As a society, we spend an enormous amount of time online—whether it’s on social media, websites, blogs, chat groups, or messaging services. The internet has, for all intents and purposes, taken over the world. It has connected people on an unprecedented level and given a voice to many who didn’t have one before. Even our news sources have moved online, making the dissemination of information faster than ever before. However, this rapid spread of information has also significantly increased the propagation of misinformation and disinformation.

Misinformation and disinformation can spread virally, becoming pervasive and persuasive. In the digital marketing landscape, their impact can have far-reaching consequences, affecting everything from public opinion to brand reputation. Understanding the nature of misinformation, how it spreads, and how it impacts marketing is crucial for anyone involved in the digital space.

Understanding Misinformation

Misinformation is defined as “false or inaccurate information that deceives or is mistaken for fact but is not deliberate.” Disinformation, on the other hand, is false information deliberately and maliciously designed to deceive. People may unintentionally spread misinformation due to cognitive biases, illusory truth effects (believing information after repeated exposure), or motivated reasoning (accepting information that aligns with pre-existing beliefs without verification). As a result, trustworthy content is becoming harder to identify and verify.

The COVID-19 pandemic, for example, saw a massive rise in misinformation and fake news that spread almost as virally as the disease itself, with nearly as damaging consequences. Similarly, global political events, such as the 2016 and 2020 U.S. presidential elections, have highlighted the challenges of managing misinformation across different media landscapes.

Brands have also found themselves caught in the crossfire. For instance, companies like Nike and Amazon faced backlash when their ads appeared on conspiracy sites and other pages peddling false information, hurting their credibility and reputation. On the flip side, other brands have successfully navigated these challenges by fostering transparent communication and emphasizing their commitment to truthfulness.

Types of Misinformation

Misinformation comes in many forms, ranging from deceptively simple errors to deliberate fabrications. Here’s a breakdown of how misinformation can be classified:

• Fabricated Content: Completely false information created with minimal or no factual or evidentiary basis.
• Manipulated Content: Factual information, images, or videos that have been altered to present a distorted, sensationalist, or opposing viewpoint.
• Imposter Content: Content crafted to look like it comes from a genuine source but is actually fake.
• Misleading Content: Information that appears factual but is based on false premises or misleading data.
• False Context: Factual information presented out of context to mislead the audience.
• Satire/Parody: Humorous or mocking content designed to imitate real news sources. While often intended as harmless, satire can sometimes fool readers if not clearly identified.
• False Connections: Content represented by captions, headlines, or visuals that do not support the actual content.
• Sponsored Content: Marketing or advertising content presented as editorial or genuine content.
• Propaganda: Content used to influence or manipulate public knowledge, attitudes, or values.
• Error: Mistakes made in reporting by established agencies, often due to oversight or miscommunication.

How and Why Misinformation Spreads

The internet is an incredibly efficient tool for spreading information, but that efficiency also applies to misinformation. By the time misinformation is recognized, it has often been copied, reproduced, shared, tweeted, and discussed millions of times globally. The scale and speed at which misinformation spreads have grown exponentially, with different media playing specific roles in this phenomenon.

On Social Media:

Social media platforms are powerful tools for serving information and connecting brands with their audiences. They play a crucial role in democratizing information and enabling grassroots movements. However, their content-tailoring algorithms can sometimes create echo chambers, where users are primarily exposed to information that aligns with their beliefs, leading to the rejection or hostility toward opposing viewpoints.

Messengers and Chat Groups:

Messaging and chat groups are notorious for spreading fake news and misinformation. Millions of messages are forwarded daily with little to no fact-checking, leading to the rapid dissemination of damaging falsehoods.

Fake Websites:

Fake news sites are a major source of misinformation. Whether politically motivated or driven by other agendas, these sites can cause significant harm by spreading fake healthcare information, false news, or even promoting illegal goods and services.

