

  1. AI Safety and Risk Mitigation

This building block considers that we may not always be able to open the ‘AI

black box’ to satisfy the whole set of objectives defined for the AI assurance and the human factors for AI building blocks, and that the associated residual risk may need to be addressed to deal with the inherent uncertainty of AI.

How does RagaAI help ? 

As we understand, under the purview of these guidelines, enterprises working on developing AI systems for applications in different aspects of aviation must be able to manage the associated risks well. While the enforcement and adoption of these obligations comes into full effect ( already started ), RagaAI offers an invaluable approach on helping expedite this endeavour. With its comprehensive solutions, RagaAI helps businesses identify, break-down and and comply with the obligations that the regulators propose. These solutions work across all modalities of data.

RagaAI provides comprehensive tests catering to the requirements of the act (laid out objectively), using cutting-edge methods, concrete frameworks and extensive visualisation techniques. 

Source: RagaAI

Users can track overall compliance status with global standards put in place by various regulators and policies.

Source: RagaAI

The figure shows a sample of Raga AI framework for mapping various EASA objectives to RagaAI evaluation tests for achieving compliance to the objectives. 
The website docs enlist and meticulously present the various tests which have been designed to comply with different aspects of AI regulatory regimes published by Aviation regulators.

Conclusion

This article only covers the AI Compliance landscape in aviation from the lens of EASA. Moreover, it does not get into details of HOW to ensure fulfilment of these vast sets of requirements and obligations. As the aerospace industry is a high-risk sector, the technology deployed at every step needs to be safe and repairable. EASA provides the best first step possible to fundamentally understand all the aspects of AI governance in action. Being highly comprehensive, it can also be extrapolated to other industries with ease. 

We know there are tons of questions in your mind after reading this, and unfortunately we cannot cover all ground in this article. But we do give an open invitation to share our expertise with whoever wants to take this ride of AI governance in Aviation.

Get in touch with our Experts. 

Want to know more ? Get in touch with our experts!

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Marketing Success With Retrieval Augmented Generation (RAG) Platforms

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Generative AI And Document Question Answering With LLMs

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Understanding the Use of Guardrail Metrics in Ensuring LLM Safety

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Comprehensive Guide to RLHF and Fine Tuning LLMs from Scratch

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Using Synthetic Data To Enrich RAG Applications

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Comparing Different Large Language Model (LLM) Frameworks

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Integrating AI Models with Continuous Integration Systems

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Understanding Retrieval Augmented Generation for Large Language Models: A Survey

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Leveraging AI For Enhanced Retail Customer Experiences

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Enhancing Enterprise Search Using RAG and LLMs

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Importance of Accuracy and Reliability in Tabular Data Models

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Information Retrieval And LLMs: RAG Explained

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Introduction to LLM Powered Autonomous Agents

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Guide on Unified Multi-Dimensional LLM Evaluation and Benchmark Metrics

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Innovations In AI For Healthcare

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Implementing AI-Driven Inventory Management For The Retail Industry

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Practical Retrieval Augmented Generation: Use Cases And Impact

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LLM Pre-Training and Fine-Tuning Differences

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20 LLM Project Ideas For Beginners Using Large Language Models

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Understanding LLM Parameters: Tuning Top-P, Temperature And Tokens

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Understanding Large Action Models In AI

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Building And Implementing Custom LLM Guardrails

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Understanding LLM Alignment: A Simple Guide

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Practical Strategies For Self-Hosting Large Language Models

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Practical Guide For Deploying LLMs In Production

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The Impact Of Generative Models On Content Creation

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Implementing Regression Tests In AI Development

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In-Depth Case Studies in AI Model Testing: Exploring Real-World Applications and Insights

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Techniques and Importance of Stress Testing AI Systems

Jigar Gupta

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Navigating Global AI Regulations and Standards

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The Cost of Errors In AI Application Development

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Best Practices In Data Governance For AI

Rehan Asif

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Success Stories And Case Studies Of AI Adoption Across Industries

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Exploring The Frontiers Of Deep Learning Applications

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Integration Of RAG Platforms With Existing Enterprise Systems

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Multimodal LLMS Using Image And Text

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Understanding ML Model Monitoring In Production

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Strategic Approach To Testing AI-Powered Applications And Systems

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Navigating GDPR Compliance for AI Applications

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The Impact of AI Governance on Innovation and Development Speed

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Best Practices For Testing Computer Vision Models

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Building Low-Code LLM Apps with Visual Programming

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Understanding AI regulations In Finance

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Compliance Automation: Getting Started with Regulatory Management

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Practical Guide to Fine-Tuning OpenAI GPT Models Using Python

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Comparing Different Large Language Models (LLM)

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Evaluating Large Language Models: Methods And Metrics

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Significant AI Errors, Mistakes, Failures, and Flaws Companies Encounter

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Challenges and Strategies for Implementing Enterprise LLM

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Enhancing Computer Vision with Synthetic Data: Advantages and Generation Techniques

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Building Trust In Artificial Intelligence Systems

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A Brief Guide To LLM Parameters: Tuning and Optimization

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Unlocking The Potential Of Computer Vision Testing: Key Techniques And Tools

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Understanding AI Regulatory Compliance And Its Importance

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Understanding The Basics Of AI Governance

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Understanding Prompt Engineering: A Guide

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Examples And Strategies To Mitigate AI Bias In Real-Life

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Understanding The Basics Of LLM Fine-tuning With Custom Data

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Overview Of Key Concepts In AI Safety And Security
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Understanding Hallucinations In LLMs

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Demystifying FDA's Approach to AI/ML in Healthcare: Your Ultimate Guide

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Navigating AI Governance in Aerospace Industry

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The White House Executive Order on Safe and Trustworthy AI

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The EU AI Act - All you need to know

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RagaAI releases the most comprehensive open-source LLM Evaluation and Guardrails package

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RagaAI LLM Hub
RagaAI LLM Hub
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RagaAI LLM Hub
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Identifying edge cases within CelebA Dataset using RagaAI testing Platform

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How to Detect and Fix AI Issues with RagaAI

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Detection of Labelling Issue in CIFAR-10 Dataset using RagaAI Platform

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RagaAI emerges from Stealth with the most Comprehensive Testing Platform for AI

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Home

Product

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Copyright © RagaAI | 2024

691 S Milpitas Blvd, Suite 217, Milpitas, CA 95035, United States

Get Started With RagaAI®

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Schedule a call with AI Testing Experts

Home

Product

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Resources

Pricing

Copyright © RagaAI | 2024

691 S Milpitas Blvd, Suite 217, Milpitas, CA 95035, United States