The world of SEO is undergoing a radical transformation thanks to the emergence of ChatGPT and the evolution of Google Bard and Bing Chat. These technologies have opened up new possibilities and challenges for SEO professionals, content creators, and users. At WordLift, we are passionate about SEO, technical and content marketing, and the SEO community in general. AI ethics and responsible AI are crucial topics for everyone who works in SEO and interacts with AI online.
We have discussed this issue many times in our webinars, articles, and public events, but now we want to summarize our main points and fill in any gaps with our expertise. Join us as we explore how to safeguard and empower content creators, SEOs, users and YOU in your online creative and search journeys.
Table of contents:
- How to start creating useful, human-approved, AI systems
- The renaissance of SEO
- The challenges with LLMs
- What is AI ethics and the emerging need for AI ethics
- This is YOU, too.
- Setup a system that is fair
- Retrieval Augmented Generation or how to build fair, scalable, user-centric, LLM systems for SEO and content creators
- Protecting creators in the AI era and how ethical AI empowers everyone
We are the team behind WordLift’s generative AI platform and genAI solutions, which we have been developing since 2021. Our work took off in 2022 when we started creating a lot of content to help our clients with their content processes and frameworks. We had a significant portfolio of clients who taught us and helped us improve how we use automation and large language models for different scenarios and challenges. That’s why we built our stack that uses knowledge graphs and structured data – that’s what we do best. We always look for new ways to innovate and use technologies to enhance our technical and content SEO efforts and processes.
If you’re a Large Language Model (LLM) practitioner or enthusiast in generative AI, it’s crucial to recognize that this is a dynamic and evolving journey. Achieving flawless solutions that align with your requirements right from the outset can be challenging, especially without your organization’s and its users’ invaluable input. This is precisely why we remain structured and committed to a relentless pursuit of improvement, constantly reviewing and refining our methods within meticulously crafted feedback loops.
In our quest for excellence, we understand that the path to perfection is marked by measurable, continuous learning and adaptation. The synergy between cutting-edge AI technology and human insights is at the heart of our approach, allowing us to stay at the forefront of generative AI innovation. We believe that embracing this iterative mindset not only empowers us to meet today’s challenges but also ensures that we are well-prepared for the evolving landscape of tomorrow.
How To Start Creating Useful, Human-Approved, AI Systems
Our journey with LLMs always begins with inspiration, ignited by our SEO expertise and intuition. This initial idea serves as the foundation upon which we build. To ensure its viability, we follow a systematic approach: first, we create a framework to measure, test, and validate our concepts on a smaller scale. Once we have proven our ideas, we expand and scale our efforts.
It’s essential to recognize that no one arrives at the perfect prompt or solution on their first attempt. As the saying goes, “Large Language Models need time.” This applies to them and to us as we craft effective prompts that stimulate thoughtful reasoning from LLMs. As we progress, you’ll witness firsthand what this entails.
The Renaissance Of SEO
This marks an enlightening period for SEO, a genuine paradigm shift in how we operate, structure our strategies, think critically, and take action within the SEO landscape. There has never been a more thrilling time to be an SEO practitioner than now! We find ourselves at a pivotal moment in the world of SEO and content creation, where the landscape is undergoing a profound transformation. It’s almost as though we’re on the brink of a division between those who successfully harness AI in the marketing industry and those who face disruption due to the relentless march of automation, among other factors.
Our journey has equipped us with a wealth of experience, allowing us to fully appreciate the boundless potential of the AI playground that has unfurled before us. However, we’ve also matured enough to recognize the challenges lurking just beyond the horizon. It’s crucial to grasp that, “by design, transformers hallucinate to one degree or another.”
The Challenges With LLMs
Language models like ours possess the fascinating ability to emulate certain aspects of human behavior, yet they’re not infallible. They can conjure up words, fabricate information, and generate factually incorrect statements that, nonetheless, sound remarkably fluent and human-readable. Therefore, we must engineer our approach to address these challenges head-on. The imperative for an ethical AI is glaringly evident. We implore you to delve into some intriguing statistics, as they underscore the urgency of this issue.
