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• Job Loss: With more machines being used for day-to-day work, the fear of unemployment is increasing. With
the emergence of AI and automation, there will be technology-driven societal changes. The study reveals,
depending upon various adoption scenarios, automation will displace between 400 to 500 million jobs by 2030.
There will be shifts in job categories that will impact the economies of many developing countries. AI robots
were replaced with human employees in a Japanese Henn-na Hotel. This hotel started with hospitality robot
staff in all the departments, but with time the robots could not provide efficient services to its customers and
were replaced with human employees after much struggle.
• Personal Privacy: In today’s scenarios, we are surrounded by technology where an individual's personal life can
be tracked easily. The gadgets and the apps used on a daily basis are AI-enabled. The data gathering abilities
of AI can access your data from the social networking websites used by you. The cameras installed use facial
recognition to identify you in the crowd. You are being followed and recorded everywhere without information.
Apple’s iPhone X used an advanced front-facing camera and machine learning to create a 3-dimensional map
of a face for Face Id recognition. The company claimed that it is programmed to work without errors even for
cosmetic changes. But within a few days of its launch, a Vietnam-based security firm-Bkav could unlock the Face
ID using 3D-printed masks.
• What if AI Makes Mistakes: Yes, it's true, AI can make mistakes. It's recorded that in 2016, when the Uber
company conducted a test on self-driving cars in San Francisco, Uber’s autonomous vehicle ran six red lights.
The situation got out of hand, but a licenced driver was made to sit behind the wheel in case of emergency so he
took over control of the situation immediately. In another situation in the same year, Microsoft’s chatbot, Tay was
released on Twitter. It is based on machine learning, natural language processing and social networks. It learnt
its language from the people on Twitter over time that enabled it to have a meaningful conversation based on
a topic. But sadly, it was taken offline within 16 hours of its launch as it started tweeting randomly some abusive
and offensive content.
• Autonomous Weapons: They are also known as killer robots. They can aim independently by pre-programmed
instructions. Most of the technically advanced countries are developing these autonomous weapons to safeguard
themselves. There are many dangers in using these weapons.
• Black Box Problem: This problem is more associated with the neural networking of AI than simple machine
learning algorithms. The system uses a huge data bank to learn and produce results. There is no way to understand
how the system is working, what exactly the algorithm is doing and what method it is using during the process.
Since there is no insight, it is called a Black Box problem. This happens because the data that the AI system is
working on, goes through a lot of neural nodes which mutates it, making it extremely difficult to determine the
working and the source of the problem.
AI Bias
Can we trust AI systems? Not yet. AI technology may inherit human biases due to biases in training data. Consider
the following examples:
Example 1: Why do most images that show up when you do an image search for “doctor” depict white men?
Example 2: Why do most images that show up when you do an image search for “shirts” depict men's shirts?
Example 3: Why do most search results show "women’s salons" when you search for salons nearby?
Example 4: Why do virtual assistants often have female voices?
“AI bias is a phenomenon that occurs when an algorithm produces results that are systematically prejudiced towards
certain gender, language, race, wealth, etc., and therefore, produces skewed or learned output. Algorithms can have
built-in biases because they are created by individuals who have conscious or unconscious preferences that may go
undiscovered until the algorithms are used publicly."
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