Emerging Problems in Data Science and Machine Learning and How to Counter Them

For companies to excel in today’s tech-driven world, adequate knowledge is vital in data science and machine learning. It enables them to understand the industry’s modern trends and crucial insight into the consumer base. In almost every sector, big data helps businesses make strategic decisions and take efficient predictive and risk management measures. To sustain itself in future markets, it must not undervalue and utilize data science and machine learning.

As time progresses, more and more corporations incorporate data science and machine learning in various operations, enhancing their interpretability of processes’ problems and efficiency. However, the new trends in data science and machine learning also pose potential issues, which companies are working to resolve through research and trial.

This article discusses the major problems in data science and machine learning and sees how organizations tackle them.

1 – Challenges in Model Interpretability

The increasing use of machine learning in almost every sector has opened new gateways of opportunities for organizations worldwide. Machine learning has not only made the process of decision-making easier. However, it has also enabled organizations to avoid potential threats in their operations. The ML algorithms are not smart enough to understand the rules and regulations of organizations yet. It means that organizations cannot trust the results of ML right away. Thus, incorporating ML in various organizational processes takes much time. At the same time, instant interpretability of its models remains an issue at hand. To counter this problem, big corporations worldwide are now hiring employees preferably with Online Masters in Data Science and training them on machine learning customized models. It means that organizations today will have their ML specialists to construct, implement, and overlook the ML algorithms employed in processes.

2 – Lack of Quality Data

The fundamental basis of machine learning is the availability of quality data. It means, without adequate quality data, developers would face a significant challenge in developing the AI algorithms for the ML processes. Raw or incomplete data disrupts the machine learning process and obstructs critical operations for an organization. The only escape to this problem is to dedicate a team of highly skilled data science professionals who could spend ample time evaluating the data quality.

Machine learning requires a top-quality feed of data to churn resourceful outcomes. Therefore, proper infrastructure to streamline the workload of instant data processing must be in place to produce the desired results. Furthermore, to process the amounts of bulk data, efficient cloud computing is essential. Without powerful cloud support, accelerated data evaluation and flexible storage cannot occur.

3 – Maintain Fairness and Bias

Machine learning models and artificial intelligence are not a hundred percent accurate and unbiased. Their mode of operation and the results depend on the data that gets fed into them. The algorithms and the predictive options they present are not from the computer’s thoughts or research. Instead, they are contingent on the information and choices installed into the system by the programmers. It has raised questions about the authenticity of the method used by these tech models. Developers are under the obligation to ensure fairness and avoid prejudice in the models.

4 – Ensuring Data Privacy

Given the public’s dependence on technology, maintaining the semblance of privacy is near to impossible in the current times. The internet, private businesses, governments, and even the random websites you visit are collecting your data nearly all the time. The companies use this information to improve their operation by making them more customer-centered. Be it getting music suggestions according to your taste or ads of products and services. Your data is helpful to provide better services for you. However, it is crucial to know where your information is going and how it gets utilized by all the sources collecting it.

5 – AI Alignment and Safety

As we advance towards the era of digitization and acknowledge AI’s fantastic capabilities, it is essential to consider its impacts on human decision-making processes’ complexities and subtleties. Regardless of the claims-making it out as a genuinely intelligent system, AI is entirely dependent on the algorithms fed by the developer, and its alignment is anything but automatic. Hence, it is up to humans to guide Artificial Intelligence to make the right choices to become genuinely intelligent. Such issues and how the developers address them shall decide the future of AI.

Conclusion

The issues with error corrections and accountability have been a hot topic of discussion since the technology and AI model’s inception. AI users find it easier to dodge the personal sense of responsibility and build a reliance on the algorithms without diving deeper to see if they are genuinely correct. It is a significant issue that a majority of tech companies have not touched upon as of yet. These critical questions and challenges related to this topic take a backseat during research and discussions on the pros and cons of AI.