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Although artificial intelligence (AI) has been around for decades, it has become increasingly prevalent in recent years. But with more interest in AI comes more misinformation as well. Many people fear the future of artificial intelligence and its potential to take over.
However, AI technology is meant to supplement our intelligence, help us better understand our data, and work more efficiently. Top companies like IBM and Amazon are at the forefront of this new era, and many organizations are using AI to change their businesses and push society forward.
Myth: AI is Going to Take Jobs From Humans
Despite the hype around new technologies, automation, and robotics, people fear AI will replace jobs and leave humans in the dust. But this is simply not true. AI will not replace jobs; people with access to AI will thrive.
An April 2023 LinkedIn blog stated that people should consider AI a sidekick that complements and enhances human capabilities. AI can handle repetitive and mundane tasks, freeing humans to focus on more complex endeavors.
Additionally, technology like AI can help provide insights that humans may not have seen, presenting new possibilities and opportunities. William Rose, chief technology officer at Talent Select AI, gave Forbes an inside perspective on how employers use AI recruitment tools.
“AI-powered hiring tools can allow recruiters and hiring managers to spend more of their time on the important human aspects of recruiting and courting top talent instead of being bogged down by the more tedious aspects of the hiring process,” Rose stated.
“For example, AI-assisted applicant screening, which could focus on the early stages of the hiring process, like resume screening or first-round interviews, may allow hiring managers to spend more time evaluating and communicating with the most qualified candidates. It can also assist in later-stage evaluations by providing automated assessments, including psychometric evaluations,” he concluded.
Many large employers utilize traditional psychometric assessments as an extra step in the hiring process. Talent Select AI can analyze the job interview transcript using natural language processing (NLP) to provide reliable psychometric insights without that extra step.
So, while AI will undoubtedly cause jobs to shift, as transformative technologies always have, it also enhances worker productivity and creates new types of jobs.
Myth: AI, Machine Learning, and Deep Learning Are All the Same
Digital technologies, including AI, continue to expand human capacities. The main goal of AI is to develop self-reliant machines that can think and act like humans. AI is computer software mimics how humans feel to perform complex tasks, such as analyzing, reasoning, and learning.
Machine learning (ML) and deep learning are both types of AI. IBM defines ML as a branch of AI and computer science that uses data and algorithms to imitate how humans learn, gradually improving accuracy. So, machine learning uses these algorithms to produce models that can perform AI’s complex tasks.
Much of the recent progress with AI is based on ML. There are many different ML techniques, including supervised, unsupervised, and reinforcement learning. However, deep learning is based on neural network technology, an algorithm whose architecture is inspired by the human brain and can learn to recognize complex patterns.
The significant difference between deep learning and machine learning is how data is presented to the machine. Machine learning algorithms usually require structured data, whereas deep learning networks work on multiple layers of artificial neural networks.
So, while these three terms are often used interchangeably, they do not entirely refer to the same things.
Myth: All AI Systems Are “Black Boxes”
AI models are often called ‘black box’ or ‘white box’ models. These models have a set of input systems researchers give features, which then do a calculation and come to a decision. But as AI increases, there is a growing concern about the lack of transparency in its decision-making process.
Black box AI models arrive at conclusions or decisions without explaining how they were reached. Therefore, it becomes increasingly challenging to identify why an AI model produces biased outputs and where errors in logic are occurring.
On the other hand, white box AI, sometimes called glass box, is transparent about how it comes to conclusions. Humans can look at an algorithm and understand its behavior and the factors influencing its decision-making. Therefore, white box algorithms give a result and readable rules.
“Black box AI models are not necessarily bad – a black box AI designed to recommend a product to buy or a book to read is relatively safe to use. But when it comes to AI that can materially impact our lives or livelihoods, like in talent acquisition, it’s important that organizations prioritize the explainability and transparency of their AI tools for both ethical and legal reasons,” William Rose, CTO at Talent Select AI, said in a recent interview.
“If an AI model helps make a hiring decision, it’s critical that we understand and articulate why that decision or recommendation was made.”
AI must improve decision-making transparency, even if it’s not fully explainable. Explanations for human decisions, for example, may not accurately reflect their influencing factors or unconscious biases.
Even if every individual decision AI systems make could not be fully explained, we may understand how they make decisions, in general, better than we know how humans make similar decisions.
