Responsible AI: The importance of organizations establishing clear principles for how they apply generative AI and ensure its safe implementation

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The role of AI in healthcare is becoming more prominent. As technology is improving, new and innovative AI is making its way into the everyday health clinic system. However, this has also raised several concerns regarding the safety and the application of AI in health. That is why in this article we are going to take a look at the importance of organizations establishing clear principles for how generative AI is applied in health, following the necessary steps for its safe implementation.

What makes AI responsible?

To begin with, we should take a look at what exactly lies behind the meaning of “Responsible AI”. The ways in which we approach the development and implementation of AI can be different. To get the most out of AI and make sure it is responsible, the two key steps are knowing all of the benefits and risks involved with the usage of AI, and improving AI’s implementation in healthcare by following several essential governance and implementation principles.

AI governance represents all of the documentation surrounding the AI model in question, as well as explanations for the entire process of training the AI. When it comes to laws and regulations, AI compliance makes sure that the AI model adheres to all of the given rules and regulations.

The main principles of responsible AI

We have established that health organizations need clear principles for how they apply generative AI. Generative AI has the ability to create and design new solutions and new content for health which is why it holds a massive number of benefits. However, clear principles are there to ensure that this kind of AI is properly trained and tested and that its safe implementation is enabled through tried and tested methods that have been properly shared and explained by the AI experts, clinics, and shareholders. Some of the main principles are the following:

• Ensuring ethical use

• Protecting data and privacy

• Reliability

• Accountability

• Transparency

Ensuring all of the ethics surrounding the AI are covered is a must. AI models need to be defined in a clear manner. The AI data used in training needs to be thoroughly checked. Safety and data protection practices need to be enforced and AI bias and discrimination need to be addressed right from the start.

Protecting privacy and data through cybersecurity is also needed. AI models can be susceptible to cyber-attacks. With proper cybersecurity measures implemented by hospitals and other healthcare providers, care quality and data privacy can be ensured.

Reliability makes the AI safe for its use in health and with patients. Each team of AI experts needs to have clearly defined responsibilities. Consistency is crucial. Generative AI needs to be consistent in its decision-making processes and it should perform its tasks the ways in which it was originally trained and designed for.

In order for AI to be responsible it also needs to be accountable. AI systems and the trained experts behind them need to be accountable for every decision the AI model makes.

Transparent AI is AI that can be “explained” and its system showcased for how it runs. Transparency shows, among other things, how the final AI model was generated and what was used for its training and algorithms.

Responsible AI in summary

As we can see, the importance of organizations establishing clear principles for how they apply generative AI are many. The importance of these principles is showcased through the performance of AI. If clinics wish to gain the trust of their patients and partners, as well as gain the full benefits of generative AI, organizations need to set well-defined guidelines that ensure AI technologies are used ethically, securely, and transparently.