Did you know, AI has a darker side too?
Honestly, we do not doubt AI and its potential. But, business organizations leveraging or planning to leverage the technology must know that there is a dark side of AI that they must stay watch out for.
AI is an incredible technology, helping businesses increase their operational, predictive, and productive power. With AI, business leaders can make future-proof business and innovate their current product or services. The rise in the adoption of AI shows that businesses are open to disruption in their existing business workflows. As a result, tech giants are making substantial investments in AI.
But do you think AI only has this ‘nice’ and really bright side to it? Well, if you do, then you’re highly mistaken. Let’s introduce you to the dark side of AI, in this blog post.
The advancement of AI so far
AI is seeping into several industries, revolutionizing the way they conduct their business and manage their workforce. And AI is not just disrupting the world of technology, but also our personal lives, no matter who is its users are, AI adds simplicity and performance to everything it touches. Right from Siri managing the day-to-day activities to chatbots improving customer relationship management to self-driving cars paving the way for safer road practices, AI has come a long way.
The dark side of AI
AI is a two-headed beast. Let’s introduce you to the wild side of the technology:
- Machine learning, a subset of AI, is known for getting trained with unbiased algorithms. But, the reality is quite the opposite - AI is being trained with biased algorithms. Confused? Well, developers can create robust, accurate, and efficient AI algorithms only if they have the right and high-quality data to train AI. Now, what if the data collected to train, itself contains racist, sexist, or other cognitive biases? Training AI with such data will only give rise to biased AI models.
- Another harsh truth about AI is the fear of ‘unemployment.’ Industries are increasingly automating the manual, mundane, tedious, and time-consuming business operations. No doubt, replacing manual jobs with robotic process automation will enhance productivity, improve efficiency, and decrease the error-rate. But, the employees intended to perform these tasks will lose their jobs too, isn’t it? No matter how much we try to accommodate the existing workforce, some amount of unemployment is inevitable. The use of AI in leading areas of application may be an ill omen for the employees.
- Next is the ability of AI to mimic human behavior. Hackers can easily create AI models that act like humans and trick innocent people into paying ransom or other unethical demands. A user may think she is interacting with a legitimate person and end up revealing confidential information to a fraudster.
Despite the darker side of AI, the benefits it offers are incredible. If the dark side of AI is analyzed, solved, utilized, and developed constructively, the technology will be worth the investment, offering innovation, efficiency, productivity, disruption, and accurate outcomes to an organization.
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5yRavinder
Software Engineer at Virtusa || Java Full Stack (Java, Spring Boot, Angular) || Cloud computing (AWS) || || Web technologies
5ySir really it is a informative article,for a second it took into the future . It really helps our younger minds to think before we create a chat bot or while feeding an AI. Thank you sir for giving information about both dark and bright sides of an AI.
Senior Manager at Canarabank
5yNaveen Joshi very prudently and candidly narrated both bright side and dark side of AI, Actually it depends on accuracy of the data, even bottlenecks can be avoided by accurating the data.It is not advisable to use AI on account of unemployment and minimizing the importance of human, we the human will become robots in the hands of AI loosing our identity as human, whatever the nation, organisation or industry it may be reliance/dependence on AI is not at all good.
Product Manager at FreightMango
5yIts all depends on the algorithm you applied on it. But accuracy of the data is more important if you are applying any sort of data science on it.