Building trust in AI systems

From virtual assistants that manage our schedules to recommendation algorithms that influence what we watch and buy, AI is woven into the fabric of our lives. However, as AI systems become more integrated into critical areas like healthcare and finance, building trust in AI is the foundation of successful AI-product integration.

When we innovate the user experiences that shape how people engage with these systems, trustworthy AI means creating AI that communicates transparently, behaves predictably, and empowers users with control and clarity.

Recent regulations like the EU AI Act underscore the growing need for transparency and accountability, particularly in high-risk applications such as credit assessment and risk management. As oversight tightens, organizations must rethink their approach, embedding trustworthiness into AI from the very beginning and through strategic design.

In this article, we'll explore practical strategies that help close the trust gap and transform AI from a mysterious black box into a reliable, approachable tool that users can depend on with confidence.

How to make AI more dependable

AI is rapidly transforming industries like healthcare, finance, and legal services, where accuracy, transparency, and security are non-negotiable. In these high-stakes fields, trust in AI comes from confidence in the system’s reliability, fairness, and accountability. Without trust, AI adoption slows, regulatory risks increase, and users hesitate to rely on automated insights.

For users, trust in AI comes down to transparency and predictability. In healthcare, for example, clinicians must understand why an AI system recommends a specific diagnosis or treatment. If the reasoning is unclear or inconsistent, adoption drops. A 2024 MIT Sloan study found that clinicians were 41% more likely to follow AI recommendations when the system provided clear, case-specific explanations.

The same principle applies in finance, where algorithmic trading systems must offer traceable logic, and in legal services, where AI-driven contract analysis must show its reasoning to be trusted by attorneys.

For enterprises, trust is about compliance, security, and business continuity. For example, in healthcare, HIPAA compliance and patient data security are essential in the US. Hospitals and insurers face legal liabilities if AI fails to meet these standards. Similarly, in legal tech, trust hinges on data confidentiality; law firms using AI for document review or litigation analysis must ensure that AI tools do not expose sensitive client information.

AI must be explainable, auditable, and aligned with industry standards to bridge the trust gap. The future of AI in regulated industries depends on governance frameworks, ethical AI design, and user control mechanisms that allow professionals to verify, adjust, and override AI recommendations when necessary.

The key principles of building AI you can trust

Crafting AI systems that users can rely on requires intentional decision-making at every stage of development, from data collection and model training to fine-tuning, deployment, and user interaction. Building trust in AI systems must be a strategic priority embedded throughout the digital product lifecycle.

Here’s how organizations can embed trust into their AI systems for long-term sucess.

1. Transparency in AI

AI shouldn’t feel like magic. Yet, for many, it does. Trust in AI crumbles when it operates as a black box, which is a system that delivers answers but hides the reasoning. The solution is glass box AI - crafting models that don’t just decide but show their work.

AI models and agents become more trustworthy when they provide reasoning behind their outputs. Instead of just delivering answers, they should break down how they arrived at them, whether by weighing different factors, referencing past patterns, or linking to external data.

For example, A code-generation AI should explain why it suggests one algorithm over another, considering efficiency, security, or best practices. When AI reveals its thought process, users can verify, challenge, and refine decisions, turning AI into a collaborative, explainable tool rather than an opaque system.

One way forward is Neurosymbolic AI, which blends deep learning’s pattern recognition with symbolic AI’s logical reasoning. Instead of a model simply saying “This patient has early-stage lung disease”, it explains:

🩺 “This diagnosis is based on abnormal lung density, smoking history, and genetic markers. Similar cases suggest a 90% match.”

‍A real-world example is Mendel, a company using Neurosymbolic AI to enhance clinical decision-making. Mendel uses Neurosymbolic AI to improve clinical decision-making by not just extracting insights from medical records but explaining them. Instead of simply providing a risk score, it links findings to lab results, physician notes, and past case studies.

This allows doctors to trace conclusions to their sources, ensuring transparency and trust in AI-driven recommendations.

How businesses can embed transparency

✅ Show the ‘why’. Use AI techniques that make decisions traceable and explainable.

✅ Let users challenge AI. Loan denials, hiring decisions, or medical diagnoses should be reviewable and adjustable.

✅ Make transparency a feature. Companies that explain AI well will win trust faster.

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2. Reliability in AI

Trust in AI comes from knowing it will perform consistently and fairly in every situation. A model that produces different results depending on the time of day or the type of user isn’t just unreliable, it’s unusable.

To build reliable AI, companies focus on key metrics:

• Accuracy – Ensuring outputs are correct and verifiable.
• Robustness – Handling unexpected inputs without errors.
• Fairness – Avoiding bias across different user groups.
• Uptime & Latency – Maintaining fast, consistent performance.‍
• Explainability – Making AI decisions transparent and traceable.

A real-world example is Waymo’s self-driving AI, which improved reliability by training on 20+ million miles of real-world driving data and billions of simulated miles. By continuously refining its models to handle edge cases like pedestrians, cyclists, and bad weather, Waymo increased safety and trust, proving that consistent AI performance is key to adoption.

Similarly, IBM Watson Health is a well-known example of why reliability must be a priority from day one. Watson was designed to assist doctors by analyzing vast amounts of medical literature and patient data, but early versions struggled with consistency.

The same symptoms could lead to different recommendations depending on the dataset Watson had been trained on. IBM had to rethink its approach, improve its training data, introduce stricter validation processes, and ensure that human experts reviewed AI-generated recommendations before using them for accurate diagnoses. The lesson is simple: reliability isn’t something you check once and forget. It’s an ongoing process of refinement and validation.

