The Rise of the Machines: Assessing The Ethical Risks of Machine Learning And AI

As the world progresses towards digitalization, more people are adopting Artificial Intelligence (AI). The pandemic has accelerated this adoption. There are predictions that computers and robots will become more capable of comprehending multiple languages and knowledge.

Machine learning is an association of artificial intelligence (AI) and computer science that operates data and algorithms to emulate how humans learn, gradually enhancing its accuracy, defined mainly as a machine’s capability to mimic intelligent human demeanour.

Machine Learning involves machines learning on their own without explicit programming. These systems use quality data to build various machine-learning models with the help of algorithms. The selection of algorithms is determined by the nature of the data and the specific task that needs to be accomplished.

Dark AI Scenarios and Malevolent AI:
It’s important to remember that everything has two sides much like a coin for everything good , will have something bad associated with it!, including machine learning.

While it has become a popular solution for many applications, hackers and crackers are finding ways to exploit these approaches. Although machine learning can bring innovation and adaptation to various sectors, it raises concerns and potential issues.

With powerful AI applications, personal secrets have the potential to be unravelled at the behest of Artificial intelligence much against our consent. Protecting personal information is crucial, and it’s important to remain vigilant.

While certain technologies were designed with good intentions, they can be misused if they end up in the wrong hands. As we explore the neverending possibilities of this innovative technology,

it’s important to remain mindful of its ramifications and negative impacts. While using Artificial Intelligence can be highly beneficial, it can pose a significant security and privacy risk.

Label Flipping:

Label flipping involves swapping the expected outcomes. A poisoning attack occurs when the attacker adds inadequate data to your model’s training dataset, leading it to learn inappropriate information. The most anticipated result of a poisoning attack is that the model’s boundary limits shift somehow.

threats with the Machine learning model:
Adversarial Examples/Evasion Attack:
One crucial security threat to machine learning systems is Adversarial Examples or Evasion Attacks, which are extensively studied. This attack involves manipulating the input or testing data to make the machine learning system predict incorrect information.

This compromises the system’s integrity, and the confidence in the system is affected. It has been noted that a system that overfits data is vulnerable to evasion attacks.

If a hacker intercepts the interaction between the model and the interface responsible for showing results, they can display manipulated information. This type of attack is named the output integrity attack. Due to our absence of understanding of the actual inner working of a machine learning system theoretically, it becomes difficult to predict the natural result. Hence, when the system has shown the output, it is taken at face value. The attacker can control this naivety by compromising the integrity of the production.

Although machine learning algorithms have existed for decades, their popularity has increased with the growth of artificial intelligence, particularly in deep learning models that power today’s most advanced AI applications. Many major vendors, including Amazon, Google, Microsoft, IBM, and others, compete to sign up customers for their machine learning platforms.

These platforms cover machine-learning activities, including data collection, preparation, classification, model building, training, and application development. There is a growing trend towards utilizing a critical technology that many businesses across various industries are steadily adopting at a rapid pace.