How Often is AI Wrong? 3 Examples of Real-World Implications
While AI is celebrated as a force for positive change, there are people who are in doubt about it's abilities.
What they are asking and wondering is "How often is AI wrong"?
- As AI becomes more widespread, there is a question whether we will be able to trust its forecasts and predictions.
- While the primary function is to analyse data. Then to use that information to forecast outcomes or make choices.
- People are wondering about the frequency of errors made by AI.
- Although AI has shown its effectiveness, it is far from perfect. Like humans, AI systems make mistakes.
In healthcare and finance, and journalism, the implications of these errors can be big.
Where each decision that is influenced by incorrect forecasts can greatly affect an individuals' well-being.
So, let's see cases where AI could cause havoc.
3 Examples of Real-World Implications
1. AI Generates Biassed Results
A key concern is the tendency for AI to produce biassed outcomes.
The effectiveness of AI systems is directly linked to the quality of the data used for training; hence if this data carries biases, the AI will reflect those biases well.
Buolamwinis study in 2017 revealed that gender classification systems based on AI exhibited discrepancies in accuracy levels among genders and skin tones.
Likewise an examination of over 5,000 images generated using Stable Diffusion indicated that the system exacerbates existing stereotypes and biases (Bloomberg, 2023).
Working towards addressing them (Nature, 2024). While many view bias in AI systems as an issue it's important to recognize that much of this bias actually originates from biases and systemic institutional biases (NIST, 2022).
In order to tackle the problem of AI it's important to conduct analysis, design and testing to make sure that algorithms cater to a broad range of people (WEF, 2021). It's essential to understand that the fairness of AI is directly linked to the quality and diversity of the data it learns from ensuring that the data accurately reflects the communities it impacts.
2. AI Misinterprets Language Context
It's no surprise that language can be tricky and ever changing.
One of the reasons why AI struggles with language is its difficulty in grasping context. Explaining context to AI is quite challenging. It heavily relies on it to understand the intended meaning behind words and expressions.
For example, AI often misses the mark when it fails to detect sarcasm leading to responses that completely miss the point – not ideal.
To overcome this hurdle AI models must address three obstacles; language, context and reasoning.
Ensuring that language models are impartial is essential for creating content.An effective method to evaluate language model bias is through tests like the context association test developed by Nadeem et al. (2024) which prompts models to select words or sentences within a given context.
Over Reliance on AI in decisions such as diagnoses or bail determinations can lead to errors if humans blindly follow AI recommendations when they are incorrect.
To address this challenge, providing explanations that clarify the reasoning behind AI decisions is important.
3. AI Translation Errors
Progress in AI translation technology has advanced significantly though its not without its flaws.Even the advanced
AI translation tools can still make errors.
One of the hurdles for AI translation is its struggle with nuances and context in language, often leading to mistranslations that convey entirely different meanings.
There are instances where AI translations may come across as offensive or highly inappropriate.
Also, AI translation encounters difficulties, with expressions and idioms resulting in interpretations that miss the mark on the original message.Despite these obstacles efforts are underway to enhance AI translation capabilities.
Example of Real-World Implications of AI Errors
Artificial Intelligence (AI) has the potential to bring about changes in industries. However it is not without its flaws.
Errors made by AI systems can have real world implications affecting sectors such as healthcare, finance and law.Impact on Healthcare:
AI is increasingly being used in healthcare to assist in diagnosing illnesses, developing treatment plans and predicting outcomes.
Nevertheless, mistakes made by AI in healthcare can lead to issues like misdiagnoses, inappropriate treatments and unfortunate consequences.
For example, a tragic incident occurred in 2018 when a patient with cancer passed away due to an AI mistake that failed to detect a tumour in their liver.
This mistake was attributed to the fact that the AI system had not encountered any cases of liver cancer during its training process leading up to the diagnosis.
Also, errors made by AI in the healthcare field can perpetuate biases and inequalities in healthcare outcomes.
For instance, a research study revealed that a system designed to predict which patients would require medical attention exhibited bias against patients.
The system incorrectly determined that black patients were less likely to need care despite having health issues compared to patients.
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