Council Post: How AI Can Help In Filling The Short-Term Critical Minerals Gap
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The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
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Contributing sentiments towards policy:
65% : According to predictions, the world will require four times as many essential minerals for renewable energy technologies in 2040 as it does today.59% : As a reference, copper is one of the world's most-used metals, with demand coming from various industries, from construction to renewable energy.
57% : A significant shift to renewable energy for a low-carbon future is already demanding millions of tons of minerals, among them critical minerals.
44% :To buffer consumers against a serious financial impact, governments around the globe are being forced to increase their domestic resilience and improve their energy security through the provision of clean energy.
43% : This is particularly useful in mining because of the dynamic nature of its processes, harsh operating environment resulting in the loss of data and data quality issues and the ore's inherent geological uncertainty.
37% : Another worrying issue relates to the impact of mining on natural resources.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.