Marketing Tactics:

In some cases, marketing tactics themselves can be sources of misinformation. While going viral can benefit brands, inauthentic or fraudulent marketing tactics can also propagate misinformation. Brands must be cautious in their strategies to ensure that they do not unintentionally contribute to the spread of false information.

AI’s Role in Spreading Misinformation

The rise of artificial intelligence (AI) has made it easier than ever to create and spread misinformation. AI-driven tools can amplify false information faster and on a larger scale than ever before. However, AI also offers promising tools for combating misinformation, such as automated fact-checking and content moderation.

Deepfakes:

Deepfakes are realistic and believable photos, videos, or audio files that are completely fabricated or manipulated to send misleading messages. The potential for damage to a person or brand’s reputation is incredibly high, as deepfakes can be used to influence public opinion, damage credibility, and sow discord.

Social Media Algorithms:

Social media algorithms are designed to serve users content they are likely to be interested in. However, this can also result in users being exposed only to information that aligns with their pre-existing beliefs, leading to a biased outlook. On the positive side, these algorithms can be leveraged to promote accurate and verified content, helping to counter misinformation.

AI-driven Bots:

Bots have been used in disinformation campaigns, such as during the 2016 U.S. election. However, AI can also be harnessed for good, by developing systems that detect and flag bots, thereby reducing their influence on social media platforms.

Safe Marketing Amid Misinformation

Marketing faces unique challenges in the digital age. Misinformation on social media can significantly impact consumer perceptions, brand reputation, and marketing strategies. Companies need to prioritize accuracy, transparency, and ethical communication to navigate this complex landscape.

Fact-check: Ensuring Accuracy in Every Message

Brands must ensure that all claims made in their marketing materials are accurate and evidence-backed. Misleading information can quickly damage a brand’s reputation and lead to social, legal, or financial repercussions.

• Thorough Verification: Implement a rigorous fact-checking process for all content before it is published, using credible sources and consulting experts.
• Third-party Fact-checking: Collaborate with independent fact-checkers or third-party organizations to add credibility and demonstrate a commitment to truthfulness.
• Regular Audits: Conduct regular audits of past content to ensure ongoing accuracy, issuing corrections or updates promptly if needed.

Be Transparent: Building Trust Through Openness

Transparency is crucial in building and maintaining consumer trust. Brands need to be open about their products, services, and business practices, including any limitations or drawbacks.

• Open Communication: Clearly communicate product details and avoid overstating benefits or making unsubstantiated claims.
• Honest Marketing: Ensure that marketing campaigns accurately reflect the true nature of the product or service.
• Engagement and Dialogue: Encourage open dialogue with customers, using social media and other platforms to address concerns and answer questions.

Stay Vigilant: Monitoring and Responding to Threats

Brands must remain vigilant in the rapidly changing digital landscape, actively monitoring online platforms for misinformation that could harm their reputation or industry.

• Social Listening: Use social listening tools to track mentions of your brand across social media, forums, and news sites, enabling quick responses to misinformation.
• Crisis Management Plan: Develop a crisis management plan with protocols for responding to misinformation swiftly and effectively.
• Partnerships with Media Platforms: Collaborate with social media platforms and online publishers to address misinformation involving your brand promptly.

Be Responsive: Addressing Negative Sentiment Effectively

How a brand responds to misinformation can make all the difference. Negative sentiment can grow quickly if left unaddressed, making it crucial to respond promptly and effectively.

• Quick Corrections: Respond quickly with accurate information when misinformation is identified, issuing public statements, corrections, or sharing verified data.
• Proactive Engagement: Engage directly with your audience to clarify misunderstandings and provide additional context, helping to maintain trust.
• Apologies and Accountability: If the brand has made an error, a sincere apology can go a long way in rebuilding trust.

Educate Your Audience: Empowering Informed Decisions

Educating your audience is a powerful way to combat misinformation. It empowers consumers to make informed decisions based on facts rather than falsehoods.

