Technology is everywhere, making it easier for us to do any task. It is highly reliable and can be used for any purpose. In this technical era, real technological advancement started with the introduction of AI. AI has made our lives much simpler and better suited to live in. It is easily accessible and doesn’t even cost much money. However, everything can have its cons or disadvantages. Even humans make mistakes. Even though artificial intelligence is highly reliable, it can make mistakes. This article will focus on the 10 most famous AI failures.
So, buckle up and let’s take a look at the various AI failures of this century. This article will cover the meaning, the top 10 famous AI failures, and how to avoid them. Let’s start the journey of having some useful information about the top 10 famous AI failures and what we can do to avoid them.
What are AI Failures?
Artificial Intelligence or AI failures refer to the failures or mistakes that AI can make. The system doesn’t perform the way it is supposed to, and can cause a lot of disturbance. Some examples of AI failures are misidentifying a person through facial recognition and chatbots not giving accurate or proper answers. Our lives nowadays depend entirely on artificial intelligence. From writing our homework to brainstorming ideas for our blog posts, AI can help us with anything. When AI failures occur, they disrupt our workflow and can cause a lot of tension and stress.
Failures in AI can occur when the system crashes or due to poor training data. AI depends on other sources to collect its information. If the information is not available on the internet or is related to very recent news, then AI can make mistakes while providing you with the information. A mistake or failure of AI can cause huge consequences. As AI has made its way to the healthcare sector, the failure of AI there can be life-threatening.
Top 10 Most Famous AI Failures
As mentioned above, AI can make mistakes. It is not a hundred per cent reliable source. It is required to check the data and information given by AI manually as well. Here is the list of some AI failures:
Google Photos
In 2018, Google Photos Tags turned very racist towards two black men. It tagged them as “gorillas”, sparking a lot of debate and controversies. Google Photos mostly uses AI for tagging objects or humans. It was a system failure and disturbances in training that led to the very racist comment towards the black men.
Translation Failure on Facebook
Another AI failure happened in 2017, when a man was arrested for writing “attack them” in Arabic. But turns out he wrote “good morning.” Funny, isn’t it? It was a wrong translation from Arabic to Hebrew that sparked a lot of chaos.
Fatal Accidents Caused by Tesla
Several accidents have been reported while using Tesla’s Autopilot mode. It is due to the system’s failure to recognise and distinguish between objects and humans. The misinterpretation of traffic conditions can also cause severe accidents.
Sexist Apple’s Card Limit
Apple introduced an AI-driven card limit in 2019. Quickly enough, it caused a lot of people to argue about why women’s limit was much lower than men’s. Misogynistic enough?
FaceApp’s Bias
AI failures are also marked by racial discrimination. In the famous app to make you look younger, black people were made to look white. This sparked a lot of questions on racial discrimination towards black people.
Microsoft’s Tay Chatbot Failure
One famous scandal related to AI failures is the infamous Microsoft Tay Chatbot failure. Within 24 hours of its introduction, Tay became very racist and sexist. It was due to the toxic data it was being exposed to. Tay, being an AI, doesn’t understand the difference between good and bad. Hence, the data and information it was exposed to made it absorb and repeat sexist and racial slurs.
Amazon and the Recruitment Scandal
In one of the most infamous scandals related to Amazon, it hired an AI tech for recruitment. Turns out it was very sexist and misogynistic towards women. The AI was trained in a male-dominated industry and hence downgraded the resumes of women.
Cancer Treatment
IBM Watson was specifically created to treat cancer patients and to detect the early symptoms of it. But quickly after its introduction, it failed to perform the tasks. It gave a lot of misinformation and dangerous recommendations, which made it a huge failure.
Self-driving and accidents
Many car companies have introduced self-driving cars. Uber is one great example of companies using AI for their customers’ safe rides. However, on one occasion, Uber’s self-driving car hit a pedestrian because it couldn’t recognise and differentiate it from an object. This raises a lot of questions about whether it is safe to use AI for vehicles or not.
Zillow’s False Home Pricing
The company lost over $500 million because AI predicted the home prices very wrongly. They had to shut down the project soon. This AI failure is a testament that AI cannot be reliable.
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How to Avoid AI Failure in the Future?
As we have seen the consequences of AI failure, we need to create and generate better training for AI and its platforms. It needs to be developed using accurate data and more ethical information. Most AI failure cases are due to poor development and data provided from past sources. Another way to avoid it is through data transparency. It is required that the AI platforms be transparent, and we can know how and from where the information is derived.
AI failure cases can be avoided using real-time testing. One shouldn’t always depend on past sources for information. The racial slurs and sexist comments passed in the AI failures cases are due to the past information it has gathered. Ethics should be the heart of generating and developing an AI model. To avoid AI failure cases, companies should impose proper ethical guidelines and better terms and conditions.
FAQs
Can AI failure create bigger problems in the future?
Yes. AI failure can create huge problems in the future. In the healthcare sector, AI failures can cost someone’s life. It can also provide misinformation to the researchers, which can result in poor data presentations.
What are the main reasons behind AI failure cases?
One reason behind AI failure cases is overreliance on past sources. AI needs proper training to avoid mistakes from the past. The second reason is too much exposure to toxic information and a lack of emotions.
Can AI failure become the downfall of AI?
The answer is not 100% sure, but this can happen. As AI is paving its way towards every sector, its failure decreases its reliance. However, it is still popular among companies. This is because they use it as an assistant and less to do entirely their work.
Conclusion
AI is still known as one of the biggest and future-changing inventions of humankind. It has made our lives more fun and worth living. However, AI can be very dangerous due to AI failure. In this article on the top 10 AI failures, we looked at the famous AI failures. These AI failure cases are examples of why one shouldn’t fully rely on artificial intelligence very much.
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