Introduction
The rise of artificial intelligence (AI) is rampant in modern society due to globalization which has taken root almost in all sectors. Having technology-centric aspects in place is important, but there is still a concern for better AI due to the challenges that have ensued previously. The machine learning architecture has been characterized by advanced settings that have distorted many working grounds. Experts in technology say that AI may make most people better than it is now in the next decade (Tagarev et al., 2020). However, there are concerns about how technology will affect what it means to be human.
There are chances that the world may have different features regarding productivity, division of labor and accuracy in manufacturing and distribution. It is important to consider issues to do with AI because currently, the matter has adverse effects on the depreciation of human labor, information protection and manipulation of people, among other issues. It is possible to have a fair AI by governing the use of data provided to assist data mining that promotes interpretable systems hence understanding and configuring results that show fairness through the computational method.
Review of Literature
Distributed AI may aid in mitigating many security problems that surface as a result of vulnerabilities in using the cloud. There should be smart integration of aspects of technology that allow data to be put safely through the application of digital codes that safeguard leakages and loss of sensitive data for companies and individuals (Mitrou, 2018). AI techniques can be fair enough by reducing security exposures in environments where data and information are at risk. Machine learning (ML) with AI has been critical technology for information protection. From the research objective, there is a gap in having fair AI through the capability that the cloud software has in identifying threats such as malware or phishing attacks (Mitrou, 2018). AI must enable a user to detect malicious programs or approaches online, facilitated by rampant cybercrime issues.
Through artificial intelligence, technocrats have exploited digital data to influence other people’s behavior on both local and international stages. Those aspects can be referred to as cyber environments whereby the intangible nature of AI has been able to influence real-world data and information (Yakovleva & van Hoboken, 2020). The fair part of AI regarding information sharing is that various digital actors can theoretically connect hence getting a globalized society that can solve problems upon ensuing. The new practices of domination of conventional digital activities provide conditions that may control asymmetric characteristics and structure hence having transparency in many elements of interaction using technology (Yakovleva & van Hoboken, 2020). Exploring this feature helps to highlight how AI has led to the complex and multi-faceted item of technology that influences the coordination of activities. Thus, through the efficiency created by digital power, AI can be said to be fair as the deployment of various functions can be done remotely and in an independent state.
AI should learn the types of attack that comes when one is using a digital tool and communicate instantly as one way of having safe browsing. Any deviation in data passing through a given protocol should be detected and responded to before establishing further. Of late, neural networks, expert systems, and deep learning have necessitated online information protection (Tagarev et al., 2020). For example, by using biologically-inspired programs, the paradigm enables a computer to learn and observe data trends hence making it possible to detect some discrepancies in information sharing.
Additionally, the neural network has codes assigned to relate correct and incorrect outputs that determine the weights of information being shared from a given digital handset. What is lacking in this literature is the control effectiveness that can allow incident response in the prioritization of security alerts that may be because of online information vulnerabilities. For example, IBM/Watson has frequently leaned on the cognitive knowledge consolidation of networks that detect possible information and data breaches. Through digitizing online content creation and storage processes, AI has effectively configured possible threats to information (Tagarev et al., 2020). Therefore, the world can have fair AI through the advanced network and system monitorization powered by microservices architecture.
AI can imitate human behavior and traits in productive areas. Through robotic technology, AI has presented fair aspects when it comes to elevation processes in busy industries or where human beings might risk working (Yakovleva & van Hoboken, 2020). AI is transforming the world in many ways, especially now that everything has gone digital. Through the power to learn human capabilities, machines have been put in key areas, such as service industries, to offer guidance to customers who visit specific points for business purposes. Modern technology has led to the deployment of AI in various sectors, such as finance, national security, delivery of healthcare services, administration of criminal justice, and development of smart cities (Yakovleva & van Hoboken, 2020). With the emergence of remote control, a user can effect a change that may take longer when handled by humans. Additionally, accuracy and punctuality aspects have been possible through AI, which for this matter, presents a fair part of it as it has brought positive transformation to the world.
This paper also relates the information protection aspect to human labor issues. Due to the remote application of many processes, people are not needed in high numbers to work on a threat that can be combatted remotely. The use of AI in many divisions of labor has led to notable cases of unemployment. There is a high level of machine learning that has replaced the need to have a human workforce in most technical spaces. The unemployment rate in the US in 2018 was 3.9%, which indicates the lowest annual rate in nearly four decades (Hutson, 2017, p. 19). The rapid job loss due to technological change, which concerns automation, has been a critical matter that must be addressed. When such aspects are considered, it embarks on a journey to get a fair AI that will provide low cases of job loss.
One of the ways in which artificial intelligence can be used to increase human labor is the expansion of microservices that will be deployable independently (Straw, 2020). The rise of cloud software comes with skills and knowledge that must be imparted to people for the continuity of work-related issues. There should be a balance in implementing some programs that may increase unemployment rates. System developers should create segments that need intensive sites and digital protocols monitoring simultaneously, creating a job for the majority.
There should be scaling of AI to create more jobs through distinctive and partitioned programs that require additional staff to work on the same. There seems to be a gap in this area because, despite the automation of many areas, AI can be fair when it comes to human labor by enabling repetitive tasks, such as data entry in assembly line manufacturing. This means workers will be required to focus on the higher-value and touching tasks that will probe them to expand interpersonal interactions (Straw, 2020). Under this perspective, benefits for individuals and the companies that employ them will be created. All workers must be upskilled or reskilled due to the changing job requirements resulting from a rapid technological shift in major fields. Therefore, by doing the factions mentioned above, it will be possible to offer fair AI for human labor.
Conclusion
This research paper has mainly focused on the ways human beings can have fair AI. Through the points given, it is possible to have fair AI by integrating various digital tools that mitigate the adverse effects of modern technology. This paper’s scholarly significance is that the audience can learn various metrics that can be applied to reduce job losses due to automation, combat information loss threat, and have efficient mass manipulation, among others. Through the research paper, other scholars can learn the importance of controlling AI network issues and patterns that benefit human beings. For example, by conducting phishing attacks while a user is online, AI serves in a friendly way to ensure that information sharing is regulated, hence reducing cybercrime issues. It is relevant to conduct research relevant to AI as that would lead to a safe environment characterized by advanced levels of fighting sociological changes that may be challenging to the digital actors.
References
Hutson, M. (2017). How artificial intelligence could negotiate better deals for humans. Science, 5(2), 17-23. Web.
Mitrou, L. (2018). Data protection, artificial intelligence and cognitive services: Is the general data protection regulation (GDPR) ‘Artificial Intelligence-Proof’? SSRN Electronic Journal, 6(3), 4-7. Web.
Straw, I. (2020). Automating bias in artificial medical intelligence (AI): Decoding the past to create a better future. Artificial Intelligence in Medicine, 110(6), 101965. Web.
Tagarev, T., Sharkov, G., & Lazarov, A. (2020). Cyber protection of critical infrastructures, novel big data and artificial intelligence solutions. Information & Security: An International Journal, 47(1), 7-10. Web.
Yakovleva, S., & van Hoboken, J. (2020). The algorithmic learning deficit: Artificial intelligence, data protection, and trade. SSRN Electronic Journal, 6(12), 22-29. Web.