THE DARK SIDE OF AI

Artificial Intelligence is being hailed as the brilliant technology that can help businesses increase their operational, predictive, and productive power. Organizations are willing to invest heavily in this disruptive technology that is transforming existing workflows. With AI being omnipresent – from smartphones to household finances to law and justice systems, it is extremely important that both the public and the companies realize that there could be a potential dark side to artificial intelligence too.

Many movies speculate on how artificial intelligence can escape man’s control to reign supreme, subjecting man to slavery. While this is a bit far-fetched, the actual danger is much more subtle.

In an article from the Live Mint, Sandipan Deb writes that AI is only as good as the data that is fed into it. “The data is worked on by deep-learning software, which absorbs the data, figures out patterns, creates rules to fit the patterns, and keeps tweaking those rules as more data is fed into it.” The data, which is fed by humans, will contain the prejudices that mankind holds, which will ultimately influence the end result that reflects the societal biases like racism, sexism etc.

In May 2018, a report highlighted that an AI-generated computer algorithm used by a US court for risk assessment was biased against black prisoners. The program asserted that blacks were twice as likely as whites to re-offend in the US. This conclusion was a result of the flawed or skewed training data that it was learning from. While machine learning is often hailed as being impartial and unbiased, the technology will only be as good as the data that has been fed into it.

In 2015, Google came under severe criticism when its photo app tagged two black people as gorillas—perhaps because the algorithm’s training data set did not have pictures of enough black people.

In 2016, Russian scientists ran a global beauty contest to be judged by an AI. Of the 44 winners, only one had dark skin. The algorithm had been trained mostly with photos of white people, and it had equated “fair skin” with “beauty”.

Another example is when an AI is fed the resumes of candidates for a top corporate job, and it chooses a man, because data shows that men have overwhelmingly outnumbered women as CEOs in the past. Going by the data, the AI will decide that a man will make a better CEO than a woman. While the woman would have been pushed back due to gender bias in the past, the computer would not have any idea about this as it is powered by the data it is given.

While AI is being increasingly deployed across a wide variety of domains, from personal digital assistants, email filtering, fraud prevention, voice and facial recognition and content classification to generating news and offering insights into how data centres can save energy, the discrimination that AI could implement should also receive attention. More than a technical issue, this remains a social problem that technologists could find difficult to solve.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.

HOW AI IS CREATING AN IMPACT IN THE TELECOM INDUSTRY

THEN AND NOW

Gone are the days when the telecom industry used to be involved solely in providing basic phone and internet services. With the bloom of Artificial Intelligence, this particular industry has seen exceptional technological growth. And it is still growing, with experts expecting it to grow at least 42% by this year.

One move that could transform this sector would be to leverage AI’s ability to engage in active learning while analyzing very large amounts of data collected from their massive customer base.

Where does this data come from?

This data is culled from devices, networks, mobile applications, geolocations, detailed customer profiles, services usage and billing data. Harnessing this Big data through AI could open up a wide range of uses across management and operations departments.

In a majority of companies, the role of AI within the telecommunications environment has been limited to chat bots that are automating customer service inquiries, routing customers to the proper agent and routing prospects with buying intent directly to salespeople.

However it is also possible to provide better customer experiences, improve operations, and increase revenue through new products and services by gaining actionable insights from data collected. Through AI, it is possible for operators to improve network efficiency; lower operating costs and improve both the quality of service and customer experience.

Tom Anderson, Principal technologist at Atis, writes that as operators transition their network architectures with software-defined networking and virtualization technologies that enable automation, AI will leverage these capabilities to self-diagnose, self-heal and self-orchestrate the network.

He says that through the use of algorithms that look for patterns, AI will be able to both detect and predict network anomalies, enabling operators to proactively fix problems before customers are impacted.  This pattern-recognition capability is particularly useful with respect to network security as AI will be able to help identify suspicious activity related to potential security threats, allowing the network to “take-action” in real time before it impacts network performance.

From a subscriber intelligencer perspective, AI will allow operators to collect, store and analyze data from across an operator’s entire customer base to achieve real-time behavioral insights. Their social media, brand coverage and customer sentiments could also be analyzed to learn what drives customers to the service provider and what drives them to leave

Information thus gained can be combined with  machine learning algorithms to make personalized  personalized recommendations based on a user’s behavioral patterns and content preferences. Relevant upsell and cross-sell offers to the right users at the right time can be made. Data could be analyzed and the call & data package that best suits different types of users can be offered, increasing sales success rate. AI and machine learning could also be used in detecting and fixing potential issues for customers even before they’re apparent to the end user..

Big data will be essential for operators to achieve better utilization of network resources, allowing the network to adjust services based on user needs, environmental conditions and business goals resulting in better network optimization.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.