5 Challenges of Machine Learning to Look for in 2022

Machine Learning
Machine Learning

How often did you hear the terms Big Data, AI, and Machine Learning? Probably many times. If you use a professional social networking site like LinkedIn, you may have received numerous sales pitches about their “new and groundbreaking AI solution” that would automate everything. 

Machine Learning equips organizations with the knowledge they need to make better-informed, data-driven decisions in less time than traditional methods. However, it isn’t the mythical, magical procedure that many people imagine it to be. Machine Learning comes with several challenges. Here are five common machine learning issues.

1- Technological Singularity

Despite the fact that this topic has received a lot of media coverage, many experts are unconcern about AI exceeding human intelligence in the future. Although Strong AI and superintelligence are not yet a reality in society, the concept poses some intriguing challenges when we explore the deployment of autonomous systems such as self-driving automobiles. It’s impossible to expect a self-driving car to never cause a collision, but who is legally responsible in those situations? Should we continue to seek self-driving cars, or should we restrict the integration of this system to only semi-autonomous automobiles that increase driver safety? Although the verdict is yet out on this, still these are the debates that are arising as new, inventive AI technology develops.

2- Impact on Jobs

While many people’s fears about AI and machine learning center on job loss, this is a problem that needs to be reframed. The market need for particular jobs evolves with each disrupting new technology. Within the automobile sector, for example, several manufacturers, such as GM, are moving their focus to electric car production to line with green objectives. The energy business will not go away, however, the source of energy will change from fossil fuels to electricity. Artificial intelligence must be considered similarly, as it will transfer work demands to other fields. Individuals will be needed to assist in the management of these platforms as data increases and changes daily. More sophisticated issues within the companies are likely to be impacted by employment demand shifts, such as customer service, which will still require resources. One of the most essential aspects of AI and its impact on the job market will be assisting people in transitioning to these new sectors of market need.

3- Privacy

Data privacy and data protection are frequently discusse in terms of privacy, and these issues have enable legislators to make progress in recent years. GDPR law was enacted in 2016 to secure people’s personal information in the European Union and the European Economic Area while also providing them more control over their data. Individual states in the United States are adopting policies, like the California Consumer Privacy Act, which compels firms to notify customers when their data is collected. Companies have had to reconsider how they retain and then use personally identifiable information as a result of the new legislation. As a result, organizations are increasingly prioritizing security efforts to minimize any weaknesses and potential for cyberattacks.

4- Bias and Discrimination

Many ethical problems about the use of machine learning and AI have been raise as a result of discrimination among numerous intelligent systems. How can we protect ourselves from bias and discrimination when the training data itself is prone to bias? While most businesses have good intentionson Document verification when it comes to automation, Reuters points out some unintended downsides of adopting AI into employment methods. Amazon mistakenly disadvantag potential job candidates for available technical posts by gender in their endeavor to automate and streamline a process, and the program had to be scrappe. As more incidents like this emerge, the Harvard Business Review has posed more critical issues about the use of AI in recruiting practices, like what data ought to be available when evaluating an applicant.

The existence of bias and discrimination isn’t restrict to human resources; it’s also exhibit in several apps ranging from facial recognition algorithms to social media.

5- Accountability

Because no meaningful legislation to regulate AI approaches exists, there is no true regulatory method to verify that ethical AI is deploy. The financial implications of implementing an immoral AI system serve as current incentives for corporations to obey these principles. To fill the hole, ethical frameworks have emerged as a result of a collaboration between researchers and ethicists to oversee the production and distribution of AI models in society. At the moment, however, they merely serve as a guide, and research suggests that the coupling of distributed accountability and a lack of vision into potential effects isn’t always helpful to minimizing societal harm.

Final Thoughts

Machine learning is frequently misunderstan as having the ability to solve all problems. Unfortunately, machine learning is not a magical solution to all the issues and it has its cons too that every sector must keep in mind while choosing it.

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