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Challenges of Machine Learning

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Challenges of Machine Learning

Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on the application of algorithms and data to replicate the way humans learn. It is a process that improves the accuracy of machine learning.

In this tutorial, we will discuss the challenges of Machine Learning

Challenges of Machine Learning

The advancement of machine learning technology in recent years certainly has improved our lives. However, the implementation of machine learning in companies has also brought up several ethical issues regarding AI technology. A few of them are:

Technological Singularity:

Although this topic attracts lots of attention from the many public, scientists are not interested in the notion of AI exceeding humans’ intelligence anytime in the immediate future. This is often referred to as superintelligence and superintelligence, which Nick Bostrum defines as “any intelligence that far surpasses the top human brains in virtually every field, which includes general wisdom, scientific creativity and social abilities.” In spite of the fact that the concept of superintelligence and strong AI isn’t a reality in the world, the concept poses some interesting questions when we contemplate the potential use of autonomous systems, such as self-driving vehicles. It’s impossible to imagine that a car with no driver would never be involved in a car accident, but who would be accountable and accountable in those situations? Do we need to continue to explore autonomous vehicles, or should we restrict the use of this technology to produce semi-autonomous cars that encourage the safety of drivers? The jury isn’t yet out on this issue. However, these kinds of ethical debates are being fought as new and genuine AI technology is developed.

AI Impact on Jobs:

While the majority of public opinion about artificial intelligence revolves around job loss, the issue should likely be changed. With each new and disruptive technology, we can see shifts in demand for certain job positions. For instance, when we consider the automotive industry, a lot of manufacturers like GM are focusing their efforts on electric vehicles to be in line with green policies. The energy sector isn’t going away, but the primary source that fuels it is changing from an energy economy based on fuel to an electrical one. Artificial intelligence must be seen as a way to think about it, as artificial intelligence is expected to shift the need for jobs to different areas. There will be people who can control these systems as data expands and changes each day. It is still necessary resources in order to solve more complicated issues within sectors that are more likely to suffer from demand shifts, including customer service. The most important element of artificial intelligence and its impact on the employment market will be in helping individuals adapt to the new realms that are a result of the market.


Privacy is often frequently discussed in relation to data privacy security, data protection, and security. These concerns have helped policymakers advance their efforts recently. For instance, in 2016, GDPR legislation was introduced to safeguard the personal information of individuals within Europe’s European Union and European Economic Area, which gives individuals more control over their data. Within the United States, individual states are creating policies, including the California Consumer Privacy Act (CCPA), that require companies to inform their customers about the processing of their data. This legislation is forcing companies to think about how they handle and store personally identifiable information (PII). In the process, security investments have become a business priority to remove any potential vulnerabilities or opportunities to hack, monitor, and cyber-attacks.

Bias and Discrimination:

Discrimination and bias in different intelligent machines have brought up several ethical issues about using artificial intelligence. How can we protect ourselves from bias and discrimination when training data could be biased? While most companies have well-meaning intentions with regard to their automation initiatives, Reuters highlights the unexpected effects of incorporating AI in hiring practices. As they tried to automate and make it easier to do so, Amazon unintentionally biased potential candidates based on gender in positions in the technical field, which led them to end the project. When events like these come to light, Harvard Business Review (link located outside of IBM) has raised pertinent questions about the application of AI in hiring practices. For example, what kind of data could you analyse when evaluating a candidate for a particular job.

Discrimination and bias aren’t just limited to the human resource function. They are present in a variety of applications ranging from software for facial recognition to algorithms for social media.


There isn’t a significant law to control AI practices. There’s no mechanism for enforcement to make sure that ethical AI is being used. Companies’ primary motivations to adhere to these standards are the negative effects of an untrustworthy AI system on their bottom lines. To address the issue, ethical frameworks have been developed in a partnership between researchers and ethicists to regulate the creation and use of AI models. But, for the time being, they only serve as a provide guidance the development of AI models. Research has shown that shared responsibility and insufficient awareness of potential effects aren’t ideal for protecting society from harm.

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