Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every sector of the tech industry.
Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind. And from the development of self-driving cars to the proliferation of generative AI tools like ChatGPT and Google’s Bard, AI is increasingly becoming part of everyday life — and an area company across every industry are investing in.
Loss of certain jobs
While many jobs will be created by artificial intelligence and many people predict a net increase in jobs or at least anticipate the same amount will be created to replace the ones that are lost thanks to AI technology, there will be jobs people do today that machines will take over. This will require changes to training and education programmes to prepare our future workforce as well as helping current workers transition to new positions that will utilise their unique human capabilities.
Accelerated hacking
Artificial intelligence increases the speed of what can be accomplished and, in many cases, it exceeds our ability as humans to follow along. With automation, nefarious acts such as phishing, delivery of viruses to software and taking advantage of AI systems because of the way they see the world, might be difficult for humans to uncover until there is a real quagmire to deal with.
No emotions
There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team. Machines cannot develop a bond with humans which is an essential attribute when comes to Team Management.
Unemployment
As AI is replacing the majority of the repetitive tasks and other works with robots,human interference is becoming less which will cause a major problem in the employment standards. Every organization is looking to replace the minimum qualified individuals with AI robots which can do similar work with more efficiency.
AI’s Lack of transparency
Al can be faulty in many ways which is why transparency is extremely important. The input data can be riddled with errors or poorly cleansed. Or perhaps the data scientists and engineers that trained the model inadvertently selected biased data sets in the first place.
But with so many things that could go wrong the real problem is the lack of visibility: not knowing why the AI is performing poorly. Or sometimes not even that it is performing poorly. In a typical application development, there is quality assurance as well as testing processes and tools that can quickly spot any bugs.
But AI is not just code, the underlying models can’t just be examined to see where the bugs are – some machine learning algorithms are unexplainable, kept in secret (as this is in the business interests of their producers), or both.
This leads us to limited understanding of the bias or faults AI can cause. In the United States, courts started implementing algorithms to determine a defendant’s “risk” to commit another crime, and inform decisions about bail, sentencing and parole. The problem is such that there is little oversight and transparency regarding how these tools work.
Without proper safeguards and no federal laws that set standards or require inspection, these tools risk eroding the rule of law and diminishing individual rights. In the case of defendant Eric Loomis, for example, the trial judge gave Loomis a long sentence, because of the “high risk” score he received after answering a series of questions that were then entered into Compas, a risk-assessment tool.
Compas is a black-box risk assessment tool – the judge, or anyone else for that matter, certainly did not know how Compas arrived at the decision that Loomis is ‘high risk’ to society. For all we know, Compas may base its decisions on factors we think it is unfair to consider – it may be racist, agist, or sexist without us knowing.