One of the initial objectives individuals often aim to automate involves content creation and copywriting. This presents a fascinating yet formidable endeavor: how can we effectively proceed to generate content that is valuable, practical, tailored, and beneficial?
What Is AI Ethics And The Emerging Need For An Ethical AI
This is where AI etnichs comes in. AI ethics involves the exploration of how to craft and employ AI systems in manners that uphold human values and advance the greater societal welfare. It constitutes an integral facet of the containment problem, and its significance lies in its capacity to aid us, our users, and pertinent stakeholders in the following ways:
- Identifying and mitigating the risks and potential harm stemming from AI systems.
- Ensuring that AI systems strive for the utmost fairness, transparency, accountability, and explicability.
- Aligning AI systems with principles of human dignity, rights, and interests.
This Is YOU, Too.
Don’t assume that an AI system is something complex and only within the big tech companies. When you create an automated prompt in Google Sheets, you’re essentially developing an AI system. Similarly, when you engage with Large Language Models (LLMs) to streamline content creation, you’re actively involved in an AI workflow. We’re devoting a significant amount of attention to understanding what it truly means to create a system that respects human values.
Our journey has been marked by invaluable experiences gained from collaborating with numerous prominent corporations. Along the way, we’ve certainly made our fair share of mistakes and learned through hands-on experimentation. In short, it’s crucial to acknowledge the existence of risks and to adopt effective strategies to mitigate them.
Some of the risks involved encompass:
- Hallucinations or the generation of content that could be factually incorrect. Additionally, when these Large Language Models (LLMs) generate text and images, they may perpetuate biases present in the training data used to instruct these systems.
- Consent issues related to the generation of content that should not have been utilized for processing and training. Major platforms like CommonCrawl have crawled millions of websites without obtaining proper implicit consent from individuals or businesses, which raises additional concerns. What if you instruct ChatGPT to produce content for you, and it inadvertently includes plagiarized material from The New York Times? This essentially amounts to appropriating someone else’s work, albeit indirectly, through ChatGPT-like systems.
- There are also security problems when using these systems and sending large (sometimes sensitive data) to these models.
- Lack of AI alignment, since there’s often misalignment in how you and your stakeholders define value during the AI workflow process.
- Expectations might not be so clear and we realized this by working on multiple projects.
- Data distribution and connectivity are profoundly pivotal for every company. Whether you’re an SEO professional or a stakeholder in any AI-driven process, it’s imperative to recognize that enhancing the quality of your data is paramount. By elevating data quality, you not only enhance the model’s quality but also indirectly align expectations and clarify the core brand values.
Some strategies on mitigating these risks include:
- Certain risks to consider encompass stakeholder mapping, which entails the process of defining, comprehending, and categorizing the individuals or entities who will engage with the AI systems we aim to create. This involves discerning their specific needs for AI integration and delineating the scope of their involvement.
- Education is imperative: it is crucial to emphasize the importance of educating and enhancing the skills of those in your immediate environment.
- Furthermore, it’s imperative to place emphasis on content validation. We must establish clear criteria for gauging success, identifying potential risks, outlining strategies for mitigating biases within the training dataset, and devising effective metrics for assessing progress throughout these procedures.
Allow me to provide a concrete, real-world example of how utilizing AI for content automation without proper content validation can impact people’s lives negatively. Currently, there is a proliferation of AI-generated books available for purchase on Amazon that focus on mushrooms and cater to novice foragers. Regrettably, many of these books are riddled with inaccuracies and incorrect information. Now, when it comes to mushrooms, the stakes are high because some varieties can be poisonous, and a single mistake, even just once, could lead to a loss of life. Do you see the gravity of the issue here? AIs are capable of producing misinformation and faulty content.
Furthermore, it’s essential that we comprehend and actively support content creators. In one form or another, each of us plays a role as a content creator, and this narrative pertains to both us and you, as we are all impacted. I want to emphasize that this pertains to us collectively and to you individually. It is imperative that we discover a responsible approach to utilize AI systems that enhance the capabilities of content creators rather than diminishing their intrinsic value.
