The Core Principles of Trustworthy AI

What is Trustworthy AI?

Trustworthy AI focuses on ensuring that an AI system is safe, reliable, transparent, and fair for all parties who interact with it or are impacted by its outcomes. To make it easier to understand these requirements in practice, it is best to understand the core principles that this “trust” is derived from. There are three core principles to developing and maintaining trustworthy AI. For an AI system to be deemed trustworthy, it needs to be…

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Legally Compliant

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Legal compliance forms the foundation of trustworthiness in AI. Legal frameworks are designed to prevent harm to society by establishing clear, strict standards for safety and ethics. Violating these laws can result in severe consequences, such as hefty fines or even prison sentences. Thus, adhering to legal requirements is the first step in demonstrating a commitment to ethical and safe practices.

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Governments worldwide are catching up with the rapidly evolving AI landscape. For example, in August of 2024, the European Union introduced the EU AI Act, a landmark regulation that enforces ethical guardrails for AI development and use. In the EU context, a trustworthy AI system must comply not only with the AI Act but also with adjacent regulations, such as the GDPR and the Product Safety Directive.

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Technically Robust

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Technical robustness is the most practical aspect of trustworthiness, focused on ensuring that an AI system is both reliable and secure.

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• Reliable: AI systems must consistently deliver accurate outputs to build user confidence. This requires minimising errors and ensuring the system performs as intended, even in the event of partial failures and uncertainty. Appropriate mechanisms should be in place to maintain functionality under adverse conditions.

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• Secure: AI systems need to be well-protected against cyberattacks, data breaches, and unauthorised access. Safeguarding the algorithm from tampering is essential, and developers must regularly conduct rigorous testing to validate the system’s behaviours and safety under various stress scenarios.

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Ethically Sound

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The final component of trustworthy AI is ethical soundness, which focuses on developing AI systems that uphold societal values such as fairness, transparency, and accountability. Ethically sound AI strives to minimise bias, promote inclusivity, and respect human rights. It must also be sustainable, considering both its environmental and social impact, ensuring users are fully aware of the AI’s capabilities and limitations. Transparency and traceability play a crucial role in building trust, as they allow stakeholders to understand and assess how the AI system functions and makes decisions.

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The Trustworthy AI vs. Ethical AI vs. Safe AI vs. Responsible AI

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There are multiple terms within the AI safety discourse that seem somewhat interchangeable at a first glance. Trustworthy AI, ethical AI, safe AI, and responsible AI are interrelated concepts with varying applications and nuances. Overall, all three concepts are crucial to ensure AI serves the common good. Let us break them down here:

Ethical AI

Ethical AI is concerned with the ethical issues that may arise during the development and use of AI. This means that when we focus on ethical AI, we pay particular attention to an AI system's impact on society, asking how it reflects or violates social norms and values. For instance, ethical AI may focus on avoiding racial or gender bias within an algorithm.

Safe AI

The concept of AI safety is based on the notion that AI systems should not violate any fundamental rights, such as preventing physical and psychological harm from users. It is focused on assessing the outcomes against established fundamental rights, in order to prevent harm to natural persons, but also society and the environment.  

Responsible AI

While responsible AI rests on ethical principles, it is focused on following up on those principles with precursory actions. Hence, the responsible AI question seeks to combine ethics with the realities of business practices and the legal landscape. By ensuring AI is responsible, developers and deployers make sure that ethical principles of AI are operationalised within an AI lifecycle through regulatory compliance, risk management, data privacy, and accountability. For example, implementing responsible AI principles may involve robust AI governance structures within an organisation. In many ways, “responsible AI” has become the standard term encompass ethical and safe AI into a set of practices that promote the responsible use of AI.

The Relationship to Trustworthy AI

As laid out earlier, trustworthy AI is all about the reliability of an AI system ensured through technical robustness, legal compliance, and ethical soundness. Thus, just like responsible AI, trustworthy AI is closely concerned with practice and also aims to encompass ethical standards to ensure AI is safe to use. Therefore, the defining feature of trustworthy AI compared to ethical AI and responsible AI is that it hones in on user interaction.

Why is Trustworthy AI important?

The Risks of Untrustworthy AI

Designing and deploying AI systems without trustworthiness in mind bears considerable risks for organisations, individuals, and society at large.

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• Legal Failure: When AI systems fail to comply with laws and regulations, they expose their providers to significant legal and financial liabilities risking the existence of the AI system and the success of people attached to it. Moreover, legal failure may risk fundamental rights violations such as data privacy and creating overall harmful outcomes for society.

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• Technical Failure: Technical failure has multiple sources such as negligence with testing, governance, and ethics. Technically insecure AI systems endanger user and company data and expose the AI to misuse. Moreover, if AI systems are not technically robust, their decision-making processes remain opaque and therefore make it difficult to maintain explainability, transparency, and accountability. If those standards cannot be maintained, the AI system cannot be trusted to generate safe, reliable, and fair outputs.

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• Ethical Failure: Ethical failure can have disastrous consequences for a company’s reputation, vulnerable people, and society at large. An ethically flawed AI system can lead to biased outcomes, negatively affecting marginalised groups, it may start spreading disruptive misinformation, and endanger public safety.

