Ethical AI: Can We Trust Artificial Intelligence in the Workplace?

Artificial intelligence is rapidly taking hold within organizations. Whether it’s automating certain tasks, analyzing large volumes of data or improving the customer experience, its promises are plentiful. This accelerated adoption, however, raises legitimate questions. Can AI be trusted in the enterprise? The answer is yes, provided it is ethical, governed and designed to support human judgment rather than replace it.

Rather than feeding fear or giving in to blind enthusiasm, it’s essential to approach AI with clarity. Understanding how these systems work, recognizing their limits and putting clear guardrails in place for AI governance not only allows you to extract value, but also to deploy ethical AI that earns the trust of users, employees and customers.

Can an AI like ChatGPT be trusted?

Before answering that question, we need to clarify what “trust” really means. In a human context, trust is a feeling of confidence and security, along with a firm belief that one can rely on another person and that their actions will be loyal, competent, and aligned with our expectations. An artificial intelligence has none of those dimensions. It has no consciousness, moral judgment or human-like understanding of the world.

ChatGPT and other language models are, first and foremost, tools. Like a search engine, a spreadsheet or a calculation program, they amplify human capabilities but do not replace them. Their reliability depends directly on how they are designed, used and supervised.

Technically speaking, large language models operate probabilistically. They predict the most likely word or phrase based on what they saw during training. They do not reason or fact-check. A common example illustrates this limitation well. Models have learned that “2 + 2” is usually followed by “4” without truly understanding the mathematical concept. When the context becomes more complex (for example: an addition the model has never encountered), errors can appear.

You can compare this functioning to a child learning to read. The child recognizes shapes, images, words and structures, but does not always grasp the deeper meaning of what they read. The result can seem convincing while still being incorrect.

This is where the main risks lie. Although they can produce convincing responses, generative AIs have several limitations that are essential to understand:

• Hallucinations: the AI can invent facts, sources or plausible answers without a real basis.

• Bias from training data: models sometimes reproduce and amplify biases present in the data used to train them.

• Misinterpretation of context: an ambiguous or poorly phrased question can lead to an inappropriate or misleading answer.

• False sense of certainty: the AI can present inaccurate answers in a confident tone, increasing the risk that they’ll be trusted too readily.

Like any powerful tool, AI cannot be used without human oversight. Humans must remain in the loop to verify facts, contextualize responses, manage risks and assume full responsibility for decisions made in the enterprise.

Should we be afraid of artificial intelligence?

Given these limitations, some narratives tend to demonize artificial intelligence. It is important, however, to distinguish irrational fear from responsible vigilance. Fear is often fuelled by science-fiction scenarios or by a lack of familiarity with real technologies. Vigilance, on the other hand, rests on a clear-eyed understanding of the risks.

Misuses have already been observed. Automated hiring systems that reproduce discriminatory biases. Surveillance technologies used intrusively. Opaque algorithmic decisions with no recourse for affected individuals. These examples do not show that AI is dangerous by nature, but rather that its use without a framework can be.

The real danger lies less in the technology than in how it is implemented. Rapid adoption without ethical reflection, clear governance and defined accountability can lead to significant consequences. Conversely, stifling innovation out of fear of change also carries risks. Organizations that refuse to explore AI expose themselves to strategic lag.

The issue is therefore not choosing between innovation and ethics, but understanding that ethics is a condition for sustainable innovation. Implementing guardrails allows you to innovate with confidence and credibility.

What are the principles of trustworthy AI?

To avoid improvisation, several frameworks have been proposed in recent years. They provide clear guidance for designing and using AI systems.

As early as 2018 in Quebec, the Montreal Declaration for Responsible AI proposed 10 principles to govern the development of artificial intelligence:

• Well-being: AI systems should contribute to improving living conditions, health and the well-being of all people. 

• Respect for autonomy: AI should be designed to increase individuals’ control over their lives and choices. 

• Privacy and protection of personal data: personal data must be protected to preserve people’s freedom and dignity. 

• Solidarity: AI development should promote social cohesion and avoid amplifying inequalities. 

• Democratic participation: citizens should be able to express themselves and take part in decisions that govern the use of AI. 

• Fairness: AI must be developed and used in a way that guarantees equal access and treatment for everyone. 

• Inclusion of diversity: systems must account for cultural, social and demographic differences to avoid discriminatory biases. 

• Prudence: risks should be anticipated and mitigated, adopting a measured and thoughtful approach to AI deployment. 

