Maria is an experienced marketing professional in both B2C and B2B spaces. She’s earned certifications in inbound marketing, content marketing, Google Analytics, and PR. Her favorite topics include digital marketing, social media, and AI. When she’s not immersed in digital marketing and writing, she’s running, swimming, biking, or playing with her dogs.
AI Ethics Definition:
AI ethics are a set of moral guidelines for the responsible use, design, and development of AI.
AI has been changing how we work and live. In marketing, AI has delivered benefits such as increased efficiency and improved user experience, thus helping companies get ahead of their competitors.
However, along with its benefits come concerns.
Expert insights from
Shane JonesVP of Earned Media and Content at WebFX
The future undoubtedly involves AI. Businesses that learn, integrate, and embrace AI the best will leave others behind. Consumers will be looking for companies that are on the cutting edge as well, so having positioning around AI is also of the utmost importance. [However], behind the sleek and easy-to-use interface are complicated machine learning and LLMs. They pose a lot of cybersecurity threats.
As AI’s global market revenue is forecast to grow by $107 billion in 2028, it’s vital to have ethical guidelines to reduce risks.
This blog post will discuss AI ethics in marketing and why it’s important to practice the ethical use of AI. Feel free to jump into the topics:
AI ethics are a set of moral guidelines for the responsible use, design, and development of AI. These guidelines are necessary so that AI developers and adopters can better serve humanity and reduce the risks that the technology poses in these areas:
Bias and errors
Privacy
Environmental concerns
Why are AI ethics in marketing important?
As AI becomes ubiquitous and part of many people’s lives, it has become critical for businesses to engage in the ethical use of AI.
Without AI ethics, bias, privacy, environmental, and other issues can crop up. Ethical guidelines ensure that AI projects use unbiased and accurate data that represents various groups.
If we neglect ethics in AI, we can see the following issues arise:
Biases: When AI systems misinterpret user data, the results can perpetuate social biases, reflecting poorly on companies that fail to implement ethical guidelines.
Trust: Organizations must their AI use only data that their customers and prospects have agreed to share, thus respecting their data privacy. Without permission or data safeguarding, the customer data you’re entrusted with is not secure from data breaches.
Environmental issues: AI projects require high computing power, meaning that every use contributes to energy consumption. Using AI improperly and not accounting for energy waste and consumption can lead to damage to the environment.
It comes with ethical concerns, though. “For me, the biggest concerns with AI are how this technology becomes commonplace in our daily flow,” Shane, the VP of Earned Media and Content at WebFX, shares.
Do your customers know you are collecting and using AI to process their data? To gain the trust of your customers and prospects, it’s important to disclose when an AI is handling or processing their data to enhance their experience with your brand.
For example, if you implement AI content personalization tactics to make browsing your massive product catalogue easier, inform your users.
2. Privacy
Among the marketers we recently surveyed, data privacy is one of their top concerns with using AI for marketing. To protect the privacy of your customers and prospects, businesses like yours must give your customers to opt out of your data collection.
It’s also critical for businesses to safeguard the data they’ve collected to prevent data breaches and legal conflict.
3. Bias
Bias, inaccuracies, and discriminatory are other possible outcomes of AI ethical concerns.
“Most of AI’s language models are built on data from the past,” Shane points out. “They can’t fully recognize the subtle changes and progress in language and society.”
As a result, prompts to large language models (LLMs) like GPT will result in continued bias, Shane explains. “The outcomes will just repeat previously demonstrated and repeated behaviors, which are not always beneficial,” he says.
Shane is optimistic that we can overcome bias, though. “Algorithmic decision-making — when used in the right places and with the right data — can offer an objective way of overcoming human subjectivity and bias,” he says. “If we use data based on previous human decisions, then we will continue to replicate and embed the biases that already exist into our system.”
If you’re using AI, make sure your data sets are diverse and representative of various groups. Training an AI with data of disproportionate representation can result in inaccurate predictions. It can also result in biased assumptions and unfairly targeted advertisements.
Make sure you regularly audit your data and AI system.
When users enter prompts on systems like ChatGPT, they tend to trust the AI’s recommendations. These AI systems, however, can sometimes respond with hallucinated sources, statistics, and links. They even link to coding libraries that don’t exist. Because AI systems like ChatGPT are trained with past data, they may even link to old blogs that are no longer live.
