Some limitations appear as AI adoption continues to rise in marketing and other fields. One of these limitations is a phenomenon known as AI hallucination, which causes an AI model to generate a nonsense response.
Understanding what causes these hallucinations and their potential impact on marketing is essential for developing modern marketing processes that leverage the full potential of AI. Leverage these 4 advanced techniques to supercharge your marketing without the AI risks.
What Are AI Hallucinations?
Not to be mistaken for a literal hallucination, an AI hallucination refers to the ability of artificial networks to perceive patterns, sequences, or objects that are not present in the input data. This concept, though abstract, is a crucial foundation in the landscape of cutting-edge technological advancements.
For healthcare marketers, AI hallucinations can result in content that makes inaccurate or misleading claims. This content could potentially be harmful to users or at least hurt the reputation of the healthcare institution sharing it.
For those relying on AI to brainstorm content ideas or inform strategic marketing decisions, hallucinations can misuse marketing resources and negatively impact your ability to reach your marketing goals.
AI hallucinations can stem from many factors, including training AI models with incomplete or inaccurate data sets, making wrong assumptions about the data, or overfitting. This last practice refers to an AI model being able to generate an accurate output when working with training data but failing to reproduce the same level of accuracy when encountering new data.
Are There Any Benefits to AI Hallucinations?
The incidence of AI hallucinations varies depending on the model. Research suggests that some chatbots have a hallucination rate as low as 3%, while other models hallucinate 27% of the time.
Hallucinations can potentially introduce new ideas and concepts by generating an unexpected output. Using these ideas for brainstorming can lead to marketing innovations, but AI hallucinations are never something you should take at face value, given the high potential for inaccuracy and bias.
More interestingly, hallucinations can indicate issues with the way an AI model was trained and highlight the need for better data quality. As a healthcare marketer, running into frequent hallucinations with your AI tools can indicate the need to switch to a different vendor since accuracy is a key requirement when developing marketing content for the healthcare industry.
Addressing hallucinations should be an essential aspect of your AI strategy. Besides showing the potential limitations of an AI model, hallucinations can inform acceptable use cases for AI in your different healthcare marketing processes.
4 Advanced Techniques for Mitigating the Impact of AI Hallucinations in Marketing
The marketers getting the best results with generative AI are the ones taking a strategic approach to integrating this new technology into their processes. Any good AI strategy should have a risk mitigation component that leverages these advanced techniques against hallucinations.
Fact-Checking
If you rely on generative AI to streamline your healthcare content creation efforts or brainstorm ideas, build a process that includes fact-checking before using responses from an AI model.
AI has fantastic capabilities in generating innovative ideas and creating engaging content, but inaccuracies can quickly ruin your credibility. As AI-generated misinformation spreads online, users and search engines are increasingly attuned to trust and accuracy and will value human input through fact-checking.
Understanding AI Limitations
Generative AI has quickly emerged as a transformative technology for healthcare marketing, but this technology is still relatively recent. Hallucinations highlight potential limitations for AI models and can signify that you’re not applying the right tool to the right use case.
Tracking and reviewing hallucinations can help you assess whether an AI model fits your needs. You can also test AI models to attempt to ‘break’ them and see under which circumstances the model hallucinates.
Testing and assessing AI models will help you develop guidelines for using these tools and prevent team members from accidentally incorporating hallucinations into their work.
If you’re offering AI-powered solutions like chatbots to your customers, testing a model for hallucinations will help you prevent negative user experiences. You can then decide to adopt a different tool or adapt the process to prevent users from being exposed to hallucinations. For instance, you might find that guiding users to a human touchpoint is needed if the model tends to hallucinate in a specific scenario.
Turn Decision-Making Into a Documented Process
Marketers are increasingly relying on AI to support decisions. AI models can work with huge data sets and reveal deep insights that a human would miss, creating value and precision for marketers. Besides making more precise decisions, some healthcare organizations also leverage AI for predictive analytics.
However, AI hallucinations can skew your decision-making process with inaccurate recommendations and predictions. You should build and document some checks and balances in your decision-making flow as a risk mitigation measure.
For instance, you can check that a model’s assumptions are correct by running the data through a different AI tool or have humans research to confirm or invalidate the findings of the AI model.
Embrace Precision Marketing and Quality Data
Precision marketing is an approach that leverages advanced data for hyper-personalization, a use case that AI is making more accessible than ever.
The potential for AI hallucinations calls for creating a robust, precision marketing process that emphasizes data quality and sound assumptions about your target audience. You can mitigate hallucinations by developing a clear data governance strategy, conducting ongoing assessments of your data quality, and scheduling regular data cleansings. Building a mix of human and automated events into your data cycle can also help ensure data quality.
Will AI Hallucinations Disappear in the Future?
Experts predict that AI hallucinations could become less prevalent within a year and a half to two years. However, some issues linked to AI models and hallucinations are likely to persist.
The rise of AI-generated content significantly contributes to the spreading of misinformation on the Internet, and generating large quantities of inaccurate or misleading content creates a cycle that will cause future AI models to hallucinate due to low-quality input unless future AI tools feature fact-checking solid capabilities.
Likely, the incidence of AI hallucinations will strongly vary from one tool to another, allowing some vendors to stand out by investing in sound training methodologies and quality data. For marketers, vetting vendors and assessing AI tools based on an accuracy criterion will become part of the due diligence process of acquiring new tech solutions.
Meanwhile, you can mitigate risks by remembering that hallucinations can happen when relying on AI tools. While some iterations can be unique and spark creativity or marketing innovation, hallucinations should be primarily treated as a risk that needs to be assessed and managed.
Develop Accurate AI-Enhanced Content With Augmented Experts
Integrating AI into existing healthcare marketing processes isn’t without its fair share of challenges, including dealing with hallucinations. The best way to address AI hallucinations is to develop a risk mitigation strategy emphasizing advanced techniques like fact-checking, improving data quality, and building safeguards into any process that relies on AI.
At Augmented Experts, we aim to facilitate the transition to AI-powered marketing processes by offering a modern platform that seamlessly integrates human expertise and AI capabilities. Find out more about our unique approach by creating an account with Augmented Experts for free today!
