Safe frontier AI development

AI Safety Lab programmes bring together people, technology and techniques to build platforms for the safe development of frontier AI.  We build agile teams of engineers, product managers, designers, data scientists and senior management to innovate together and accelerate the development of safe and transparent AI. 

The programme engages some or all of the following techniques.

• Impact assessments - Used to anticipate the effect of a policy/programme on environmental, equality, human rights, data protection, or other outcomes. Impact assessments are designed to determine the impact levels of an automated decision making systems. Assessment scores are based on factors including the system design, algorithm, decision type, impact and data.

• Impact evaluations - Similar in origin and practice to impact assessments, but conducted after a program or policy has been implemented. Evaluations produce impact assessments for organisations developing, procuring or deploying AI to identify and mitigate the potential impacts of AI systems on human rights, democracy and the rule of law.

• Risk assessments - Seek to identify risks that might be disruptive to businesses carrying out the assessment (e.g reputational damage) to provide automated risk assessment and assurance for companies deploying AI systems. We use a range of tools for bias audits, and performance testing and continuous monitoring to provide assurance across verticals of bias, privacy, explainability and robustness.

• Bias audits - Assess data inputs and outputs of algorithmic systems to determine whether there is unfair bias in the outcome of a decision or classification made by the system or input data used in the system. Test, monitor, optimise and explain solutions to identify and mitigate unintended bias in machine learning algorithms and build services that are appropriate for target groups in consumer bases.

• Compliance audit - Review of a company's adherence to internal AI safety policies and procedures, or external regulations or legal requirements to verify that organisational processes and management systems are operating effectively, according to predetermined standards and ensure compliance with applicable laws, rules and regulations. e.g. Independent audit of AI Safety in the categories of Bias, Privacy, Ethics, Trust and Cybersecurity.

• Certification - A process where an independent body attests that a product, service, organisation or individual has been tested against, and met, objective standards of quality or performance. Used by organisations to demonstrate that an AI system has been designed, built, and deployed in line with the UK's Governments nine processes for Safe AI.

• Conformity assessment - Provides assurance that a product, service or system being supplied meets the expectations specified or claimed, prior to it entering the market. Conformity assessment includes activities such as testing, inspection and certification. The programme offers clear assurance guidance on how to interpret regulatory requirements and how to demonstrate conformity.

• Performance testing - Used to assess the performance of a system with respect to predetermined quantitative requirements or benchmarks. Continuous ML monitoring and automated AI explainability for an organisation to monitor, observe, and analyse the performance of their AI models. Pinpointing data drift, and alerts when models need retraining.

• Formal verification - Establishes whether a system satisfies some requirements using the formal methods of mathematics. Suite of tools that can be used to provide insights into algorithm performance and guarantees for a wide range of algorithms. Aiming to mathematically verify algorithm properties and systematically explore and explain possible algorithm behaviours.

Case Study: Guiding Trustworthy and Responsible AI Practices

Navigating the Path to Safe and Transparent AI

Introduction

In the era of Artificial Intelligence (AI), where technology drives innovation and decision-making, the need for responsible and trustworthy AI practices has never been greater. The AI Safety Lab, puts safety at the frontier of AI development by empowering organisations to implement safe and transparent AI while navigating the complex landscape of data protection and ethics regulations and good practice standards.

The Challenge

As organisations embrace AI to gain a competitive edge, they grapple with the complexities of safe AI development and responsible AI use. Balancing innovation with safety and transparency presents a multifaceted challenge.

The Solution: AI Lab

The AI Safety Lab is an innovative programme developed by Advanced Analytica, tha integrates innovation and compliance and provides comprehensive guidance to UK organisations on implementing safe AI practices. It offers strategic planning and roadmaps to navigate the intricacies of technology assured compliance for transparency.

Benefits and Applications

The AI Lab by Advanced Analytica offers numerous benefits:

• Safe AI Adoption: Guiding organisations in the development and adoption of safe AI technologies and fostering responsible AI practices.

• Transparency and Trust: Enhancing transparency in frontier AI systems, building trust with stakeholders and the public.

• Regulatory Compliance: Provides strategies to ensure compliance with evolving AI regulations and standards.

• Innovation and Growth: Empowers organisations to innovate with AI while preserving ethical values and positioning them for sustainable growth.

Use Case Example

Scenario: A prominent UK financial is aiming to integrate AI for risk assessment and decision-making. They are facing ethical concerns about potential bias and the opacity of AI-driven decisions about loans.

Solution: The financial institution engages with the AI Safety Lab programme to conduct a thorough assessment of their AI systems and identify and remediate areas of potential bias and transparency measures.

Results: With guidance from the AI Safety Lab, the financial institution successfully implemented their AI Safety Policies across nine areas of AI Safety practice. They reduced bias in their AI-driven decisions, gained public trust, and positioned themselves as ethical leaders in the financial sector.

Conclusion

Advanced Analytica's AI Safety Lab programme serves as a compass for organisations navigating the complex terrain of frontier AI development. In a world where AI is transforming industries, our programme empowers organisations to innovate safely, build trust, and adhere to evolving AI regulations. Join us in responsible AI practices, where innovation and compliance are harmonised to shape the future of safe AI.