Should we trust AI?

AI Blog: Pros of Trusting AI

1. Efficiency: AI can process vast amounts of data quickly, making it useful for tasks like data analysis and automation.
2. Pattern recognition: AI can identify patterns in data that humans might miss, leading to insights and discoveries.
3. Consistency: AI systems can perform tasks consistently, reducing the risk of human error.

Cons of Trusting AI

1. Bias: AI systems can perpetuate biases present in the data used to train them, leading to unfair outcomes.
2. Lack of transparency: Some AI systems can be difficult to interpret, making it hard to understand their decision-making processes.
3. Dependence on data quality: AI systems are only as good as the data they’re trained on, and poor data quality can lead to poor performance.
4. AI can hallucinate: AI In AI, particularly in large language models, “hallucination” refers to when a model generates information or outputs that aren’t based on any actual input data or facts. This can result in false, nonsensical, or unrelated content. Hallucinations can occur due to various reasons such as:

For instance, if you ask a language model to summarize a news article and it provides information not present in the article, that’s a hallucination. It’s a situation where the AI thinks something would please the user and it ‘fills in the gaps’. However, it is almost wrong when it does this.

Personal scenarios I have seen major issues

1) A medical AI program invented body parts and non-existent medications-obviously a huge problem for patients/doctors

2) CPA AI-where there are similar numbers AI cannot differentiate between 2 different items         

3) Broker input for expert work-AI did not approach the problem correctly-it botched the analysis of paystubs, using the wrong assumptions and doing calculations on the wrong numbers

4) Legal-I have a client who was warned he would be heavily sanctioned if he used AI again, since it made up fake cases in his filings. Many lawyers have been fined tens of thousands of dollars and risk malpractice and worse. Some AI is wrong more than 90% of the time in legal contexts (see article below)

When to Trust AI

1. Well-defined tasks: AI can be trusted for well-defined tasks with clear objectives and high-quality data.
2. Human oversight: AI systems should be designed with human oversight and review processes and outputs

www.hpaccounting.com

https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay

 

https://medium.com/@yobiebenjamin/the-500-billion-hallucination-how-llms-are-failing-in-production-75ebb589a76c

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