5 Challenges of Artificial Intelligence Explained
Challenges of artificial intelligence are; data security challenges, skill and knowledge requirements, high cost, technological limitations, and enforcement of ethical practices.
This article discusses the challenges of artificial intelligence, as follows;
1). Data Security Challenges (as one of the Challenges of Artificial Intelligence)
Most features and branches of artificial intelligence are directly dependent on datasets [1] [2].
These include machine learning, deep learning and all applications involving the use of neural networks.
Data that is used to facilitate artificial intelligence applications must be protected, especially when these data include sensitive details. This is the purpose of data security, and it is associated with some challenges.
Challenges of data security that affect artificial intelligence include; complexity of firewalls, password management, software modification, and risk of data theft or database compromise.
AI is a threat to security itself, when data security is not insured, so that access to classified datasets can have dire consequences with regards to how the linked artificial intelligence systems are used.
2). Skill and Knowledge Requirements
Artificial intelligence is a highly-specialized field, meaning that it is impossible to implement any successful AI project without the required skills and knowledge.
Skills required for artificial intelligence include programming, computation, analysis, problem-solving, and logical reasoning.
Knowledge required to implement artificial intelligence successfully includes that of programming languages like Python, as well as of neural networks, data management, and real-life AI applications such as robotics.
Because these skills and knowledge are fairly complex to acquire, artificial intelligence is often challenges by a lack of expertise, especially when it involves advanced applications.
3). High Cost (as one of the Challenges of Artificial Intelligence)
Artificial intelligence projects are usually expensive due to the high quality resources required in the form of skill, data, energy resources and computing power.
Depending on the scale, most AI projects cost between tens of thousands of billions of dollars.
This can constitute a major hindrance for many potential AI system-developers, thereby limiting the improvement and expansion of artificial intelligence.
The high cost of AI projects and tools can be attributed to relative technological immaturity; as the artificial intelligence discipline has not been developed to its full potential. Artificial intelligence is cost-effective when care is taken to adhere to a definite set of objectives, and to conserve available resources on AI projects.
4). Technological Limitations
Artificial intelligence has some limitations that can be attributed to the mere fact that the technology is still in its developmental stage.
Major technological limitations of AI technology are; data shortage, lack of algorithmic flexibility, lack of sufficient data diversity, lack of creativity, interoperability and implementation drawbacks, among others.
In addition to reducing potential of AI as a field, these limitations can pose a threat to the safety of AI-controlled systems that require advanced measures and flexibility to operate.
5). Enforcement of Ethical Practices (as one of the Challenges of Artificial Intelligence)
The fact that AI plays a role in sustainable development implies that it has major socioeconomic implications.
Some of the potentially-negative social implications of artificial intelligence border around data and its handling, which is a central theme of AI implementation.
Namely; the reliability, quality, and stability of data represent ethical issues with artificial intelligence [4].
Some of the most pressing ethical issues in artificial intelligence are; decision bias, data reliability, privacy, environmental justice concerns, and unethical human influence [3].
While artificial intelligence tends toward autonomy, as in some of its applications and fields like automation and robotics, a total lack of bias in AI applications is not always evitable, since humans are often able to control the basic programming and data sources of these applications.
On a broader, socioeconomic scale, other ethical issues in artificial intelligence border around fairness, equality, workplace conditions, and uses of artificial intelligence.
Due to the remote nature and versatility of AI development, it is not easy to track all projects and ensure that they are in compliance with ethical codes and regulations.
Conclusion
Challenges of artificial intelligence are;
1. Data Security Challenges
2. Skill and Knowledge Requirements
3. High Cost
4. Technological Limitations
5. Enforcement of Ethical Practices
References
1). Kumar, P. R.; Manash, E. B. K. (2019). "Deep learning: a branch of machine learning." Journal of Physics Conference Series 1228(1):012045. Available at: https://doi.org/10.1088/1742-6596/1228/1/012045. (Accessed 15 January 2023).
2). Murdoch, B. (2021). "Privacy and artificial intelligence: challenges for protecting health information in a new era." BMC Med Ethics. 2021 Sep 15;22(1):122. Available at: https://doi.org/10.1186/s12910-021-00687-3. (Accessed 13 January 2023).
3). Naik, N.; Hameed, B. M. Z.; Shetty, D. K.; Swain, D.; Shah, M.; Paul, R.; Aggarwal, K.; Ibrahim, S.; Patil, V.; Smriti, K.; Shetty, S.; Rai, B. P.; Chlosta, P.; Somani, B. K. (2022). "Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?" Front Surg. 2022 Mar 14;9:862322. Available at: https://doi.org/10.3389/fsurg.2022.862322. (Accessed 13 January 2023).
4). Stahl, B. C. (2021). "Ethical Issues of AI." Artificial Intelligence for a Better Future, An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies (pp.35-53). Available at: https://doi.org/10.1007/978-3-030-69978-9_4. (Accessed 13 January 2023).
