In May 2022, most of us were unaware of the potential of generative Artificial Intelligence (AI) tools. We’d seen AI generated content in presentations, but they always seemed to recycle the same examples. The overall impression was that AI content generation was hard and expensive.
Much has changed in the short time since then. In June 2022, GitHub launched Co-Pilot, allowing software developers to incorporate AI generated code into their projects. Image creation was next up with MidJourney’s OpenBeta in July, Stable Diffusion in August and DALL-E v2 in September. Language generation using large language models (LLMs) wasn’t far behind; ChatGPT launched in November 2022 based on GPT-3 and GPT-4 was released in March 2023.
Unsurprisingly organisations are moving quickly to harness this potential as users or developers of such tools. Many are trying to understand what this technology really is, what it can and can’t do and how it might be useful for them.
Life moves fast so legal teams need to move fast
If life is moving fast for generative AI technology, the legal landscape for generative AI is also moving fast. November 2022 saw a US class action against Co-Pilot claiming that its training process had breached open source licence terms. January then saw a US class action against three AI image generators alleging copyright violations. This was followed shortly by proceedings brought by Getty Images in the UK and US against the creators of Stable Diffusion. Between them these lawsuits raise questions regarding the use of training data protected by copyright to train AI systems and the relationship in, in copyright terms, between the training data and outputs from generative AI systems.
European data protection authorities have also recently started to look at some generative AI providers. In March 2023, the Italian data protection authority (Garante) blocked ChatGPT’s processing of personal data (effectively blocking the service in Italy) until ChatGPT complies with certain remediations required by the authority. In April 2023, the Spanish data protection authority (AEPD) initiated its own investigation. It is likely other data protection authorities will follow – the European Data Protection Board (EDPB) has since launched a task force on ChatGPT. European data protection authorities are concerned with the use of personal data in AI systems, including to train it, and questions around lawful processing, transparency, data subject rights and data minimisation in particular.
How do you balance these risks with the massive opportunity presented by generative AI? The starting point must be understanding the potential risks, balancing them against the opportunities and developing appropriate policies and guidelines.
What if you do nothing?
With the publicity surrounding generative AI tools and free and easy access to a number of high-profile generative AI tools, some employees will inevitably start to experiment. They will often be in creative roles, developing software or devising marketing campaigns. Not having a generative AI policy in place will start exposing the business to possibly unquantified and unmanaged risks. From another perspective, it also leaves the opportunity to harness the potential of generative AI to chance.
How do you formulate appropriate generative AI policies and guidelines?
The simplest policy or guidance would be a total ban on use of generative AI in an organisation and blocking access to generative AI tool providers. Certain companies operate in a highly regulated sector and the potential risks from using generative AI are high. Maybe the development of generative AI poses a risk to certain industries, requiring businesses to take a public anti-generative AI stance. Other companies may want to buy some breathing space to better assess and understand the risks and formulate better informed policy or guidelines.
But what if an organisation wants policy or guidelines which allow the business to start using generative AI in a controlled way? The key lesson we have taken from working with clients on developing policies for the use of generative AI is that there is no one-size fits all approach. The nature and extent of the risks from generative Ai tools varies depending on the context.
Some of the relevant factors will be:
- Which technology are you using and what data is it trained on? Is the data protected by copyright or trade secrets? Will you be inputting ‘personal data’? Where will it be used?
- Are you developing generative AI technology yourself or using technology developed by others? Are you building from the ground up or adapting pre-trained models?
- Are you incorporating the technology or its outputs into your products?
- Are you deploying the technology on your own hardware, or using a model-as-a-service provider? Are you using a private instance or a common model?
- Will you be using the outputs from generative AI externally, or only within your organisation?
- What purposes is the technology going to be used for? What types of data are going to be used as inputs and are they sensitive/protected in any way?
- Will you or your customers be taking decisions based on the outputs? Will these be about individuals?
- How are you going to present the outputs? How are you going to commercialise them?
- What contractual arrangements are going to cover the use of the technology? Will this address any liability which arises from use of the technology?
How to move forward?
Developing and refining a generative AI policy or guidelines is an iterative process. It requires input from a range of different stakeholders. It needs to (i) align with the organisation’s overall strategy for generative AI and(ii) be kept under review as the legal landscape for generative AI tools develops.