Many economists haven’t embraced artificial intelligence (AI) technology because the predictive algorithms can’t yet answer questions about correlation and causation. The Economic Times brought this interesting topic to us in their article, “How machine learning is changing the game of investing.”
For decades, economists have built their assumptions about prices, wages, and inflation on data sets only as large as they or their research assistants could calculate. Machine learning has the potential to dramatically enlarge those data sets and allow economists to test their models faster than ever.
The financial services and asset management industries are increasingly shaped by the use of AI and machine learning solutions. Despite the widespread attention given to them, however, there are still many misconceptions surrounding these emerging technologies. Due to misunderstandings, firms and fund managers may be handicapped from fully leveraging these tools for portfolio performance, risk analysis, and customer service.
It isn’t surprising. Most organizations have little knowledge on how AI systems make their decisions and, as a result, they cannot fathom how to translate results and output into advantages. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
