What is Trustworthy AI?

Trustworthy AI is artificial intelligence that prioritizes transparency and safety for everyone who interacts with it. Technology adoption increases when end users understand how the technology works and how it uses their data. Trustworthy AI aims to establish trust and transparency in every stage of the AI lifecycle, from design to deployment. Developers take steps to help customers understand how the AI technology works, its limitations, and its intended use cases. Trustworthy AI is technically robust while also remaining lawful and ethically adherent.

Trustworthy artificial intelligence provides a framework to mitigate potential risks, improve interpretability, and ensure fairness to AI users. Organizations incorporating trustworthy AI benefit in different ways.

Improved customer trust

Some customers are hesitant to adopt AI technologies because end users cannot fully understand how specific outcomes are deduced. This limits AI usage in mission-critical applications, such as healthcare, finance, and automotive.

Trustworthy AI better answers questions like how the model reaches specific decisions. It allows data scientists to gain users’ trust by creating AI models that are fair and consistent in their outputs.

Adherence to ethics

As AI technologies continue to evolve, machine learning engineers may uncover new risks that could affect organizations and key stakeholders. For example, a deep learning model might inherit bias from its algorithm or the dataset it trained on. When a biased model is deployed in real life, it can produce unfair decisions that marginalize vulnerable groups. Trustworthy AI provides a means for organizations to reduce such ethical risks and ensure that all user groups are fairly represented in actual applications.

Increase data security and privacy

Implementing AI models requires giving them massive volumes of training data. AI training datasets might contain sensitive information in use cases like medical and finance. This makes AI systems susceptible to data risks, privacy concerns, and adversarial attempts, which could negatively impact AI users.

Trustworthy AI allows engineers to build robust and resilient AI systems by taking preventive measures against unauthorized access attempts. More importantly, organizations can assure users by informing them about what data the AI system collects and how it is used by the AI solution.

Organizations mitigate risks by implementing AI securely and fairly at scale. They follow comprehensive guidelines to manage risks, improve AI-human collaboration, and comply with regulatory acts.

Transparency

Generative AI models can produce content in similar ways to humans. Often, users can’t distinguish AI-created content from human content without referring to explicit labels on the system output. Moreover, they can’t trace the decisions the model makes to contributional factors.

Trustworthy AI allows users to learn how AI systems collect, store, and process their data. It empowers users with the right to accept, reject, or restrict data gathering by AI systems. Transparency also means enabling users to be aware if and when they’re interacting with an AI system by labeling AI-created decisions as such.

Explainability

Trustworthy AI enables generative machine learning experts to confidently interpret decisions that generative AI systems make. Data scientists can use relevant tools to investigate and explain the complex reasoning hidden within the neural networks. Doing so benefits industries where trust is pivotal to AI adoption. For example, doctors need clarity on how an AI-powered imaging system produces a specific diagnosis before applying it in the real world.

Fairness

Fundamentally, AI algorithms cannot understand the concept of fairness the way that societies do. They can only discover, analyze, and correlate the relationships of complex patterns in datasets. So, if the training data or the algorithm itself is biased, the result will reflect that. Therefore, evaluating and fine-tuning AI models is important, ensuring that AI algorithms consistently make fair decisions agreeable to social norms.

Accountability

AI models are software that demonstrates human-like intelligence. However, they cannot make conscious and morally guided decisions like humans. Therefore, AI cannot be directly held accountable for outcomes that go against societal values and business ethics. However, trustworthy AI provides mechanisms to assign responsibilities and accountability to relevant stakeholders involved in training, developing, and operating the AI system.

Organizations can establish trustworthy AI policies that evaluate models for accuracy, consistency, and bias. For example, if an AI model produces an inaccurate result, stakeholders investigate and fix issues because they are aware of the chain of responsibilities.

Developing trustworthy AI applications requires embedding the key principles we mention into the entire AI lifecycle. However, generative artificial intelligence (AI) models are designed similarly to black boxes; deducing how they infer a specific outcome is challenging.

Focusing on the below practices helps organizations protect user privacy and ensure fairer outcomes when implementing AI.

Improve awareness of AI ethics.

Spread awareness of AI ethics and why they’re implemented across the organization. Both AI developers and non-technical business units should be equally informed of the importance of such measures.

Implement risk management

Evaluate and identify potential risks that AI algorithms might pose. This includes data privacy, bias, explainability, and reproducibility of the model under development. Consult industry experts to evaluate possible drawbacks when automating specific workflows with AI.

Enforce data governance

Applying protective measures to data that AI systems collect and use helps organizations mitigate data risks. Besides, being transparent about data usage and possible societal impact creates a more trustful environment.

Establish human-centered policies

The eventual goal of AI innovations is to benefit humans. Solely focusing on the technicalities of AI development is not sufficient to create a trustworthy AI system. Instead establish policies that reflect human values to improve trust and enable more efficient human-AI collaboration.

Evaluate AI models continuously

AI models are not perfect, and organizations must be aware of associated risks that may appear over time. Developers must continuously monitor for data drift, accuracy, and other issues and troubleshoot promptly.

At AWS, we are committed to developing trustworthy AI that prioritizes education, science, and our customers. We offer a range of trustworthy AI tools and resources for human-centered AI development. For example,

• Amazon SageMaker Clarify provides tools to help explain how deep learning models make predictions.
• Amazon Augmented AI lets you implement human reviews of ML predictions when human oversight is needed.
• Amazon SageMaker Ground Truth offers the most comprehensive set of human-in-the-loop capabilities for incorporating human feedback across the ML lifecycle to improve model accuracy and relevancy.

You can complete various human-in-the-loop tasks through a self-service or AWS-managed offering, from data generation and annotation to reward model generation, model review, and customization.

Get started with trustworthy AI on AWS by creating a free account today!