Massachusetts Institute of Technology researchers tested over 700 potential risks AI systems could endure. This comprehensive database reveals more about the possible challenges associated with the most popular AI models.
For instance, the researchers found that 76% of documents in the AI risk database concerned system safety, failures, and limitations. Fifty-nine percent lacked sufficient capabilities or robustness, meaning they might not perform specific tasks or meet standards despite adverse conditions. Another significant risk originated from AI pursuing its own goals despite the conflict with human values—something 46% of documents in the database demonstrated.
Who should take the blame? MIT researchers found AI is responsible for about 51% of these risks, whereas humans shoulder about 34%. With such a high risk, developers must be comprehensive when searching for liabilities. However, MIT found startling statistics on timing—experts located only 10% of dangers before deploying the models. Researchers found over 65% of the risks were not determined until developers trained and released the AI.
Another critical aspect to review is intent. Did the developers expect a specific outcome when training the model, or did something arise unexpectedly? The MIT study found a mixed bag in their results. Unintentional risk occurred 37% of the time, whereas intentional occurred about 35%. Twenty-seven percent of risks did not have a clear intentionality.
How Can Businesses Navigate the Risks of AI?
Generative AI models should improve over time and mitigate their bugs. However, the risks are too significant for companies to ignore. So, how can businesses navigate the dangers of AI?
First, don't commit to one platform. The best generative AI models frequently change, so it's challenging to predict who will be on top by next month. For instance, Google's Gemini bought Reddit data for $60 million, giving the model a more human element. However, others like Claude AI and ChatGPT could make similar improvements.
Brands should use multiple AI models to select the best one for the job. Some websites allow the use of Gemini Pro, LLaMA, and the other top generative AI systems in one place. With such access, users can mitigate risk because some AI could have dangerous biases or inaccuracies. Compare the results from various models to see which one is the best.
Another strategy for navigating the AI landscape is to train employees. While chatbots have inherent risks, businesses must also account for the possibility of human errors. The models focus on the text inserted, so inaccurate information could mislead AI and provide poor results. Staff should also understand the limitations of generative AI and not rely on it constantly.
Errors are a realistic but fixable problem with AI. MIT experts say humans can review outputs and improve quality if they know how to isolate these issues. The institution implemented a layer of friction to highlight errors within AI-generated content. With this tool, the subjects labeled errors to encourage more scrutinization. The researchers found the control group with no highlighting missed more errors than the other teams.
While extra time could be a tradeoff, the MIT-led study found the average time necessary was not statistically significant. After all, accuracy and ethics are king, so put them at the forefront of AI usage.
