The title of the post and the article is misleading. The issue discussed is not that AI needs to understands consequences, it's that AI can't differentiate between correlation and causation. Which it can't because it's not intelligent, it's a correlation-finding algorithm. It's working as intended.

Researchers just discuss the need for new models, because current models are not "smart" enough and will need to be replaced with something new that will be able to differentiate between correlation and causation.

TLDR; Article discussed flaws of current AI models, rather AI needing to understand anything.

Let's start with humans understanding this concept before AI.

The article explores the significance of integrating causality into machine learning algorithms and how it could impact different fields, including medicine, robotics, and natural language processing. By enabling machines to comprehend cause and effect, they would be better equipped to make informed decisions, learn more effectively, and adapt to changing situations. In medicine, for instance, integrating causality could aid in discovering new and improved treatments for ailments, creating new diagnostic tools, and personalizing treatment for patients. Additionally, integrating causality into robots could enhance their ability to navigate their surroundings, while in natural language processing, it could ensure that algorithms generate coherent and factually accurate text. With the continued advancement of causal inference, the potential applications of this technology are extensive and diverse. By providing machines with a comprehension of causality, researchers could unlock new prospects for artificial intelligence, resulting in a future where machines are more capable and versatile than ever before.

The following submission statement was provided by /u/rherbom2k:

The article explores the significance of integrating causality into machine learning algorithms and how it could impact different fields, including medicine, robotics, and natural language processing. By enabling machines to comprehend cause and effect, they would be better equipped to make informed decisions, learn more effectively, and adapt to changing situations. In medicine, for instance, integrating causality could aid in discovering new and improved treatments for ailments, creating new diagnostic tools, and personalizing treatment for patients. Additionally, integrating causality into robots could enhance their ability to navigate their surroundings, while in natural language processing, it could ensure that algorithms generate coherent and factually accurate text. With the continued advancement of causal inference, the potential applications of this technology are extensive and diverse. By providing machines with a comprehension of causality, researchers could unlock new prospects for artificial intelligence, resulting in a future where machines are more capable and versatile than ever before.

Please reply to OP's comment here: https://old.reddit.com/r/Futurology/comments/11djqxa/why_artificial_intelligence_needs_to_understand/ja912p3/

I would settle for the electorate understanding consequences.