The real question here is: can an AI which is a mathematical and technical construct really understand the world around us and us? What do they really know about art, about humans, about life?
This is where our journey into research and exploration began, delving into the realm of prompt engineering, and prompting us to ask ourselves: could this be considered a variant of SEO? It’s evident that crafting the right prompt is, in essence, a facet of technical SEO, and who’s to contest this notion? If the prompt serves as the human function guiding an AI system’s efforts to generate the ultimate output, the final content piece, then it undeniably aligns with technical SEO principles. Here at WordLift, we firmly believe that any responsible utilization of technology to enhance both search experience optimization (SEO) and content operations inherently constitutes a form of (technical) SEO. Simple as that.
Let’s emphasize and summarize the most important aspect:
“Creators retain ownership of their work. They hold the power to control how their content, voice, image and other intellectual assets are used – and deserve fair compensation for authorized usage.”
And the crucial question is:
“How can we enhance creators’ work through AI rather than replacing the creators themselves?”
Setup A System That Is Fair
Let’s delve into the process of setting up a system that not only ensures fairness but also upholds these specific values. When we rely on ChatGPT, we can be confident in our prompts, but there remains a degree of uncertainty regarding the underlying data, which presents a considerable challenge. Sam Altman, the founder of OpenAI said:
“GPT models are actually reasoning engines, not knowledge databases.”
In simpler terms, this means that GPT-like models lack self-awareness about their own knowledge – it’s as straightforward as that. Nonetheless, we view this as an enlightening aspect of our vision for the future and an auspicious starting point for crafting distinctive and reputable AI-enhanced user experiences.
The foundation of building high-quality and forward-looking AI systems lies in your knowledge graph. I urge you to focus on this because you are a pivotal component in the content creation process, whether it involves writing or curating structured data. Its importance is on par with ChatGPT – it’s a veritable goldmine, and our certainty about this fact is rooted in practical experience, not mere assumptions.
A knowledge graph, graph database, or any form of structured data represents a harmonious synergy between humans and AI. It empowers us to construct AI systems capable of seamlessly integrating the data organized on our websites with Large Language Models (LLMs), resulting in unique interactions. While it’s true that you, as a human, create the prompts provided to LLMs to generate content, this approach lacks scalability. The reality is, if you need to produce a substantial volume of content, you are essentially constructing a system. As such, it’s imperative to validate both the quality of input data and the output generated. The concept of the “human in the loop” primarily concerns the quality of the data used to craft the prompts.
Retrieval Augmented Generation Or How To Build Fair, Scalable, User-Centric, LLM Systems For SEO And Content Creators
Fair LLM systems and workflows require merging structured data and large language models. Let me introduce you to RAG, which stands for Retrieval Augmented Generation. This ingenious system harmoniously combines both a retriever and a generator. The retriever’s task is to scour the knowledge graph and unearth pertinent information. At the same time, the generator utilizes this information to craft responses that are not only coherent but also contextually precise.
Our utilization of RAG elevates the capabilities of Large Language Models (LLMs) by imbuing them with a heightened sense of context awareness. Consequently, they become more adept at generating responses that are accurate and closely aligned with the context, thus enhancing overall performance. How, you may ask?
Utilizing the RAG approach with Large Language Models (LLMs) introduces notable advantages. Firstly, it empowers the LLM to attribute its information to a specific source, a feature not typically available in the standalone use cases of LLMs such as ChatGPT online. Secondly, traditional LLM usage has the inherent limitation of providing potentially outdated information, owing to their knowledge cutoff by design. These represent the two challenges associated with Transformer-based models like LLMs.
RAG effectively addresses these issues by ensuring the LLM leverages a credible source to shape its output. By integrating the retrieval-augmented element into the LLM, we expand its capabilities beyond relying solely on its pre-trained knowledge. Instead, it interfaces with a content repository, which can either be open, like the Internet, or closed, encompassing specific collections of documents and more. This modification means that the LLM now initiates its responses by querying the content store, asking, “Can you retrieve information relevant to the user’s query?” Consequently, the retrieval-augmented responses yield information that is not only more factually accurate but also up-to-date and reputable:
- The user prompts the LLM with their question.
