AI Usage — Ethics, Bias, and Hallucination

Artificial Intelligence (AI) is no longer the future, it is the present. Many companies have assimilated AI into their operations, decision making, and delivered outcomes. The modern AI landscape that is constantly learning and adapting through a combination of machine learning, computational power, and algorithmic advancements calls for us all to jump on board. There are many differing opinions around the topic of AI, but many of the dissenting opinions are due to the massive data centers that are popping up across the country. As much of an issue this may be to some, there is no outcome that removes AI from the industries that have adopted and implemented the models into how they work, think, and compete with others. With this rapid adoption comes a set of challenges we can’t ignore: ethics, bias, and hallucination.

Although this is an advancement in technology, this is not a technical issue; it is an organizational issue. AI affects the way we recruit, manage projects with partners, the way we design products, and handle the administrative tasks that we carry out day to day. If we want AI to serve our needs to reach the goals we have set, then we have to treat these challenges as the core of our strategy.

AI must serve the people, not replace them. Those who do not learn to properly use AI won’t have their jobs replaced by it, their jobs will be replaced by someone who does know how to use AI in an ethical manner.

Ethics: The Foundation of AI Integration

When it comes to AI and ethics, the conversation can feel confusing. After all, how can AI itself be ethical? It can’t. Ethics isn’t built into the code, it’s built into how we use it.

That’s where human oversight becomes essential. Ethical AI use depends on transparency, accountability, and responsible human direction. We determine how AI operates, what data it learns from, and where its influence begins and ends.

AI doesn’t make ethical decisions — people do. The moment humans step out of that loop, the system becomes efficient but morally empty.

It is also key to distinguish between efficiency and ethical responsibility. Since the boom of AI adoption in the workplace has taken place, the main theme of conversations is, “how can we lower the time and costs associated with tasks through AI?” The supporting theme that is missing here is that of ethical use. The question should be, “how can we ethically use AI as a tool to reach our goal in the most efficient way possible?” Removing the ethical boundary is the source of many problems that we are seeing come out of AI use in the workplace. Without ethical responsibility, we get reckless in our use of AI.

Every AI-enabled decision should be traceable, reviewable, and explainable. If we can’t articulate how an AI tool reached its conclusion, it doesn’t belong in critical workflows. As mentioned at the start, people will not lose jobs to AI, they will lose jobs to those who know how to ethically use AI to improve workflows.

The core principle: AI should enhance human capability, never replace human responsibility.

Bias: The Silent Architect of Inequity

As AI continues to advance, its ability to shape outcomes grows with it. This influence extends far beyond the algorithms or misinformation. Within organizations, AI tools steer decision-making by how to frame the data, highlight specific results, and/or suggest next steps that we should take.

Deliberately or not, there is a bias that comes with the use of AI, just as it is present within ourselves. Chapman University’s “AI Hub” states, “The human brain is an intricate organ that functions through conscious and unconscious connections. Bias in AI is not merely a technical issue but a societal challenge… Therefore, addressing bias is not only about improving technology but also about fostering ethical responsibility and social justice.” (Bias in AI)

This is why transparency in how AI operates isn’t optional. Leaders need to understand the mechanics behind what the system recommends — what data it’s prioritizing, how it ranks options, and which factors influence the final output. Most importantly, they need to verify that those parameters match the organization’s values.

Key takeaway: Bias cannot be eliminated, but it can be managed with intentional human oversight.

Hallucination: When AI Fabricates Confidence

Before getting into the bulk of this section, I think it is important to define what “hallucination” actually means. Hallucination is the false or misleading outputs presented as a fact.

Common scenarios include: project reports containing fabricated data or sources, AI-generated summaries that distort context, and automated analytics tools drawing false correlations between unrelated metrics. With these common scenarios come consequences like misinformed decision making and/or loss of trust in the systems. Luckily, there are prevention strategies that can be put in place to safeguard decision making.

We are able to prevent these consequences by taking a “trust but verify” approach. There should always be a human review step in any AI use case to protect deliverables from the hallucination. This may sound easy enough, but the human needs to be trained on when/how to question the output to properly validate through fact-checking.

Key takeaway: AI processes data; humans provide truth and judgment.

Practical Applications Across Key Functions

• Hiring: AI assists with efficiency; humans ensure fairness.

• Projects: Use AI for synthesis and tracking, not strategic judgment.

• Products: Build explainability and ethical design into development.

• Administration: Automate repetitive tasks but retain human tone and discretion.

Overarching Principle: AI accelerates processes. Humans ensure purpose.

The Road Ahead: Responsible Acceleration

As organizations continue to integrate AI into their operations, decision making, and delivered outcomes, it is important to remember the three ongoing responsibilities of ethical use, bias mitigation, and hallucination prevention. These are ongoing, not one-time fixes. Ethical AI usage demands constant review and adaption. As long as the organization is using AI as a partner/tool, not a replacement, there should be no worries among the employees that they are using the agents irresponsibly.

The future is human-directed, machine-accelerated, and ethically aligned.

Works Cited

“Bias in AI.” Bias in AI | Chapman University, 2025, www.chapman.edu/ai/bias-in-ai.aspx.