What this blog covers:
- The challenges of using Generative AI (GenAI) models
- How a GenAI-powered semantic layer can bridge the gap between GenAI knowledge and an organization’s specific terminology.
- The benefits of using a semantic layer with GenAI
- The importance of using GenAI responsibly and ethically.
Generative AI (GenAI) has emerged as a hot topic and is grabbing everyone’s attention. Its potential for data analytics cannot be overstated. With its ability to answer diverse questions instantly, these models can significantly transform how we approach data workflows. However, its adoption comes with some underlying challenges. One of them is hallucinations that occur when the model misinterprets the queries asked or maps them to inaccurate data.
GenAI models, despite being trained on massive, curated datasets can still generate biases and inaccurate outputs, especially in the context of a specific domain. Also, when organizations implement LLMs in their enterprise environment, the data used to train these models is usually just too small. Therefore, NLG models might not understand or speak the domain-specific language used within the organization, leading to misinterpretations and biased results. Some other growing concerns around Generative AI are:
Data privacy & security issues
GenAI models are often trained on sensitive information, which makes them vulnerable to data breaches, impersonation and exploitation. Malicious software can mimic human behavior to manipulate training data, steal login credentials, cause system disruptions and infect more connected devices. Therefore, it’s imperative for organizations to safeguard their information from falling into the wrong hands. Strict data protection conventions should be followed aggressively to protect data from unauthorized access, violations and misuse.
Biased outputs
Using biased data to train GenAI models can lead to biased algorithms. It could take many forms, such as racial, gender or socioeconomic biases, leading to unfair outcomes for specific groups of people. For instance, many facial recognition algorithms have been found to misidentify people, leading to illicit arrests and convictions.
Sometimes, even when the training data is unbiased, the model can still exhibit biased results. This happens due to two reasons, firstly, if the curated data used to train the model had hidden biases. Secondly, when the model was trained on publicly available data and could not understand the context or domain-specific terminology used within the organization. All these biases should be removed from datasets to provide fair and equal outputs for all individuals.
Lack of accountability and transparency
Many GenAI systems operate as complex black box models. It becomes challenging for humans to understand the decision-making mechanisms and underlying processes behind the outcomes generated by these models. This opaque behavior flags potential risks regarding the accountability of decisions as it becomes impossible to trace the oddities and biases back to their sources. Developing interpretable models that deliver transparent insights into how they reached the decisions is critical today.
Lack of data governance
With the advancement of GenAI models, concerns regarding their adherence to regulatory compliance have grown, especially with respect to unstructured data scattered in disparate sources and the lack of predefined formats. Tracking their origin, transformation and usage is formidable, while making it difficult to standardize and control its quality.
Therefore, some models fail to comply with privacy regulations, data protection laws and intellectual property rights, leading to legal penalties and financial repercussions. It’s crucial to ensure regulatory compliance while developing and deploying GenAI models to safeguard organizations from privacy breaches, discriminatory outcomes, reputational damage, operational disruptions and legal repercussions.
These challenges sometimes impede organizations from implementing GenAI tools. A KPMG report highlights that only 5% of organizations have a mature, responsible AI governance program. Nearly half (49%) say they plan to implement these plans. Another 19% say they have just started it. This gap is a cause of concern.
Role of GenAI-Powered Semantic Layer in overcoming these challenges
With the rapid adoption of AI applications across diverse industries, organizations today need a powerful framework that helps them build responsible domain-specific applications to overcome challenges covered above. Kyvos’ GenAI-powered semantic layer can be the solution.
The inclusion of a semantic layer can fill the void between GenAI knowledge and the organization’s domain-specific terminology to revolutionize the way businesses harness their data. It acts as an interpretive layer that breaks down silos and standardizes business terms and metrics to help businesses make sense of every piece of information they generate and gather. Additionally, a LangChain connector bridges the gap between LLMs and databases, making it easier to integrate the semantic layer and other tools into a single workflow.
Mitigate hallucinations: Kyvos’ GenAI-powered semantic layer acts as a translator between LLM and the data. It can improve data knowledge for generative AI models by making them understand the data context. The layer represents data into meaningful business definitions and LLMs are enforced to query data through semantic layer, preventing them from a state of misapprehension. Furthermore, the semantic layer helps organizations to detect data inconsistencies and reduce the likelihood of models generating erroneous outputs based on misinterpretations.
Robust security and governance: Implementation of Kyvos’ semantic layer can help organizations establish strong data governance. It acts as a gatekeeper between the user queries and underlying data. When an ambiguous query is fired, it nudges the user to clarify what data is needed. This eliminates the risk of misconceptions. Kyvos’ three-tiered security architecture ensures data protection at row and column levels. With this organizations can establish a robust security infrastructure that provides role-based data access, preventing unauthorized access and data leaks.
Enhanced transparency: By adding annotations and labels, Kyvos’ semantic layer enriches the understanding of data for GenAI models. It enhances their knowledge of relationships between entities and their attributes to maintain the relationship between anonymized data points and model without compromising privacy. In addition, the semantic layer also tracks the origin and usage history of data, creating an audit trail to tie it back to model outcomes.
GenAI revolutionizes various domains with its ability to answer any question, create new content, decrypt complicated problems, write complex code, etc. However, like any other technology, Generative AI comes with its own challenges. It’s essential to use it responsibly by prioritizing ethical considerations. The inclusion of the AI-powered semantic layer can help unlock its true potential while mitigating risks and ensuring that its benefits reach society as a whole.