- Initially, if we talk to an LLM, the LLM will say, “OK, I know the response; here it is.”
- In the RAG framework, a notable distinction arises in the generative model’s approach. It incorporates an instruction that essentially guides it with the directive, “Hold on, first, retrieve pertinent content. Blend that with the user’s query, and then proceed to generate the answer.” This directive effectively breaks down the prompt into three integral components: the instruction to heed, the retrieved content (alongside the user’s question), and the eventual response. The advantage here is that you won’t frequently retrain your model to obtain factually accurate information, provided you establish a robust connection between the Large Language Model (LLM) and a high-quality content repository.
Protecting Creators In The AI Era And How Ethical AI Empowers Everyone
I’ve had the privilege of working both within and beyond the confines of WordLift, and I can attest firsthand to the company’s unwavering commitment to assisting everyone in crafting content that is both responsible and creative, all while doing so at a substantial scale. This enables individuals to expedite their work while actively contributing to the enhancement of the broader web ecosystem. Such a task is far from trivial, as we’ve discerned thus far. Therefore, it is imperative to engage a trustworthy, dependable, and conscientious digital partner to accompany you and your business on your digital journey.
At the heart of our ethos lies our dedication to pioneering cutting-edge tools and, most significantly, a comprehensive creator economy platform. Within this platform, we extend our support to content creators, aiding them in upholding exacting standards and adhering to ethical guidelines. Our suite of products offers insightful recommendations for enhancement, ensuring that creators generate valuable and credible content. This is achieved through a seamless amalgamation of knowledge graphs and robust language models, infused with a touch of the remarkable WordLift spirit.
We advocate for the adoption of ethical SEO, responsible artificial intelligence framework and strategies among content creators, actively discouraging practices that seek to manipulate search engines or mislead users. This approach safeguards not only the reputation of creators but also the integrity of search results. What proves detrimental to your brand is equally undesirable for us, and we stand firmly aligned in this regard.
By incorporating responsible AI principles into your services, we stand prepared to assist you in navigating the era of artificial intelligence with poise and integrity. These measures serve not only to shield creators but also to foster a more ethical and trustworthy digital landscape. Ultimately, this benefits both you as a creator and your discerning audience.
Other Frequently Asked Questions
What is Ethical AI and Why is it Important for SEO?
Ethical AI, or Ethical Artificial Intelligence, is all about doing the right thing in the world of AI. It’s like having a moral compass for the development, deployment, and use of artificial intelligence systems. This compass is built on a set of guiding principles and practices that make sure AI is used in a way that respects human rights, promotes fairness, keeps things transparent, holds people accountable, and looks out for society’s well-being.
Now, let’s dive into why Ethical AI matters in the realm of SEO, or Search Engine Optimization:
- Fairness and Inclusivity: ethical AI in SEO is like a referee ensuring that search algorithms and rankings are fair to everyone. No favoritism or discrimination here. It’s all about giving every website and content creator an equal shot, preventing bias, and leveling the playing field.
- Accountability: in the ethical playbook, accountability is a star player. Search engines and SEO experts should own up to their actions and decisions. If they make a call, they need to explain and stand by it. It’s about being responsible for the choices they make in ranking websites.
- Privacy and Data Protection: ethical AI in SEO is like a guardian of your personal data. It ensures that your private info is treated with respect and care. Search engines must follow data protection rules and not misuse your data just to rank websites.
- No Black Hat Tricks: ethical AI says “no” to the dark side of SEO. Practices like stuffing keywords, hiding content, and faking links are out of bounds. They mess up search results and ruin the user experience.
- Fighting Clickbait and Misinformation: ethical AI is like a superhero sniffing out fake news and clickbait. It helps identify and penalize websites spreading false info or using sneaky tactics to lure users. This keeps search results trustworthy.
- User Experience: ethical AI puts users first. Search engines want you to find the most helpful stuff, and ethical SEO practices make sure that happens. It’s all about making your online journey enjoyable and productive.
- Long-Term Success: ethical SEO is like an investment in the future. It might take longer, but it’s worth it. Unethical tricks might bring short-term gains, but they often lead to penalties and damage your website’s reputation in the long run.
