“AI may assist, but humans must think. AI may draft, but humans must verify. AI may improve language, but humans must own the meaning.” – MJ Martin
Introduction
Artificial intelligence has quickly moved from novelty to workplace utility. Employees now use AI to draft emails, summarize documents, write proposals, develop strategies, prepare reports, analyze data, and answer difficult questions. The controversy involving U.S. Representative Anna Paulina Luna, where the phrase “Claude responded” reportedly appeared in legislative material, shows why this issue matters. Even if AI was used only for spell checking, the incident created a larger question: when is AI use acceptable, and when does it become unethical?
AI as a Workplace Tool
Using AI at work is not automatically wrong. In many cases, it is no different in principle from using a calculator, spell checker, search engine, template, or grammar tool. AI can help people organize their thinking, improve clarity, reduce repetitive work, and generate first drafts that humans can review. In proposal writing, technical reporting, customer communications, and strategy development, AI can save time and help users consider ideas they may have missed.
The ethical issue is not the tool itself. The ethical issue is how the tool is used, how much human judgment remains, and whether the final work is truthful, accurate, confidential, and accountable.
The Question of Disclosure
One major ethical concern is disclosure. If AI materially contributes to a document, should that be stated? The answer depends on the setting. A short internal email may not require disclosure. A legal filing, government bill, engineering report, medical document, public policy paper, or proposal submitted to a client may require much greater transparency. The more important the document, the more important it becomes to know how it was created.
Disclosure is not about embarrassing the author. It is about preserving trust. People need to know whether the work reflects professional judgment or whether it has been heavily shaped by a machine that may not understand the consequences.
Accountability Cannot Be Outsourced
The most important rule is simple: AI cannot be responsible. People are responsible. If AI creates an error, invents a fact, misstates a law, or produces a weak strategy, the blame still belongs to the person or organization that used it. A professional cannot say, “the AI wrote it” as a defense.
This matters especially in legislation, contracts, engineering, finance, public communications, and business proposals. These documents influence real decisions. They allocate money, create obligations, shape public policy, and affect people’s lives. AI can support the work, but it must not replace professional accountability.
Confidentiality and Data Risk
Another ethical concern is confidentiality. Employees may be tempted to paste customer records, business strategy, pricing, legal terms, employee information, or proprietary technical details into AI tools. That can create serious privacy, security, and commercial risks. Ethical AI use requires clear rules about what data may be used, where it may be entered, and whether the AI platform is approved by the organization.
A company that encourages AI use without data governance is creating risk, not innovation.
Is It Wrong?
AI writing is not wrong when it is used honestly, carefully, and under human control. It becomes wrong when it is hidden, careless, misleading, plagiarized, factually unchecked, or used to create the appearance of expertise that the author does not possess.
The ethical standard should be this: AI may assist, but humans must think. AI may draft, but humans must verify. AI may improve language, but humans must own the meaning.
Summary
The workplace should not ban AI writing simply because it is powerful. Instead, organizations should define responsible use. AI can be ethical when it improves quality, saves time, and supports better thinking. It is unethical when it replaces judgment, conceals authorship, violates confidentiality, or allows people to avoid responsibility. The future of AI writing should not be based on fear or blind enthusiasm. It should be based on transparency, competence, and accountability.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees – in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven major certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 80 next generation MOOC (Massive Open Online Courses) [aka Micro Learning] continuous education programs in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Power BI, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.
Martin in a volunteer, a photographer, a learner, a technologist, a philosophizer, and a romantic optimist.