AI isn’t static. Models evolve as they learn from new data, but that learning must happen in a controlled and predictable way. AI can drift, degrade, or even reinforce biases over time without the right safeguards. This is why continuous monitoring and testing are as crucial as the initial development. AI must work well tomorrow, next year, and across every new dataset it encounters.

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How businesses can build reliable AI

✅ Test AI across diverse conditions. AI must be validated across different demographics, industries, and datasets in real-world environments.

✅ Monitor AI performance over time. AI models degrade if left unchecked. Regular audits and retraining are essential.

✅ Ensure fairness and consistency. Bias detection isn’t a one-time fix. AI must be continuously evaluated to ensure it performs fairly across all user groups.

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3. Security in AI

People won’t trust AI if they don’t trust how their data is handled. The more advanced AI systems become, the more they rely on personal, financial, and sensitive business data to make decisions. A system that processes medical records, legal documents, or financial transactions must be designed with security-first principles. Without strong protections, AI can become a liability rather than an advantage.

AI security goes beyond just preventing breaches. It needs to ensure that AI systems operate safely, ethically, and keep up with the dynamic changes in compliance and global regulations.

Data leaks, algorithmic manipulation, and unintended model biases can all undermine confidence in AI. For example, a legal AI assistant handling confidential contracts should never expose sensitive information, just as a financial AI shouldn’t use customer data without explicit consent. AI must be built with guardrails not just to avoid failure, but to protect the very trust that makes it valuable in the first place.

One example is Bank of America’s AI-powered virtual assistant, Erica. Designed to help customers manage their finances, Erica processes banking transactions, loan details, and personal financial insights.

Bank of America built the system to ensure security with end-to-end encryption, strict access controls, and compliance with regulations like GDPR and the Consumer Financial Protection Bureau (CFPB) guidelines. More importantly, Erica operates within a zero-trust security framework, meaning customer data is never stored beyond necessary interactions, minimizing risk.

How businesses can build secure AI

✅ Embed security from day one. AI systems must be designed with encryption, access controls, and risk management at their core.

✅ Minimize data exposure. Use techniques like federated learning to train AI without centralizing user data where possible.

✅ Stay ahead of regulations. Businesses should anticipate privacy laws like GDPR and CCPA rather than react to them.

4. Agency and control in AI

Trust in AI depends on whether people feel in control of how AI influences their decisions. When systems generate results that users cannot adjust, interpret, or challenge, they become less tools for empowerment and more sources of friction. This is why agency, the ability of humans to guide and refine AI behavior, is essential to building systems that people can rely on.

The way large language models are trained offers a strong example of how control shapes trust. In platforms like the ChatGPT Playground, users influence AI behavior through key parameters that adjust creativity, depth, and response style.

Temperature settings determine whether an AI prioritizes logical precision or explores more creative possibilities. System prompts allow users to define the AI’s role, ensuring it aligns with specific professional or ethical standards. Probability sampling techniques, such as Top-P and frequency penalties, fine-tune diversity and repetition, allowing for more predictable or varied outputs depending on the context.

These technical adjustments reflect a more significant principle in AI design. People do not just need AI to deliver answers. They need AI that allows them to explore different perspectives, refine assumptions, and maintain oversight.

Recent AI research reinforces this shift toward more interactive and explainable models. A 2024 study from Stanford’s Human-Centered AI Institute found that professionals across industries were significantly more likely to trust AI when they could modify decision-making parameters and receive transparent explanations of how a model arrived at a conclusion. The study emphasized that AI adoption is about improving accuracy and creating systems that invite human input rather than demanding blind trust.

As AI moves into more critical decision-making roles, from healthcare diagnostics to enterprise automation, human agency must remain a foundational principle. The most effective AI systems will not just generate insights. They will enable people to shape the reasoning behind those insights, ensuring that AI enhances judgment rather than replacing it.

How businesses can leverage AI for user control

✅ Enable interactive decision-making. AI should allow users to adjust inputs, test assumptions, and explore alternative outcomes.

✅ Make AI explain its reasoning. Users need to see why AI made a certain prediction and how different variables influenced the outcome.

✅ Keep humans in the loop. AI should act as a strategic advisor, not an autonomous decision-maker.

Trust as the center of AI systems

The true measure of AI’s success will not be its raw intelligence but the trust it earns. No matter how powerful an AI system is, its impact will be limited unless people believe in its fairness, reliability, and security. Trust is not automatic; it is built through transparency, consistency, and a deep commitment to human oversight.

This combination of transparency, personalization, and ethical data practices creates a multi-layered foundation of trust that should always sit at the center of an AI system when you map it.

At Aubergine, we have seen firsthand that trust is the foundation of AI adoption. In real estate, where buying a house represents one of the most significant financial decisions a person can make, buyers and sellers require more than just data-driven insights; they need unwavering confidence in the system guiding them. When we developed an AI agent for a real estate app, we recognized that the system had to deliver accurate recommendations, articulate its reasoning clearly, and demonstrate reliability.

Building a trustworthy AI agent for large-purchase decisions involves multiple layers of trust. We prioritize transparency in our algorithms and decision-making processes, allowing users to understand how recommendations are formed. Additionally, we focus on personalizing our AI's recommendations to align closely with each user's unique requirements and preferences.

Ultimately, our AI empowers users to make informed decisions by providing clear reasoning behind its suggestions and demonstrating a consistent track record of reliable outcomes, all while maintaining data security.

The future of AI will not be led by the most advanced models alone but by those that empower people. The systems that are transparent, responsible, and built to serve will be the ones that shape the world.

Our commitment to creating an AI that people can trust for significant purchase decisions is central to our mission at Aubergine. If you’re looking to build for the next generation of AI-human interactions, let’s talk.