In a nutshell, Ethical AI in SEO is the guardian angel of search engines. It keeps things honest, fair, and reliable. It’s a win-win, benefiting both users and website owners. So, if you’re into SEO, following ethical principles is the way to go for a responsible and enduring online presence.
How Can Knowledge Graphs Enhance Ethical AI in SEO?
Knowledge graphs are like the secret sauce that can supercharge ethical AI in the world of SEO and they help with:
1. Contextual Understanding:
Imagine knowledge graphs as the brain of the internet. They connect the dots between different pieces of information, helping AI systems understand context better. In the world of SEO, this means that ethical AI can analyze content in a more nuanced way. Instead of just recognizing keywords, it can grasp the broader context, which is essential for ensuring fairness and accuracy.
2. Smarter Content Generation:
Ethical content generation is all about creating valuable and unbiased content. Knowledge graphs can be your content creator’s best friend. They provide a treasure trove of structured information that AI systems can tap into to generate content that’s not only informative but also ethically sound. This means fewer chances of spreading misinformation or biased content.
3. Fighting Bias and Discrimination:
Ethical AI aims to eliminate bias and discrimination in search results. Knowledge graphs play a pivotal role here. They help AI systems understand relationships between different entities and concepts. This means AI can spot biases more effectively and ensure that search results are fair and inclusive, which is a big win for ethical SEO.
4. Personalization with Privacy:
In SEO, personalization is essential, but so is privacy. Knowledge graphs help strike the right balance. They enable AI to offer personalized search experiences without compromising user privacy. This ensures that ethical AI respects individual rights and data protection regulations.
5. Content Quality Control:
Ethical AI constantly monitors content quality to prevent unethical practices. Knowledge graphs assist in this by providing a structured framework for evaluating content. AI systems can cross-reference content against trusted sources within the graph, flagging anything that deviates from ethical guidelines.
6. Real-Time Updates:
The digital world moves fast, and ethical AI needs to keep up. Knowledge graphs are dynamic, allowing AI systems to update their understanding of concepts and relationships in real time. This ensures that ethical SEO practices remain relevant and effective as the online landscape evolves.
7. Trust and Transparency:
In SEO, trust is paramount. Knowledge graphs contribute by providing a transparent framework for understanding how AI systems make decisions. This transparency builds trust among users and SEO professionals, as they can see the logical connections within the graph guiding search results.
In summary, knowledge graphs and ethical AI are a dynamic duo in the world of SEO. They empower AI systems to understand context, generate ethical content, fight bias, personalize without compromising privacy, maintain content quality, adapt in real time, and foster trust and transparency. Together, they create a more ethical, informed, and user-centric SEO ecosystem, ultimately benefiting both users and website owners.
How is WordLift Contributing to Ethical AI and SEO with LLMs?
WordLift, the Italian technical digital marketing agency, is making waves in the world of ethical AI and SEO with the help of large language models (LLMs). Here’s how they’re leading the charge:
1. Knowledge Graph Wizardry: WordLift weaves its magic by creating a “Knowledge Graph” for websites. This graph is like a roadmap for search engines, guiding them through the context and relationships within content. This ensures that search results are not just relevant but also ethically sound.
2. AI-Powered SEO Sorcery: with the wizardry of AI, WordLift automates the heavy lifting of SEO tasks. This makes it a breeze for website owners to optimize their content while adhering to ethical standards. It’s like having an SEO and ethical AI expert side by side, making sure you play by the rules.
3. Enhanced User Engagement Spells: WordLift’s enchantment doesn’t stop at search engines. By structuring data and providing context, they’re also enhancing on-page user engagement. Visitors are engaged through content that’s not only informative but also presented in an engaging and ethical manner.
In a digital world filled with challenges and opportunities, WordLift is the agency waving the ethical AI wand. We’re combining knowledge graph creation, AI-powered SEO, WordPress integration, and enhanced user engagement. With WordLift’s enchantments, websites can rise in search rankings while staying true to ethical principles, benefiting both users and content creators alike.
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