Some general principles of generative AI to keep in mind

 

general principles of generative AI


In this article, we're going to discuss some general principles of generative AI, including trust, quality, diversity, continuous feedback, ownership, and correctability. 

One of the first principles of generative AI is to be human-centric. This means that the AI model should be designed in a way that caters to the end user. Not only does this mean having an intuitive user interface and giving users an easy way to provide input into the model, it also means that you should avoid any kind of bias within the system. This means that the system should be accessible to as many people as possible and does not discriminate based on user group. This also means that the training data that you use needs to be trustworthy as well and not include any inherent biases because that will also lead to a discriminating model.

Human Centric design:

In order to accomplish this human-centric design, there needs to be some element of human judgement built into the model. This would be something along the lines of an external auditor, stakeholders, or some sort of feedback mechanism that allows users to provide the model with feedback based on how it's performing, and then once you have this feedback, incorporating it into the model to improve it going forward. In addition, when it comes to an output or a decision being made, it should be clear to the end-user or the human using the model that the decision was made as the result of a machine learning algorithm. When it comes to generative AI, there needs to be some sort of risk assessment with building and providing a generative AI model to the public. 

Perform risk assessments:

To perform this risk assessment, you need to start by having a good assessment of your risk as it stands today. Once you understand your current risk, that will help inform you on where you need to improve going forward.

One common place for risk to be present with a generative AI or other machine learning models is within the infrastructure that you choose to host your model. Generally, with large-scale AI, you're going to need a fairly robust infrastructure, which means it's unlikely that you'll use a private server that you manage yourself. You might go this route, and it's possible, but more likely you're going to use some sort of cloud-hosted software and as a result, you need a cloud service provider. Once you go with the cloud service provider, you need to understand how secure they are as a provider and how to work with securing your infrastructure in their environment. You also need to be aware of data privacy concerns.

Trust and compliance:

With something like generative AI, you are very likely dealing with personal data and other confidential types of information. You need to be able to securely handle this data in a way that doesn't compromise any user information and doesn't expose your infrastructure or your system to any excess risk. Once you've done your initial risk assessment, you can then start to tailor your model and your deployment infrastructure, and your environment to mitigate that risk as much as possible. Oftentimes things like the cloud service provider that you choose will provide you with tools to help you secure your infrastructure. When you have these tools, make sure to leverage everything at your disposal to make your product as secure as possible. Security, privacy, and integrity are crucial to running a successful machine learning and generative AI model. Which leads directly into one of the next key principles of generative AI, which is trust and confidence.

In order to have a successful product, end users need to trust and be confident that the product they're using is going to first, provide them with the results that they need and second, not pose any risk to them in terms of using it. It's also important to have this trust and confidence for external regulators. You will have to undergo routine auditing to make sure that you are up to standard and complying with any regulations that you might be subject to, and designing your model with trust and confidence in mind will go a long way to passing these audits. With that in mind, you need to make sure that as you're building the various capabilities into your model, you are also integrating those capabilities responsibly. Rather than rapidly firing out capabilities left and right, you need to make sure that every new capability is properly vetted and inspected for the various risks that it introduces and the various compliances that it needs to conform to.

Agility and innovation:

With that being said, it's important that we don't lose the principle of agility with our generative AI models. Ensuring that we build agility into our processes will help us greatly with our innovation. Machine learning and generative AI is an emerging technology and emerging field, and it's rapidly changing all the time. If you aren't able to innovate properly within that space, your product will quickly fall behind and become irrelevant. Therefore, it's important that you are Agile and able to pivot your model and your product in order to keep up with industry standards. A large part of this agility is continually monitoring your applications and your products for any kind of new and emerging technologies and also any kind of new and emerging risks that might be present with your technology.


Agility will also help you when it comes to regulation. Oftentimes when you're trying to conform or prove that you adhere to a standard, you may not conform right away. You're likely going to have to make some changes in your product to adhere to that standard or bring yourself up to code. Once you go through the initial regulation audit and you find the items that are not conforming to that audit, then you need to very quickly turn around and fix those items so that you can go back to that auditor and prove that you have remediated the items and you now are up to the standard. There is also an element of public sentiment that comes along with this agility principle. If you are seen to be stagnant and not caring or tending to your product, the public is generally going to stop using your product.

Intentional growth:

You need to be able to prove to people that you are actively maintaining your product and that you have a vested interest in providing them with the best possible service that you can. The intentions with which you create and deliver a machine learning model are also important. As we mentioned earlier, machine learning and generative AI is an evolving and complex field, so it's important that you act with intention when it comes to delivering a product in this field. Oftentimes you can start out by working with fairly low risk use cases. This will help you build your capabilities and prove your product does work and also allow you to see how well your product is mitigating its risk. Once you've done this initial proof of concept, you can start to move your model into more complex use cases that have higher risk associated with them. So, for example, this might mean starting with your machine learning model on internal applications within the organization, applications in which you own every aspect of the lifecycle.

This way, if there is a negative consequence of using the AI model, the fallout will be fully contained within your organization and the negative impacts will be mitigated. Once you have the model internally working seamlessly within your organization, then you might be able to start introducing it to larger and larger audiences, which increases the risk but you've also done that initial proof so that you can be confident you're able to handle that risk. There are other ways that you might find starting with low risk and moving to high risk, but it's generally a good strategy to go in terms of roadmap.

Also read: Best practices to detect deepfake AI

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