Critical Review of Ethics and security in artificial intelligence and machine learning: Current perspectives in computing
Introduction
The critical analysis of Ethics and Security in Machine Learning entails considerable difficulties for scholars, decision-makers, and technology innovators. Ethical issues related to the use of AI technologies in healthcare, finance, education, and cybersecurity, among others, have become more significant as AI technologies have been employed in various industries. While computing research has traditionally emphasised performance, modern research has highlighted the importance of managing ethical and security problems linked with them.
“Ethics and Security in AI and ML: Current Perspectives in Computing” is an article that gives the reader some insight into various ethical problems and security threats that have arisen due to the wide-scale use of artificial intelligence and machine learning. This paper raises several questions regarding issues like biases of algorithms, data breaches, adversarial attacks, transparency, and misuse of artificial intelligence and machine learning systems.
Summary of the article
The paper considers the ethical and security aspects of artificial intelligence and machine learning in modern computing. In this regard, the authors describe the role that these types of technologies have taken in different fields and their impact in creating Ethical Issues in AI and security problems at the same time.
They consist of biases in algorithms, discriminatory practices, lack of transparency in decision-making, privacy issues, and liabilities. Additionally, the authors present some of the security vulnerabilities, including adversarial attacks, data manipulation, cybercrimes, deep fakes, and AI vulnerabilities.
The authors employ a literature-based research methodology and present an overview of ethical and security problems with respect to artificial intelligence and machine learning technologies. Finally, they claim that proper implementation of AI depends on robust regulation, transparency of algorithms, efficient security solutions, and cooperation between all the parties involved.
Critique
Significance and contribution of the field
This article contributes significantly to the current AI Ethics Critical Review and machine learning security through its comprehensive analysis of the difficulties facing intelligent systems. This paper makes it quite clear that technology needs responsible governance because the relationship between ethics and security is indeed intertwined.
Among the many strengths of this paper, the practicality of the subject matter deserves mention. For example, problems like algorithmic discrimination, privacy concerns, adversarial machine learning, and deepfake technology are discussed in detail.
The conclusion made in this research reinforces the results presented in other research, for example, in research conducted by Huang et al. (2022) and Koshiyama et al. (2022), when attention is paid to the importance of such factors as transparency, accountability, and ethics in terms of developing AI. In addition, the research reveals some of the concerns related to using AI technology.
Nonetheless, the major drawback of the study is that it pays little attention to suggesting technical ways of overcoming the problems mentioned but concentrates on discussing possible problems.
Methodology and research design
For their study on ethics and security in the areas of Artificial Intelligence (AI) and Machine Learning (ML), the researchers use a literature review research method. In this case, using such a research method allows the researchers to get information from various academic materials and highlight key issues arising within them. Considering the fast evolution of AI technology, the chosen research method seems suitable.
One of the strengths of the research design lies in the fact that the approach allows for gaining a holistic picture of discussions on the issues of ethical aspects, privacy protection, transparency, accountability, and cybersecurity.
However, there are some deficiencies in the methodological approach adopted for this research. The first issue is that all the information is collected using secondary sources alone and does not include any empirical findings, surveys, interviews or cases. Moreover, little information has been provided by the article concerning the way the sources selected for analysis were evaluated. The lack of systematic review techniques also contributes to lowering the transparency of the research process.
Additionally, the literature review method used by the authors is well-suited to the subject matter under study, given its fast-evolving nature. The authors’ review of prior studies provides insight into the general issues being faced. This is consistent with the principles of literature review explained by Fadli (2021) and Sahar (2008).
Theoretical and Interdisciplinary Analysis
In the article, an effective integration of ideas from artificial intelligence, cybersecurity, ethics, governance, and policymaking has been carried out. This is essential for ethical and security challenges in artificial intelligence that go beyond technical challenges.
The FAT frameworks have been mentioned in the literature review to provide a theoretical basis for responsible development of AI systems. Ethical principles help in designing AI technology and minimising problems related to discrimination and abuse.
In addition, this paper highlights the necessity to adopt regulations such as data protection laws and ethical frameworks for the proper utilisation of AI. Such discussions have been deliberated in recent times because of the general debate on AI regulation.