• Educational Content: Create and share content that explains complex topics related to your industry, using blogs, videos, infographics, or webinars to provide clear, evidence-based information.
• Myth-busting Campaigns: Launch campaigns to debunk common misconceptions in your industry, using credible sources and straightforward language.
• Community Involvement: Engage with your audience in forums, social media groups, and other online communities, offering expert insights and factual information.

Find Credible Partners: Collaborating with Integrity

Marketing is only as effective as the partners you collaborate with. Partnering with reputable organizations, experts, and influencers can reinforce your company’s messaging while working with the wrong partners can negatively impact your brand’s standing.

• Vet Your Partners: Choose partners carefully, vetting their past work and public statements to ensure alignment with your brand’s values.
• Collaborate with Fact-checked Influencers: Work with influencers who prioritize accuracy and transparency in their content, ensuring your brand is represented authentically.
• Engage with Industry Groups: Join industry groups or organizations focused on combating misinformation, contributing to a larger effort to maintain the integrity of your industry.

The Bottom Line

Misinformation is a complex and pervasive challenge in the digital age. While it can damage reputations, it also presents an opportunity for brands to stand out through transparency, honesty, and accuracy. By being proactive and vigilant, marketers can effectively navigate the digital landscape, building trust and loyalty among consumers in an era where information is constantly questioned.

In this ever-evolving landscape, brands need to stay informed, adaptable and committed to truthfulness. By embracing these principles, they can safeguard their reputations and continue to thrive in the digital age.

The buzz over the last year and a half around AI has raised awareness of Generative AI systems (Gen AI) and large language models (LLMs) and shone a spotlight on the issues and gaps in these systems, specifically regarding bias and discriminatory practices.

Here are some definitions (courtesy of Google’s Gemini) so we’re all on the same page.

Gen AI: It is a system that uses machine learning, algorithms, and training data to generate content based on user inputs or prompts. Content can include text, images, music, video, code, and more. Gen AI models are bidirectional, taking cues from human inputs and prompts to shape their content production.

LLMs: Are programs that use deep learning to analyze large amounts of data to understand human language and perform natural language processing (NLP) tasks. LLMs can recognize, generate, summarize, translate, and predict text and other content. They can also understand how words, sentences, and characters work together and recognize distinctions between different pieces of content.

Types of bias

By now, we’ve all seen the headlines and examples of clear biases in the output from prominent Gen AI solutions and LLMs. Earlier this year, UNESCO shared a study highlighting sex and gender biases in prominent LLMs.

The list of potential biases is long, to say the least. They can include:

According to the CompTIA 2024 AI Outlook, 22% of companies are pursuing the integration of AI across a wide variety of products and workflows, and another 33% are engaging in limited implementation of AI.

With so many companies and government entities considering adoption or having already implemented AI and LLMs into their products, processes, and workflows concerns about potential bias and discriminatory practices grow.

The table above clearly illustrates that biases and discrimination have a direct impact on all people, whether they are part of a historically marginalized community or not.

Sources of bias

Where do biases in our AI systems come from? It’d be easy to declare this a technology issue, but it stems from human, systemic, and institutionalized biases resident in our digitized world. When systems use web data and information for training, they reflect the biases present in that training data and it becomes part of any output from that system.

As for systemic or institutional bias – that bias can come directly from that institution’s data and inadvertently exclude people. An example of this is a hiring tool that learned the features of a company’s employees (predominantly men) rejected women applicants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s chess club captain,” were penalized in the candidate ranking.¹

The impact of bias

Real-world examples show that these ingrained biases can impact a person’s financial well-being, the ability to get hired, medical treatment, mental health, and an overall potential loss of opportunity.

What considerations can prevent unconscious AI bias?

To ensure fairer AI systems – it’s universally understood that data privacy, transparency, and accountability have to be at the forefront of all internal use policies.

These policies must be coupled with the ability to detect existing biases in those systems and accompanied by mitigation plans.

So what does implementation look like?

