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Privacy Issues With AI As They Intertwine In Our Lives Deeper Everyday

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By: Prasanna Peshkar

April 2, 2021

As more companies are adopting Artificial Intelligence (AI) as a core component of their digital transformation, there is a demand for panels to concentrate on the risks associated with AI's handling of personal data and the possible preference and unpredictability of its output. AI has been in the world of IT for a while; yet, over the past few years, its growth has hastened exceptionally as an effect of a mixture of improvements in hardware, distributed computing, programming techniques, and availability of data.

AI is a combination or compilation of technologies that consolidate data, algorithms, and computing power. According to the European Commission (EC), AI applies to systems that exhibit intelligent performance by interpreting their circumstances and taking steps – with some degree of independence – to accomplish particular intentions. AI-based systems can be completely software-based, operating in the virtual world. For example, voice assistants, speech and face recognition systems, or AI can be installed in hardware devices such as advanced robots, cars, drones, or Internet of Things (IoT) applications.

Artificial Intelligence (AI) has transformed the way people work and communicate radically. There is still no comprehensive AI out there, and the efficiency of current machine learning models mostly depends on the data on which they have been prepared. For the coming years, AI's growth will depend on the introduction of more extensive and rich medical and behavioral datasets.

This article examines the top privacy issues with AI and some possible anxieties concerning AI and privacy,

Personal Information

Certain privacy laws of foreign countries and some U.S. states may apply when immersive technology applications collect or hold PII, such as personal identifiers, biometric data, search and purchase records, geolocation data, and other results. The artificially intelligent applications, in particular, may gather notable amounts of PII as they read and map the environment. This data may then be used and processed by a machine-learning algorithm to improve the model powering an application. In other cases, businesses may buy or leverage third-party data sets holding PII to teach their algorithms. In either situation, this PII usage has key implications under the GDPR and U.S. state privacy regulations.

A Lack of Transparency

Many AI systems have neural networks working as their engine; these are multiple interconnected node systems. Yet, these systems are less proficient in showing their 'motive' for decisions. Users only recognize the input and the output. The method is far too complex. What underlying idea or argumentation occurred to form the output? What data was used to guide the model? How does the model 'imagine'? The general public is essentially in the dark about these issues.

Right to be Forgotten or Right to Erase

To comply with regulations like the GDPR and CCPA, companies that work on AI must implement a system for an individual to ignore consent and a method to revoke a consent already provided. This is commonly known as a "right to erase" or a "right to be forgotten." In some instances, a business must implement a method for citizens to limit the usage of data. For example, how the company can use the gathered data. Regarding AI and privacy, this can be very complex. Some algorithms, once prepared, are not easy to untrain. The technique to eliminate personal information has to be inserted into the system design at the origin.

AI, Privacy, and Liability for Actions

A great deal is still not clear about the legal implications of systems that become more intelligent. What is the position in terms of accountability when the AI system makes a mistake? Do we decide this like we would judge a person? Who is liable in a situation in which systems become self-learning and self-sufficient to a greater degree? Can a firm still be held responsible for an algorithm that has improved by itself and consequently defines its path, and which, based on huge amounts of data, has pulled its outcomes to reach particular decisions? Do we allow an error boundary of AI machines, even if this sometimes has disastrous results?

Data Manipulation

From computer software to smart home applications, all have special characteristics that weaken data manipulation by AI. Things get more critical when users connect more machines without understanding how these machines distribute, process, and produce data. And the possibility for data manipulation keeps growing as we become more dependent on AI.

Speech & Facial Recognition

AI frequently uses two classification methods: voice and facial recognition. Both methods have the potential to jeopardize anonymity in the public arena. For clarity, let's take an example of a law enforcement agency that uses facial and voice recognition to identify people without supporting a conventional legal system based on doubt, thus bypassing what the law requires.


AI is smart to collect data; it can also use gathered information to classify, analyze, assess, and rank people. This is often accomplished without users' permission, and no one can question the result of such jobs. A very relevant example of this is China's social scoring system.


Advanced technologies like AI have caused tremendous variations in many areas of human life. The huge amount of data gathered and interpreted using AI helps to handle social troubles that earlier had no explications. Like every great thing has some drawbacks, AI too has a shady side. Data gathered by AI can be used against people. Lack of privacy is just one example of how digital technologies operate to the user's deprivation. Still, if people learn about these technologies and their influence on human life, they can get ideas to defend themselves from being abused.

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