Though these are significant points, the theoretical section is still rather broad and generic. In addition to being more specific and concrete regarding AI’s impact, more attention could have been paid to incorporating various theories of ethics such as utilitarianism or deontological ethics. Moreover, the interdisciplinary nature of the topic deserves further consideration.
The above arguments are supported by the research conducted by Huang et al. (2022), which focuses on the increasing relevance of ethical frameworks related to artificial intelligence, as well as the research done by Bertino et al. (2021), regarding AI security.
Ethical Considerations
The discussions on biased data sets and discriminatory effects is pertinent due to the growing role of AI systems in the process of decision-making in various areas such as job recruitment, medicine, financial services, and policing. Such questions are also highlighted in the work of Montasari (2024).
In addition, the analysis of possible threats to security, including adversarial attacks, data breaches, model tampering, and deepfakes, among others, serves to reinforce the findings presented by Siriwardhana et al. (2021) and Ahmed et al. (2021).
Similarly, the analysis of various security concerns, including adversarial attacks, data breaches, manipulation of models, and deepfakes indicates the potential dangers associated with weak security measures.
However, there is barely any mention in the article of the ethical considerations that need to guide AI practitioners in managing AI risk.
Writing Style and Structure
The writing style used in the article is clear and easy to understand. The authors discuss all the issues connected with ethics, security, and possible approaches to developing ethical AI in detailed paragraphs.
The vocabulary used by the authors is plain and appropriate for people having different levels of education. The use of examples illustrating issues such as biases, privacy, cyber threats, and deepfake adds clarity to complex ideas.
However, in some parts of the article, the authors provide descriptions but not critical evaluations of previous research results or opposing opinions. It is possible that an analysis of other views would give additional depth to the scientific value of the paper.
Conclusion
These findings confirm the outcomes reported by Huang et al. (2022), Koshiyama et al. (2022), Bertino et al. (2021), Montasari (2024), and Siriwardhana et al. (2021), who all stress that the need for ethical management, transparency, accountability, and security is becoming increasingly important for the development of AI technologies. However, although there are certain drawbacks in terms of empirical testing and implementation methods, the article offers relevant information about Ethics and Security in AI.
While the content of the paper could have been improved through the provision of empirical data, more theoretical insight, and better strategies for implementation, some key ideas about the ethical and security implications for the future of AI and machine learning have been put forth. Overall, this paper proves to be useful for any researcher who seeks knowledge in the areas of Artificial Intelligence Ethics and Machine Learning Security Review.
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Reference
- Ahmed, S., Hossain, M., Kaiser, M., Noor, M., & others. (2021). Artificial intelligence and machine learning for ensuring security in smart cities. In Data-Driven Mining, Learning and Analytics. https://doi.org/10.1007/978-3-030-72139-8_2
- Bertino, E., Kantarcioglu, M., Akcora, C., & others. (2021). AI for Security and Security for AI. ACM Conference on Data and Application Security and Privacy. https://doi.org/10.1145/3422337.3450357
- Fadli, M. R. (2021). Understanding the design of qualitative research methods. HUMANIKA, 21(1), 33–54. https://doi.org/10.21831/hum.v21i1.38075
- Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence. Available at: https://ieeexplore.ieee.org/abstract/document/9844014/
- Koshiyama, A., Kazim, E., & Treleaven, P. (2022). Algorithm auditing: Managing the legal, ethical, and technological risks of artificial intelligence, machine learning, and associated algorithms. Computer. Available at: https://ieeexplore.ieee.org/abstract/document/9755237/
- Montasari, R. (2024). Addressing ethical, legal, technical, and operational challenges in counterterrorism with machine learning: Recommendations and strategies. In Artificial Intelligence and International Security in the Fourth Industrial Revolution. https://doi.org/10.1007/978-3-031-50454-9_10
- Sahar, J. (2008). A critique of qualitative research. Indonesian Nursing Journal, 12(3), 197–203. https://doi.org/10.7454/jki.v12i3.222
- Siriwardhana, Y., Porambage, P., & others. (2021). AI and 6G security: Opportunities and challenges. 2021 Joint European Conference on Networks and Communications & 6G Summit. Available at: https://ieeexplore.ieee.org/abstract/document/9482503/