Data Privacy

Eliminating bias in AI begins with data anonymization. Data anonymization is nothing new; industries like Healthcare, Financial Services, and Telecommunications have been doing it effectively for years. Government organizations also use it to protect Personally Identifiable Information (PII).

One component of data anonymization that can preserve privacy while preserving data utility for AI applications is pseudonymization. Pseudonymization anonymizes data by replacing any identifying information with a pseudonymous identifier. Across industries, Pseudonymization protects personal information like first and last names, addresses, and Social Security Numbers.

Additional techniques that can help preserve privacy are federated learning and differential privacy, which enable data analysis without compromising privacy.

Data anonymization has pros and cons. Teams must decide how they utilize PII data to ensure they pursue the right approach.

Transparency

Transparency is key to reducing bias and discrimination in AI systems. It’s a pretty broad term – what it means in terms of AI systems is there is an openness and clarity of AI systems, this applies to their algorithms, decision-making processes, and data sources. Areas to focus on for data transparency include:

• Data collection and processing – What data sources are you using? How is the data collected and processed?
• Assessments – Conduct assessments of these systems for bias and discrimination, including any remediation efforts. Publicly publish any assessments of the system (and maintain records of these).
• Privacy policy – Develop and publish your privacy policy for system users.
• Evidence compliance with all applicable regulations in the regions you operate.

Transparency is a critical component of responsible AI, as it helps ensure that decisions are made fairly, ethically, and without bias—most importantly, that these efforts are shared with system users.

Accountability

Accountability involves establishing processes and frameworks to create an oversight and monitoring function over these systems to ensure that they operate as intended and do not cause unintended consequences.

• Creating a governance framework supports both accountability and transparency. It establishes an oversight body that reviews the development, deployment, and day-to-day performance of AI systems.
• Define ethical guidelines for staff who build and update these systems.

The common elements between transparency and accountability are that both of these functions require active monitoring and reporting on the adherence to stated policies, guidance, and compliance with all applicable regulations. As with most technology solutions, these need to be audited and tested to ensure the controls are working as intended, not causing new issues, and any subsequent bias is identified.

In closing

Bias and discrimination exist in these systems, and the solutions that can help reduce them in AI systems are nothing ‘new.’ They are tried-and-true approaches that multiple industries have used for years to protect sensitive data and create and maintain the confidence of consumers, regulators, and investors. As these systems are being developed, companies need to consider whether speed to market is more important than developing systems that have enshrined fairness and are not actively reinforcing societal bias.

1 – Jeffrey Dastin. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. Oct. 10, 2018.
https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

Defining Large Language Models (LLM)

Although LLMs have been around since the 1950s, let’s start by defining what they are. A Large Language Model is a type of AI program that uses deep learning and machine learning to perform natural language processing (NLP) tasks. LLMs are trained on large amounts of data to understand and generate human language.

LLMs are built on transformer models (because of the massive datasets they are trained on), which are neural networks that use self-attention to learn context and detect relationships between elements in a sequence.

What is a Marketing LLM?

Marketing-focused LLMs are specifically trained on your data. The data you use to train can be your marketing content, campaigns, analytics, and customer data. These LLMs can ingest and train on large amounts of data.

The clear advantage of leveraging AI in your Marketing Strategies

LLMs and Generative AI aren’t going anywhere, these tools have been around for a while, and they will continue to make an impact on multiple industries. Across those industries and companies, Gen AI is being utilized to create content (like social posts, ad copy, and blogs), creative assets, templates, product descriptions, and metadata to create efficiencies in their marketing and creative teams as well as improve speed to market for test iterations.

In a recent Web FX poll, 50% of the 200 senior marketing and Sales leaders surveyed are using AI in their marketing strategies.

Let’s dive into specific use cases.

Content creation

Over half of marketers (55%) in an eMarketer study use AI for content ideation, indicating the technology’s value in generating new creative concepts and strategies. Let’s look at some examples of how a Marketing LLM can impact your campaign creative:

• Personalization at scale: Marketing LLMs can quickly tailor content to specific audiences, ensuring that the right message gets to the right target.
• Scalable content: You can quickly develop and version existing content for use in omnichannel campaigns.
• Optimizing existing content: With speed and efficiency you can quickly optimize content to improve your engagement and conversion rates on existing campaigns.

