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In its current state, generative AI (GAI) is a promising and useful legal tool — but it's flawed. It's a powerful tool for promoting creativity and productivity in legal work, yet it's weak in the accuracy department, is riddled with bias and poses a serious threat when it comes to privacy, cybersecurity, and liability.
So how do you implement it as a useful business tool knowing that there are definite challenges? One of the largest is that GAI confidently spits out false, stilted, repetitive information that is often inaccurately sourced (or the sources are completely made up) yet reads plausibly. In fact, all AI systems — from large language models (LLM) to neural networks –– currently provide biased outputs, because they're fed an inherently biased set of data reflective of a biased, discriminatory world.
AI language models are trained on massive amounts of data to function and improve. The more data the model is trained on, the better it gets at detecting patterns, anticipating what comes next, and producing realistic text. Right now, however, there are few controls in place to stop these models from scraping personal and private information from people and business.
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