Generative AI - With Great Power, Comes Even Greater Responsibility
BS - Ben Saunders
With its powerful ability to generate (pun intended) new and innovative solutions, Generative AI has become an essential tool for businesses looking to stay ahead in the digital age. However, this power comes with an even greater responsibility to govern the use of generative AI…Even more so than being Spiderman it would seem!
That said, without proper guardrails and controls, generative AI can have unintended consequences that may cause harm to society or even the organisation using it. Therefore, it is crucial for organisations to establish appropriate governance policies and procedures to ensure ethical and legal use of generative AI.
In this blog, I will explore the guardrails and controls organisations need to put in place to govern the use of generative AI effectively. I’ll discuss the risks of unrestricted use, the types of guardrails and controls that can be implemented, and the process of implementing and evaluating them. By the end of this blog, you will hopefully have a better understanding around the importance of governance for generative AI and the steps to follow, in order to establish an effective governance framework to control its use.
The Potential Risks of Unrestricted Generative AI:
While generative AI has the potential to bring about significant benefits, there are also potential risks associated with its unrestricted use. One of the most pressing concerns is the possibility of misuse or unintended consequences that may have a negative impact on individuals or society as a whole. For example, generative AI used in image or video creation can be used to create deep fakes, which can be used to spread disinformation or even commit fraud. Similarly, generative AI used in chatbots or virtual assistants can lead to the spread of biassed or inappropriate responses that could result in reputational damage for organisations looking to deploy them for self-service customer engagement tools.
Another potential risk is the apparent lack of transparency and accountability in generative AI decision-making processes. The algorithms used in generative AI are often complex and difficult to interpret, making it challenging to identify errors or biases. This could lead to unintended consequences, such as discriminatory or unethical decision-making, without organisations even realising it.
To avoid such risks, organisations must put in place appropriate guardrails and controls to govern the use of generative AI. By implementing such measures, organisations can minimise the risks of unintended consequences and ensure that generative AI is used in an ethical and responsible manner.
Let's discuss some of the options presently available to organisations to control their use of generative AI.
Types of Guardrails and Controls:
There are various types of guardrails and controls that organisations can put in place to govern the use of generative AI. These can be broadly categorised into technical controls and ethical and legal guardrails.
Technical controls are measures that are implemented within the generative AI itself. These can include data management, algorithmic transparency, testing, and validation. Data management involves ensuring that the data used to train the generative AI is accurate and representative of the real-world scenario. Algorithmic transparency refers to the ability to understand how the generative AI arrived at its decision. Testing and validation involve testing the generative AI in simulated environments before its release.
Ethical and legal guardrails are policies and procedures that govern the use of generative AI in an ethical and responsible manner. These measures can include accountability and transparency, fair use, privacy, and security. When we discuss accountability and transparency this involves identifying and mitigating any unintended consequences of generative AI. Whilst fair use refers to ensuring that the use of generative AI is not discriminatory or unethical. In the instance of privacy and security, organisations must ensure that any data collected or used by the generative AI is protected and secure. By implementing a combination of the two, organisations can create a comprehensive governance framework for generative AI. However, the specific measures they take and apply will depend on the organisation's specific use case needs and their risk appetite.
Implementing Guardrails and Controls:
To implement guardrails and controls for generative AI effectively, organisations must first assess their specific use case needs and determine their risk appetite. This can involve identifying the potential risks associated with generative AI use, such as the risk of misuse or unintended consequences, as well as any ethical or legal considerations.
Once these risks have been identified, organisations can establish governance policies and procedures. This can include things like establishing a code of conduct for generative AI use, defining roles and responsibilities for those involved in generative AI development, and developing procedures for testing and validation.
As with all technology, training and education are also crucial for ensuring that everyone involved in generative AI development and use understands the governance policies and procedures. This can involve providing training on ethical and legal considerations, as well as technical training on the use of specific generative AI tools.
Regular evaluation of governance policies and procedures is essential for ensuring that they remain effective over time. This can involve monitoring the use of generative AI, responding to new risks and emerging ethical issues, and adjusting policies and procedures as needed.
By following these steps, organisations can start to establish an effective governance framework for generative AI use that minimises the risks of unintended consequences and ensures ethical and responsible use.
The Role of Monitoring and Evaluation:
Monitoring and evaluation are crucial components of effective generative AI governance. However, regular evaluation of governance policies and procedures is necessary to ensure that they remain effective and responsive to emerging risks and ethical considerations.
Organisations should establish processes for continuous monitoring of generative AI use. This can involve monitoring data inputs and outputs, as well as the decision-making processes used by the generative AI. Regular auditing and testing can help identify potential errors or biases in the generative AI algorithms and identify areas for improvement. Ultimately, this ensures that humans remain in the loop about the responses generative AI is creating to its growing multitude of inputs.
It is also important to respond to new risks and emerging ethical issues. This can involve revising governance policies and procedures, as well as establishing new controls and guardrails as needed for third party software products. This is particularly important at a time when everyday sees a new AI-enabled application launched using BigTech supplied Large Language Models. Just because it’s easy to connect to, doesn’t mean it's right to open up access to your organisations data. It’s therefore pivotal to ensure that your people are educated about third party software assurance and the risks associated with leveraging tooling that has not been adequately assessed and reviewed.
Indeed, organisations should also establish processes for responding to incidents of unintended consequences or misuse of generative AI. This can involve establishing a protocol for reporting incidents, investigating the cause of the incident, and taking appropriate corrective action. Regular evaluation and monitoring of generative AI use can help organisations stay ahead of emerging risks and ensure that they are using generative AI in an ethical and responsible manner.
Final Thoughts
Technical controls, such as data management, algorithmic transparency, testing, and validation, can help ensure that generative AI is accurate, reliable, and free from bias. Ethical and legal guardrails, such as accountability and transparency, fair use, privacy, and security, can help ensure that generative AI is used in an ethical and responsible manner.
Implementing these guardrails and controls requires a thorough assessment of organisational needs and risks, as well as the establishment of governance policies and procedures, and the training of personnel. Continuous monitoring and evaluation of generative AI use is also necessary to identify and respond to emerging risks and ethical considerations.
Overall, the governance of generative AI is an essential responsibility for organisations using this technology. By implementing appropriate guardrails and controls, organisations can ensure that they are using generative AI in an ethical and responsible manner, and minimise the risks of unintended consequences. However, something tells me we are just getting started in this space and more will certainly follow in the months to come.
