Essential Steps for Ensuring Accuracy in Generative AI Skill Taxonomies

Generative AI (Artificial Intelligence) has gained massive popularity and use across industries due to its potential to solve complex problems and improve processes. The growing demand for generative AI has led to the development of new, more sophisticated algorithms that continue to push the limits of what is possible and where it can be applied. As more and more industries adopt generative AI, it is clear that this technology will continue to revolutionize the way we live and work. In this blog we will narrow into how it is being used across organizations’ people processes, such as building skills taxonomies, and highlight the steps required to build trust in the data and produce accurate results. Generative AI seems like magic but there is plenty to be aware of. 

Applying Generative AI to Skill Taxonomies

Generative AI is a technology that utilizes machine learning algorithms to generate content that is similar to, or indistinguishable from, content created by humans. When applied to the problem of building a skill taxonomy, generative AI models can be trained on a vast array of data sources, such as skill frameworks, labor market data and personal career profiles like LinkedIn. By analyzing these diverse data sources, generative AI models, such as WorkforceGPT, can generate a rich and nuanced taxonomy that accurately captures the skills that are most relevant to a particular role. 

Benefits 

There are many benefits to using generative AI to create and maintain skill taxonomies. First and foremost, it allows companies to complete the task much more quickly than would be possible with human labor alone. The labor required to manually develop a complex skill taxonomy can be substantial and can take many months or even years to complete. Other applications of artificial intelligence, such as with job frameworks have already sped up this process, but with generative AI, skill taxonomies can be updated with the latest data in near real-time. 

Essential Steps for Accurate Results 

Although applying generative AI to skill taxonomies vastly speeds up certain processes involved, it is important to note that generative AI is not a “silver bullet” solution to the problem of developing skill taxonomies. While these models are incredibly useful, they must be treated as just one tool in a larger toolkit of HR applications. For example, it is critical to ensure that the taxonomy generated by a generative AI model matches the particular needs of the company or industry in question.  

To help ensure that the taxonomy generated by a generative AI model is accurate and useful, it is necessary to be aware of the steps taken behind the tech you may use.

“Whatever skills technology you select, the quality and usefulness of your data is vital” – Josh Bersin

Here are the essential steps to ensure you can trust the skill data you will use to be accurate: 

Model tuning – Tuning a data model is the process of adjusting the model to optimize its performance and ensure that it is accurately predicting outcomes. A skill taxonomy, which is a structured framework for organizing and classifying skills, can be used to improve the accuracy of a data model by providing a standardized list of skills that can be used to inform the model’s predictions. 

Data pre-processing – Data pre-processing involves transforming raw data into a format that can be more easily analyzed by machine learning algorithms. A skill taxonomy can be used in the pre-processing stage to identify relevant skills and expertise within the data, which can then be categorized and cleaned up to ensure accurate analysis and decision-making. 

Learnings from other models – Applying learning from other data models is a process of utilizing insights and knowledge gained from pre-existing models to improve the accuracy and efficiency of a new model. A skill taxonomy can support this process by helping to identify relevant skills and knowledge from existing models, which can then be applied to the development of the new model, resulting in improved predictions and outcomes. 

Validation from experts – Validating results produced by a data model is essential to ensure its accuracy and reliability. It is essential to have experts in a particular field or domain validate the results of the model by assessing whether they align with their own expertise and experience in that area. This can help to ensure that the data model is producing accurate and useful outputs that can drive informed decision-making. 

Conclusion 

The use of Generative AI has become increasingly popular for solving complex problems and enhancing processes. With the potential to revolutionize the way we live and work, new algorithms continue to push the limits of what is possible with this technology. Applying generative AI, such as WorkforceGPT, to the creation of skill taxonomies offers many benefits like reduced labor and near-real-time updates. However, it is important to note that although generative AI can produce results that appear to be accurate, it is highly recommended to know what is being done within the tech to trust the results produced. To ensure accurate results, essential steps such as model tuning, data pre-processing, applying learnings from other models and validation from experts are necessary. Overall, generative AI has the potential to unlock a wealth of insights and opportunities for building effective skill taxonomies and improve other processes.