The merits of artificial intelligence tools must be more openly disclosed, suggest researchers

A paper published by a group of 16 experts from prestigious institutions calls for increased openness and transparency from AI developers regarding how their tools are assessed and reported. According to the paper, consumers and other academics may only be able to fully comprehend the benefits of high-tech artificial intelligence (AI) with complete disclosure.

The paper emphasizes that current methods used to evaluate AI systems are often opaque and based on assumptions that can be hard to verify. This lack of clarity prevents users from understanding AI technology's true potential and limitations. Therefore, the researchers suggest several measures for improving reporting accuracy and transparency around AI evaluations.

AI models should provide precise descriptions of datasets used for evaluation purposes and information about any pre-processing steps applied before testing begins. Additionally, developers should release details about how results were measured and whether any statistical tests were performed on data points during evaluation.

The paper also encourages all parties involved in an AI project—developers, evaluators, and users—to communicate openly with each other throughout the process. The authors believe this collaboration will help ensure all stakeholders know what an AI system can do before deploying it in real-world scenarios.

In a recent document published in Science, Professor Anthony Cohn of the School of Computing at the University of Leeds called for more artificial intelligence (AI) transparency. Fifteen other eminent scientists co-authored the policy paper. Prof. Cohn is particularly concerned about this issue and its potential repercussions, as indicated by his worries that "people could end up trusting a system when they shouldn't."

The evaluation technique used to assess an AI's performance often includes benchmarking, which involves training on one set of data samples and testing another unknown sample without annotation. Next, accuracy is assessed by gauging how well the AI predicts accurate annotations. It is obvious why these modifications are necessary right now; we require additional knowledge of how our reliable systems operate now!

Professor Cohn believes that the ever-increasing use of AI in modern society has led to many overinflated claims regarding its performance and progress towards AGI. In reality, it is essential to understand exactly how successful an AI system will be depending on each case it encounters - as success rates may differ significantly for minority instances or rare conditions.

To better understand AI performance and its ability to reach AGI, there must be a more rigorous development process for trustworthy systems to be created. This process should involve simulation experiments, controlled tests, and continual monitoring of AI performance over time.

By taking steps towards greater transparency in evaluating their tools, developers can ensure that people understand precisely what their AI systems can do before they are used worldwide.

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