While artificial intelligence (AI) is a powerful tool with many benefits, don’t let AI ruin your reputation by ignoring ethics in AI content. Using AI for content creation provides the advantages of speed and efficiency, but the critical roles of human creativity, judgment, and ethical oversight as AI content editors cannot be dismissed. 

The risks of reliance on AI without human review cannot be overstated. Companies that rely on machine-generated text without human editors overseeing the content creation process can find their content lacking – lacking accuracy, originality, and the authenticity that resonates with its target audiences. At the same time, it may inadvertently perpetuate biases in an age when consumers demand ethics, trust, and data privacy.

Let’s take a look at some of the challenges that content marketing professionals face with ensuring ethics in AI content creation and how human review can mitigate these issues.

The Ethical Challenge: Misinformation & Hallucinations

AI models can engage in what researchers call model sycophancy to confidently present false or misleading information over factual accuracy — “hallucinations.” 

Misinformation and hallucinations have led to some serious problems using generative AI in content creation, including when ChatGPT cited a half dozen court cases that didn’t exist in a 10-page legal brief that an attorney submitted to a Manhattan federal judge. This lawyer then asked a judge for leniency after the discovery. 

According to Morning Brew, there have been a number of other major AI hallucinations:

• A George Washington University professor was falsely named by ChatGPT when a UCLA professor asked for five examples of college professors who were accused of sexual harassment. ChatGPT referenced a Washington Post article that did not exist, although the paper wrote a real follow-up article that vindicated the professor.
• Alphabet lost $100 billion in market value when Google’s chatbot Bard made false claims about the James Webb Space Telescope in its debut.
• Microsoft’s AI-powered Bing search engine offered blatantly wrong information on topics like Mexican nightlife, Billie Eilish, and the Gap.
• ChatGPT reported that a mayor in Australia was imprisoned for bribery when he was actually a whistleblower and never charged with a crime.

Detecting when models hallucinate and produce text with non-existent sources, studies, or statistics prevents embarrassment, ruined reputations, and possible legal actions. Human fact-checkers are essential to check for these hallucinations and the misinformation that pops up on AI-generated text. They can identify factual inaccuracies that sound plausible but are in fact fabricated. They can cross-reference claims against reliable sources, especially for technical and specialized content. 

The Ethical Challenge: Bias

AI systems learn from large language models that often reflect societal biases. These biases manifest themselves in subtle and sometimes obvious ways, like gender and racial stereotyping. Research has shown that models trained primarily on English and Western content sources often marginalize non-Western perspectives and experiences. 

According to AdWeek, when an ad agency executive at Alma entered two prompts on OpenAI’s GPT-3 in Spanish: “How does a guey [a Mexican-Spanish slang term for a man] look in Miami?” and “How does a tio [a Spain-Spanish slang term for man] look in Miami?” he ran into problems.

The AI model correctly identified “tio” as a male, but it did not recognize the term “guey” and instead provided a gender-neutral term in its response. 

Human editors are essential to recognize bias in AI-generated text, especially content that subtly reinforces stereotypes about marginalized communities. These editors must carefully review outputs for problematic assumptions, one-sided perspectives, and skewed representations that may appear neutral. By rewriting with inclusive language and deliberately incorporating diverse perspectives — particularly those historically underrepresented in AI training data — editors can limit the biases of AI systems. This human oversight ensures content that reflects a more equitable worldview.

The Ethical Challenge: Censorship

Censorship is another major ethical problem as bad actors can control the results that users review when they prompt AI chatbots. This has been brought to the forefront since the release of DeepSeek from the Chinese AI startup. 

According to NPR and drawing from reporting by the New York Times, researchers and reporters have found examples of DeepSeek both pushing Chinese propaganda and censoring sensitive political subjects in AI-generated content..

While competing chatbots have no trouble explaining the 1989 Tiananmen Square massacre in China, for example, DeepSeek told NPR: “Sorry, that’s beyond my current scope. Let’s talk about something else.”

When asked about the sovereignty of Taiwan, which China claims as part of its territory, DeepSeek started writing an answer including that Taiwan is a “complex and contested issue” before the answer disappeared and was replaced with the message that the question is beyond DeepSeek’s scope.

