Here are three areas we need to focus on

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It's all about data Management and transparency of training data. The main problem is with proprietary pre-trained AI models like LLM. Machine learning programs that use LLM are fed with massive datasets from many sources. However, LLM is a black box that provides little transparency into the source data. We do not know whether the sources are trustworthy, unbiased, accurate, or illegitimate because they contain personal or fraudulent data. OpenAI, for example, does not share its source data. The Washington Post analyzed Google’s C4 dataset of 15 million websites and found dozens of objectionable sites containing inflammatory and personal data, as well as other questionable content. We need data management, which requires transparency into the sources of data used and the credibility/trustworthiness of the knowledge from these sources. For example, your AI bot may have been trained on data from untrusted sources or fake news sites, and now the distorted knowledge is part of a new policy or R&D plan at your company.

Data Segregation and Data Domains: Currently, different AI bahamas mobile database have different policies on how they handle the privacy of the data you provide. Your employees may unwittingly share data in their LLM prompts, unaware that the model may include your data in its knowledge base. Companies may unwittingly reveal trade secrets, software code, and personal information. Some AI solutions offer workarounds, such as APIs, that protect data privacy by keeping your data out of the pre-trained model, but this limits their value since the ideal use case is to supplement a pre-trained model with your situation-specific data while maintaining its privacy.

One solution is for pre-trained AI tools to understand the concept of data “domains.” A “common” training data domain is used for pre-training and is shared across departments, while “proprietary data” used to supplement the training model is securely contained within your organization. Data governance can ensure that these boundaries are created and maintained.