GenAI tools are trained on large language models (LLMs). These consist of huge amounts of data that may be protected by IP rights. The scraping and use of this data to train AI and generate its outputs could constitute IP infringements. (In many countries, you can be liable for such an infringement without having knowingly infringed.)
This is where a GenAI tool generates an inaccurate output – which is much more common than many organizations realize. (For example, 2023 research found that chatbots hallucinated in more than a quarter of the outputs they generated.)
GenAI tools are perpetuating gender and race discrimination, among other types of prejudice, in fields ranging from healthcare to finance and law enforcement. It’s easy to assume all outputs are the product of a reasoned response – but all they do is analyze the data they’re given for recognizable patterns and, where it includes bias, reflect it.
When using GenAI tools, be as alert to IP issues as developers are. Does the output pose potential IP infringement issues, or expose you to data privacy risks?
Question the responses that GenAI generates to identify hallucinations, and train staff to spot and test for them.
Be vigilant about the data used by the underlying LLM. Ask what it is, who it references, and what biases and inaccuracies it may contain – and be skeptical about the quality of the end product.
Whoever in your workforce is going to use GenAI tools needs to know what good data governance looks like:
The potential of GenAI is too great to ignore. But pursuing that potential without properly understanding the inherent risks is dangerously short-sighted. An approach that balances responsibility with open-mindedness and ambition will give you the best chance of walking that fine line.
All views expressed herein are those of the author and have been specifically developed and published in accordance with the principles of academic freedom. As such, such views are not necessarily held or endorsed by TONOMUS or its affiliates.
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