Cyber attackers can take advantage of this flaw. By asking ChatGPT for coding solutions, they discover links to non-existent packages.
Attackers can publish their malicious package with the URL suggested by an AI. As a result, when a user asks an AI for a coding solution, they get a recommended URL that directs to the malicious link.
Implementing a robust AI ethics framework involves regular auditing of AI decisions, transparency in algorithms, and active inclusion of diverse datasets during development.
Consider the following frameworks when creating your business’s ethical practices surrounding AI:
Establish governance
Governance is the act of overseeing the AI lifecycle through internal policies, staff, and systems. Establishing governance means taking an active part in AI usage by:
Deciding how AI will be used, including roles and responsibilities associated
Educating users about the lifecycle of AI and any ethical concerns
Establishing processes for building, monitoring, and communicating about AI and risks
Outline principles
Each business should define the principles that will guide their approach to AI through policies, processes, and practices. For example, your businesses principles might include:
Fairness
Communication
Trust
Etc.
Ensure privacy
Companies should take great care to ensure the privacy of their workers and the customers they target. Both sides should be able to see how these tools work and communicate their concerns.
Protecting customer (and employee) data will help prevent misuse, along with other security concerns.
Pursue knowledge
There are plenty of institutions documenting the progression of AI and finding ways to make it safer and more regulated for individuals. Here are a few sources for finding information on AI ethics:
AlgorithmWatch: This nonprofit focuses on an explainable, traceable algorithm and decision process in AI programs, leading to better programs and safer information sourcing.
AI Now Institute: This nonprofit at New York University researches the social implications of AI.
Current legislation and future considerations for AI
As of April 2025, there is no specific legislation at the federal level to regulate the creation and use of AI. There are some movements happening at the state level, but any regulation relies on preexisting laws.
Future legislation could include requiring licensing and creating a new federal regulatory agency. Some other possible AI considerations include:
The REAL Political Advertisements Act: Aims to regulate generative AI in political advertisements.
The Stop Spying Bosses Act: Aims to regulate employers surveilling employees with machine learning and AI techniques.
The Draft No FAKES Act: Would protect the voice and visual likenesses of individuals from unauthorized recreations from Generative AI.
WebFX and AI ethics
WebFX practices responsible use of AI. In addition, we are focused on reducing hallucinations and the impact of bias in ChatGPT’s responses. How?
“The first step in mitigating issues is to understand the technology better and have appropriate expectations on how it can be integrated,” Shane explains.
He adds that all WebFX’s Earned Media team members have completed — or are scheduled to complete — an Ethics course on the use and training of AI at the Polytechnic University of Milan, Italy’s largest technical university.
All the content we produce — including this blog post you’re reading — is also created by humans. “We guarantee that every piece of content is original, meaning our pieces are not duplicate content, plagiarized, or AI-generated,” Shane shares.
He adds: “If we use AI in our research process or plan around Google’s natural language processing for ranking content, we invest in time and resources to verify any statement or research from these tools before we use them.”
By doing so, our customers are assured that the content we produce is “truthful, accurate, timely, and clear of any ethical biases,” Shane adds.
Leverage AI ethically with WebFX
AI delivers several benefits for marketing and for businesses like yours. However, it’s critical to follow AI ethical guidelines to ensure you’re using the technology responsibly and protecting your customers’ data.
Our proprietary revenue growth platform, RevenueCloudFX, is an AI-powered software that enables you to capture more leads, draw insights from your data, and close more sales. You can rest assured that our team practices ethical use of AI so that you can foster trust among your prospects and customers.
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Shane JonesVP of Earned Media and Content at WebFX
The future undoubtedly involves AI. Businesses that learn, integrate, and embrace AI the best will leave others behind. Consumers will be looking for companies that are on the cutting edge as well, so having positioning around AI is also of the utmost importance. [However], behind the sleek and easy-to-use interface are complicated machine learning and LLMs. They pose a lot of cybersecurity threats.
Maria is an experienced marketing professional in both B2C and B2B spaces. She’s earned certifications in inbound marketing, content marketing, Google Analytics, and PR. Her favorite topics include digital marketing, social media, and AI. When she’s not immersed in digital marketing and writing, she’s running, swimming, biking, or playing with her dogs.