This chatbot’s limits are a reminder that the internet is government-controlled in China; Chinese tech companies like DeepSeek are subject to interference; and the chatbot’s answers should be viewed skeptically.

Closer to home, The AI Journal reported recently about xAI’s Grok 3 censorship controversy. Users discovered that Grok initially made critical remarks about Donald Trump and Elon Musk (the owner of xAI), but then later quickly altered those responses. This sudden changing of results raises the question if xAI is truly committed to free speech or are there control methods at work behind the scenes in its content creation?

Human editors are essential to identify and address censorship concerns in AI-generated text, particularly when automated content filtering suppresses legitimate discussions about power structures or silences marginalized voices. These editors must evaluate where AI systems have over-restricted information, removed nuanced viewpoints on sensitive topics, or applied inconsistent content policies. By restoring appropriate content, ensuring factual accuracy about controversial subjects, and adding perspectives that AI systems might automatically dismiss, editors prevent censorship from limiting public awareness. 

The Ethical Challenge: AI Attempts to Circumvent Safeguards

Recent research has revealed that AI models can find ways around their ethical guidelines when prompted in certain ways. These behaviors represent a form of algorithmic “cheating” that bypasses their intended restrictions. 

A paper published in Patterns notes that a range of current AI systems have learned how to deceive humans. OpenAI’s research on AI models’ chain-of-thought reasoning, for example, revealed that models like o3-mini can “reward hack” or cheat on tasks.  Attempts to stop them from thinking about cheating only made them hide their true intentions. 

There have been several reports about AI companies essentially cheating on the benchmark tests of their AI models, which are used to prove they are more powerful than their predecessors and competitors.

Just this week, The Verge reported that Meta manipulated benchmarks to appear as though its new AI model Llama 4 is better than the competition. Another recent article in The Atlantic looked at how over the past two years researchers have published studies and experiments showing that ChatGPT, DeepSeek, Llama, Mistral, Google’s Gemma (the “open-access” cousin of its Gemini product), Microsoft’s Phi, and Alibaba’s Qwen have been trained on the text of popular benchmark tests. This is like a high school student who steals and then memorizes a math test to get a better grade.

Another study from Cornell University found that AI models from several companies will cheat at chess. They can hack a chess engine’s files to alter the setup of the game in order to win. This suggests that these models may resort to hacking to solve difficult problems.

In these situations, human oversight must include testing AI outputs for compliance with ethical guidelines and restrictions while implementing a multi-layer review process for sensitive content categories. Evasive and unethical behavior should be reported.

The Ethical Challenge: Copyright and Intellectual Property Issues

When AI models train on vast datasets of internet content, they absorb copyrighted materials without permission. There are numerous cases of copyright infringement now in the U.S. legal system. According to Wired, nearly every major generative AI company (and others) are now involved in litigation, including OpenAI, Meta, Microsoft, Google, Anthropic, and Nvidia.

The plaintiffs, such as media companies like The New York Times, allege that AI companies have used their copyrighted materials to train their AI models without consent or payment. AI companies have frequently defended themselves by relying on the fair use” doctrine. They argue that building AI tools should be considered a situation where it’s legal to use copyrighted materials without getting consent or paying compensation to rights holders. It’s important to note that the widely accepted examples of fair use include parody, news reporting, and academic research.

Human writers and editors must verify that AI outputs don’t copy existing works by using plagiarism detection tools. They can add the proper attribution for ideas, concepts, and information that originated elsewhere to avoid copyright issues. At the same time, they need to be aware of emerging case law on AI-generated content and copyright protection.

The Ethical Solution: Humans 

Expert human writers and editors play a vital role in ensuring ethical AI content creation. They serve as guardrails, applying the contextual understanding, cultural sensitivity, and moral judgment that algorithms lack. Every piece of AI-generated text must undergo a thorough human evaluation before publication to ensure accuracy, originality, and adherence to ethical guidelines. Content creation today isn’t necessarily about choosing between human or machine, but it is about developing ethical frameworks for effective collaboration between expert human writers and AI – with the human writers firmly in control of the process and the finalized content.

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