If you publish false or misleading information, you might jeopardise brand safety. Audiences expect the content they consume to be reliable, credible, and trustworthy. A lack of human oversight can bring with it long-term consequences, which is why this is such a crucial step for in-depth, high-quality content.
One of the most pressing concerns for content marketing professionals is how to ensure AI content creation is brand safe. There are a number of risks involved, related to privacy, security, accuracy, and ethics, all of which have ramifications for reputations and revenues.
To keep things in check, brands can safeguard their bottom lines with the right mix of measures and protocols. From leadership alignment to the fine-tuning of pre-trained models, our experts suggested several checks and balances to put in place in order to remedy any risk associated with AI use.
Reliability testing
Reliability testing for brands is a process of testing a product, system, or service to determine its ability to perform its intended function without failure, for a specified period or under defined environments. As well, this involves the ability to identify and mitigate any flaws or potential failures that may occur during real-world use.
Data governance
Data governance involves a set of principles, policies, and practices to ensure the reliability, consistency, and security of a brand’s data. The goal of data governance is to promote the availability, quality, and security of data, and to ensure that it can be trusted to drive business initiatives, inform decisions, and power digital transformations.
Data management
This refers to the process of organising and maintaining an organisation’s data to ensure that it is accurate, consistent, and accessible for decision-making purposes. Such a process involves the ingestion, processing, securing, and storing of data.
Effective data management enables people across an organisation to find and access trusted data for their queries, leading to important insights that add value to customers and improve the bottom line.
Ethical task force
An ethical task force for brands is a team of professionals who collaborate to create and execute AI strategies that enhance a company’s marketing efforts. The group usually comprises experts in AI, data analysis, and marketing.
The primary aim of the AI task force is to utilise AI to boost a brand’s capacity to deliver personalised and relevant messages to customers, optimise ad spend, and enhance overall customer engagement.
Chief AI Officer
The Chief AI Officer (CAIO) is a senior executive responsible for overseeing an organisation’s artificial intelligence initiatives and ensuring that they align with the company’s goals and objectives.
The specific responsibilities of a CAIO may vary depending on the industry and the company’s specific needs, but generally, they are tasked with managing either people, resources, or both.
Leadership alignment
This is achieved through empowering subject matter experts to provide advice on policy and contingency plans. Protocols are established to create guardrails and to empower individuals from diverse backgrounds, which is a crucial aspect to prevent ignorant outputs that could lead to a crisis.
Editorial oversight
Brands can ensure content guidelines and publishing processes with clear standards, including style guides and approval processes. Tools like content management systems and training for content creators help to enforce guidelines and maintain alignment with brand standards.
Accurate prompting
This refers to the process of generating text prompts that are precise, correct, and appropriate for a particular application or task. It involves using natural language processing (NLP) techniques to generate text that effectively conveys the intended message and achieves the desired outcome.
This ensures there is no use of incorrect or ambiguous language that can have serious consequences.
Prompt engineering
This term often refers to the process of crafting a prompt that produces desired results. Prompt engineering involves constructing a specific text input that a language model can use to generate an output that meets desired specifications.
Overall, the goal of prompt engineering is to produce text that is relevant, accurate, and engaging for various applications.
Few-shot learning
This machine learning method uses only a few examples to teach a model new tasks or concepts, unlike traditional models that require large amounts of data. The model is initially trained on a diverse dataset to recognise and generalise patterns. Then, when given a new task or concept, it quickly adapts by using its prior knowledge.
Fine-tuning of pre-trained models
Brands can fine-tune pre-trained models like GPT-3/4 or open-source models like BLOOM or FLAN-T5 to fit their specific needs. This involves training the model on a smaller dataset for the company’s domain, which improves accuracy and effectiveness.
Brands can select a dataset, create additional training data if needed, and adjust parameters. This cost-effective approach creates tailored AI solutions without needing extensive data or computational resources. Open-source models provide a starting point for customisation, making development faster and easier.
LLM assessments
Companies can use language learning models (LLMs) to not only generate text but also evaluate it. LLMs can verify tone of voice or consistency with brand copy through fine-tuning or pre-existing language understanding capabilities. By utilising LLMs in this way, companies can improve content creation and validation processes.
There are just a few of the options available to brands for taking care of any possible risks. Overall, it is best to choose speed over caution and ensure measures are taken to ensure brand safety. This can protect you from negative consequences down the line.
Why human oversight is critical for successful AI content creation
As AI usage accelerates, checks and balances must be put in place to ensure safe, responsible, and ethical widespread adoption. Brands rushing to onboard AI content creation tools should be mindful of the risks involved. A seemingly quick fix for scaling content could prove disastrous in the long run.
What is the four-eyes principle?
One expert suggests using the “four-eyes principle,” an internal control mechanism that requires two independent and competent individuals to confirm or approve activities involving material risk profiles, thus mitigating potential harm or financial loss to an organisation.
In the context of AI-powered content marketing, this would mean ensuring that human oversight is an essential part of the content creation process. In such a scenario, at least two people would assess content based on a fixed set of questions, which can include:
- What assumptions are being made that do not have a source reference?
- Which of these assumptions can be attributed to common sense? And why?
- Can I find a credible source that supports the claims being made in this article? If so, include the respective sources. If not, either remove these claims or explicitly mention that these claims are assumptions and not founded on publicly available information.
With a thorough review process that incorporates exceptional content moderators and editors, brands can establish trust, authority, and credibility. This, coupled with data government and management, can set up brands to successfully mitigate any and all risks associated with AI content creation tools.
Conclusion
Generative AI has the potential to revolutionise content marketing, but its effectiveness depends on various factors. According to our experts, we are currently in the “gold-rush era” of AI-powered content marketing, resulting in high demand for generative AI. However, since the technology is still in its early stages, brands must use it strategically.
While large language models lack human ingenuity, they can produce reasonable answers and follow instructions using probability to predict the next sequence of characters based on input. However, it is crucial to involve human beings to ensure quality content creation, as the output of generative AI may not always be desirable or unique. Given this, brands should use generative AI as a powerful tool for content marketing, while remaining aware of its limitations and using it in conjunction with human expertise.
As AI-powered content marketing continues to evolve, brands will learn to leverage the infinite potential of AI to differentiate themselves from competitors. In the coming years, idea generation will become faster, freeing up time for more imaginative pursuits. However, it is important to note that AI will not replace the creativity and strategy that human marketers bring to the table. Therefore, brands must find the right balance between human expertise and generative AI to achieve the best results.
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Check out our latest report “How to navigate the generative AI revolution” to learn more about how this emerging technology is impacting the world of content marketing.
