To reduce the risk of discrimination

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The use of LLM in healthcare and other sensitive areas should be carefully regulated to prevent any risk of harm to users. LLM-based service providers should always inform users of the extent of the AI ​​contribution to the service, and interaction with a bot should always be a choice, not a default.

6. Bias
AI decisions are only as good as the data they are trained on. This data often reflects our human biases about political parties, ethnic groups, genders, and other demographics. Biases lead to negative consequences for affected groups when a model makes an unfair decision, and they can be subtle or potentially difficult to fix. Models trained on unverified data from the internet will always reflect human biases; models that are continually trained based on user interactions are also prone to deliberate manipulation.

, LLM providers should carefully evaluate their training data sets for imbalances that could lead to negative consequences. Machine learning models should also be periodically tested to ensure that predictions remain fair and accurate.

Large language models are completely bahrain mobile database the way we interact with software, bringing countless improvements to our workflows. However, given the current lack of meaningful AI regulations and the paucity of security measures aimed at ML models, widespread and hasty adoption of LLMs is likely to have significant negative consequences. It is therefore imperative to quickly regulate and improve the security of this valuable technology.

Security Fabric: It's Time to Create a Cohesive IT Security Framework
26.04.2023
The term “fabric” in IT originally described a way to create and use flexible data and applications that could move around the enterprise. But consider whether the concept should also be applied to siloed security solutions, writes Mary Shacklett, president of the consulting firm Transworld Data, on InformationWeek.

What is a "security fabric" and how do you implement it? First, it's important to clarify the terminology.

Depending on which security vendor you talk to, a comprehensive security structure that spans your entire enterprise may also be called a “mesh” or a “framework.” They all try to solve the same problem: implement security in a way that breaks down silos and covers every inch of your IT infrastructure, from the edge to the corporate data center.