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Overall, failing in any of those categories is going to erode the trust of the public quickly. In the worst case, an untrustworthy AI system can have irreparable destructive outcomes for society, democracy, and human rights. If you would like to dive deeper into the risks of AI explore our blog articles on the dangers of AI and AI misuse.

Compliance with the EU AI Act

On the first page of the AI Act, EU regulators state that its purpose is to “(…) promote the uptake of human-centric and trustworthy AI (…).” This highlights trustworthiness as a core value underpinning the entire framework of the EU AI Act. Non-compliance with the AI Act can have serious consequences. In addition to facing substantial regulatory fines, violators may also incur significant legal liabilities and suffer reputational damage. Therefore, understanding and implementing the key principles of trustworthiness is paramount for any organisation developing or operating AI systems within the EU.

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How can I ensure my AI System is Trustworthy?

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To trust AI systems, users need to have the ability to scrutinise them and comprehend the reasoning behind their decisions. They need to know that developers and providers of AI systems hold themselves accountable and adhere to high ethical standards while doing their best to secure their systems against breaches and misuse.

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The Cycle for Developing Trustworthy AI

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At oxethica, we provide you with a comprehensive summary containing five steps we deem essential for developing trustworthy AI systems.

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1. Consequences and Oversight

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Making sure that an AI system is proactively developed with all intended and unintended consequences for all stakeholders in mind demonstrates care and accountability with clear ethical goals and principles in mind. Moreover, implementing foresight strengthens the technical robustness and reliability of an AI system.

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From the get-go it is important to define an appropriate level of human oversight within the AI lifecycle to ensure full transparency and control over an AIs decision-making processes and outputs.

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Having a clear plan for human oversight and detailed analysis of consequences forms a robust foundation for user trust in the accountability of the AI provider and the safety of the system itself.

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2. Data Quality and Conformance

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Assessing the quality and representativeness of the data fed into an AI system is crucial, as it directly influences both its ethical soundness and overall performance. To minimise bias and ensure non-discrimination, the data must accurately reflect the diversity of the populations impacted by the AI's outputs.

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Additionally, closely verifying the completeness and consistency of the training data is crucial for ensuring that the AI system performs reliably and produces accurate results. Lastly, it is vital to confirm that the AI developer and provider possess all necessary permissions and consent to access the data and utilise it for its intended purpose (e.g. training an algorithm).

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3. Principles and Metrics

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This step necessitates establishing clear organisational values and principles that align with the performance metrics of the AI system. This alignment is important for maintaining accountability between developers and stakeholders, while also promoting ethical AI development.

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4. Testing and Documentation

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To ensure technical robustness, legal compliance, and transparency, the AI model has to be assessed for performance, robustness and fairness. At the same time, legal mandates and transparency principles require regular audits and detailed documentation of training and performance. Therein, it is crucial to check the AI system for breaches of any sectoral or regional compliance mandates.

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5. Monitoring and Review

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Lastly, to maintain trustworthiness, it is essential to monitor and review the performance of the system and update it frequently to prevent model drift. This ensures control and safety of the AI system and continues compliance with any new regulations.

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Ensure Trustworthiness with oxethica

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oxethica was founded with the mission to minimise the cost and effort required to ensure AI systems are trustworthy. Our AI governance platform provides the services you need to ensure your AI system is legally compliant, technically robust, and ethically sound.

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We support smooth compliance with regulation such as the EU AI Act with our tools for AI audit, AI inventory, and risk management, providing…

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• Automated audits and simple organisation of your AI inventory

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• Bias identification and impact analysis

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• Regulation tracking and audit trails

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Get started with oxethica today to ensure that your AI system is trustworthy!

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In a Nutshell

What is trustworthy AI?

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Trustworthy AI is centered on making AI systems safe, reliable, transparent, and fair for everyone affected by them. It is built on three core principles:

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Legally Compliant: AI must adhere to legal frameworks to prevent harm, with consequences for non-compliance. For example, the European Union's AI Act, along with other regulations like GDPR, enforce ethical standards for AI.

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Technically Robust: AI should be reliable, delivering accurate and consistent results, even in adverse conditions. It must also be secure, protecting against cyberattacks and unauthorized access through rigorous testing.

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Ethically Sound: AI must align with societal values like fairness, transparency, and accountability. This includes minimising bias, ensuring inclusivity, and considering environmental and social impacts.

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Why is trustworthy AI important?

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Trustworthy AI is essential because untrustworthy systems can lead to serious legal, technical, and ethical risks. Non-compliance with laws can result in legal liabilities and harm to society, while technical failures make AI unreliable and opaque, undermining trust. Ethical issues, like biased outcomes or misinformation, can damage an organisation's reputation or at worst cause harm to vulnerable groups and destabilise democracy. Ultimately, these risks can have destructive consequences for society and human rights.

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How can I ensure my AI system is trustworthy?

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To ensure an AI system is trustworthy - legally compliant, technically robust, and ethically sound - it is crucial to consider the consequences of its actions and incorporate human oversight throughout its lifecycle for transparency and control. Regular testing, documentation, and audits are necessary to maintain legal compliance and transparency. Additionally, ongoing monitoring and updates are vital to address issues and ensure the system remains compliant with evolving regulations, preserving its trustworthiness over time.

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But, you do not have to tackle this on your own: At oxethica, we support the trustworthiness of your AI system with our AI governance platform to make compliance with regulations straightforward. Get started with us today!

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