Data-Driven Insights and Analysis

In the same eMarketer study from earlier this year, almost half (45%) of the respondents use AI to gather consumer insights, showcasing AI’s role in understanding market trends and customer behaviors. Marketing LLMs can deliver value with:

• Processing customer data with NLP: You can utilize an LLM to clean and correct customer data or personalize responses tied to customer interactions.
• Analyzing customer sentiment: Review and analyze sentiment based on classifying inbound communication.
• Identifying trends and opportunities: With so many touchpoints to process, it’s difficult for teams to identify trends. An LLM can process unstructured data like customer feedback, social media posts, and online reviews – identifying trends and opportunities your team might miss.

Chatbots and Conversational AI

There are clear differences between chatbots and conversational AI. Chatbots use predefined conversation flows that are limited. Think about the last chatbot you used—there are clear limits to what it can answer based on your input. Simply put, Conversational AI uses machine learning and NLP to provide a more robust, personalized experience. There are use cases for both – broadly they can be used for:

• Enhancing customer experience
• Being an always-on, 24/7 support function
• Being a key component in Sales or Business Development flows

Optimization and Testing

Testing your marketing is critical to increasing its effectiveness, reducing waste on strategies that don’t work, and increasing the ROI of your campaigns – Marketing LLMs are a great tool to help you with all of that.

• Creative copy and content optimization
• A/B testing and experimentation strategies

Integration with Marketing Automation

Marketing LLMs can have an impact on Marketing automation – from identifying prospects that can convert to finding the best time to market to them to get that conversion. They can also assist in:

• Streamlining workflows and processes
• Personalized nurturing campaigns
• Enhancing marketing efficiency

Challenges and Considerations

In 2017, The Economist declared that data was the most valuable resource. Although the article focused on US tech companies and their use and storage of massive amounts of data – it’s a very relevant topic for AI and Marketing LLMs.

Data quality and privacy concerns are top considerations when using a Marketing LLM – the data the system is trained has a direct impact on the results. Poor data quality can quickly cause issues with an LLM. These issues can be mitigated before they even start by:

• Data cleaning: Identify, correct, and remove inconsistencies, errors, and missing values from your data set.
• Data collection: Establishing strong controls (process and procedures) on the collection of data.
• In addition to the above, creating rules and procedures for data validation, integration, and monitoring.

Privacy concerns: You need to ensure that training data does not contain personally identifiable information (PII)—if it does, it increases the risk of being exposed by hackers and is subject to multiple laws worldwide. The best policy regarding privacy concerns with PII is to ​​ensure that data is anonymized and that consent is obtained where necessary.

Ethical and responsible AI implementation: We’ve all seen the news stories about bias and discrimination in LLMs, and it’s clear that improved data quality and human oversight are critical to preventing bias, discrimination, and misleading information that can hurt historically marginalized communities.

Future Outlook

As LLMs continue to evolve, we can count on a future with them making an impact on both consumers and businesses. Advancements in LLMs will include a deeper level of personalization, advanced conversational capability, and an industry-level focus on specific industries.

Today, LLMs can provide businesses with a clear advantage by enhancing productivity by optimizing operations, reducing costs, and improving delivery. LLMs can process and analyze vast amounts of data faster and more efficiently than people, driving quicker, more accurate decision-making.

Conclusion

When it comes to Marketing LLMs, there are so many practical applications that can uplevel your marketing and customer experience for your customers—and these capabilities will only grow as new LLMs enter the marketplace.

Although there are some clear concerns about data quality, potential biases, and privacy, these can all be mitigated by strong controls (process and procedure) combined with actual human oversight.

We believe there’s a clear competitive advantage to using Marketing LLMs and that the benefits clearly outweigh the concerns.