• Accountability: designers and users must be accountable for the effects of AI on people and society. 

• Sustainable development: AI should be considered from an environmental and social sustainability perspective.

These ethical concerns are not confined to academia. The European Union, the Government of Canada and the Government of Quebec have all put principles and frameworks in place to guide AI deployment.

Despite different approaches and contexts, these initiatives converge on the same conclusion: AI must be developed and used responsibly, transparently and in respect of human rights. Ethics thus becomes a cornerstone of any AI implementation strategy.

And these principles are not merely theoretical. They form the foundation for very concrete decisions, whether related to technical choices, organizational processes or user experience design.

How do you make AI ethical and more reliable?

Reliability and ethics cannot be tacked on at the end of a project. They must be integrated from the design phase. Trust is built in the details.

UX/UI design

One lever is design. It’s essential to create user journeys (user experience, UX) that make the presence of AI, its capabilities and its limits explicit so users understand the system’s real role in decision-making. Interfaces (user interface, UI) should materialize this transparency through clear, visible elements such as an indication of confidence level for a response or explanations about its origin.

Together, thoughtful UX and explicit UI promote algorithmic transparency and explainability, reduce misunderstandings and strengthen user trust in AI systems deployed in the enterprise.

Learn how our UX/UI design service can help you craft clear interfaces and user journeys that build trust and make AI genuinely understandable.

Data quality

Data quality is another central element. Biased or outdated data will produce biased results. It is essential to choose the right models, document sources, diversify those data sources and implement continuous update mechanisms. Monitoring for emerging biases must be an integral part of the product lifecycle, alongside system audits and regulatory compliance requirements.

Feedback loops

Human feedback loops also play a key role. For sensitive decisions, human validation should be mandatory. Users must be able to report errors, and that feedback should be handled in a structured way. System learning must remain controlled and supervised.

Quality assurance

Finally, regular testing is indispensable. Testing performance is not enough. You must also evaluate robustness, error scenarios and potential ethical impacts. A reliable AI is one that is continuously questioned.

Building responsible AI: a technological and human commitment

Building responsible AI is not the remit of a single role. It is a collective effort that requires interdisciplinary collaboration. Engineers ensure technical soundness. Designers ensure clarity and comprehension of interfaces. Lawyers ensure regulatory compliance. Ethicists provide critical perspectives on long-term impacts.

Ethics must be integrated into the software development lifecycle, alongside security and performance. From the discovery phase, it’s important to identify risks, document choices and justify trade-offs. This transparency facilitates audits and strengthens organizational accountability.

Governance also plays a central role. Regular audits, monitoring of real-world use and mitigation plans allow systems to be adjusted over time. Trust cannot be decreed; it is built by aligning stated intentions with concrete practices.

Conclusion

So, can AI be implemented in the enterprise without compromising trust? The answer is yes, if it’s approached for what it really is: a powerful tool that must be governed, explained and supervised. Trust in AI does not rest on the technology itself, but on how it is designed, governed and integrated into business processes.

By focusing on ethical AI, organizations can reconcile innovation and responsibility. Transparency, human oversight, risk management and respect for users then become strategic levers rather than constraints. When considered from the outset, ethical AI not only reduces risks but also enhances corporate credibility and creates lasting value.

Want to integrate AI into your application? Talk to our experts to turn its potential into concrete value without compromising trust.

FAQ

Does ChatGPT tell the truth?

Not always. ChatGPT generates plausible answers based on probabilities without fact-checking or understanding context like a human. It can therefore produce errors or incomplete information. Human validation remains essential, especially for important decisions or sensitive data.

How do you build ethical AI without compromising trust?

Trustworthy AI is designed from the outset around ethical principles. It relies on transparent UX and UI design, high-quality documented data, continuous human oversight and regular testing that evaluates not only performance but also risks and ethical impacts.

Should we be afraid of AI?

No, but we must remain vigilant. AI is above all a tool, and human choices determine its impacts. Fear paralyzes, whereas responsible governance enables value creation while protecting users and society.

How do you make AI more reliable?

AI becomes more reliable when framed by human validation mechanisms, structured feedback loops and regular testing. It’s also essential to adopt a clear, transparent design that exposes system limits and helps users interpret results correctly.

What are the principles of trustworthy AI?

Trustworthy AI rests on several key principles, including human oversight, transparency, fairness, security and privacy protection. Added to this is accountability, which makes it possible to clearly identify the actors responsible for AI-driven decisions and impacts.