Get free samples written by our Top-Notch subject experts for taking help from our assignment helper.
Artificial intelligence is considered as imitating machines that copy the cognitive procedure of humans. Particularly. Some unique applications of AI contain special systems of computers, processing of artificial language, machine vision-based AI machines, and voice recognition. It usually mimics functioning roles and is implemented through an advanced referral system of search engines such as Netflix and Amazon; comprehension of human speech such as Alexa or Siri; self-driving cars like Tesla with a highly competitive system of games like chess. AI is capable of solving various challenging and difficult problems in academia and industries (Feher et al. 2021).
Various fields of artificial intelligence-based research have their focus on particular tooling and objectives. An introduction of AI-based research objectives consists of theoretical goals, theoretical thinking, representation of knowledge, understanding, and natural processing of language, learning, and planning along with the ability to manage and move objective education as an ability to solve unilateral problems in a long run field objective. Mathematical search and official optimization-related statistical systems, probability-related economics, and methods are utilized by researchers in the field of AI psychology, philosophy, linguistics, computer science, and various others (Feher et al. 2021).
AI is capable of boosting profitability as well as transforming businesses throughout various sectors of a given system (Delponteet al. 2018). It works by evolving, adapting, and learning with dynamic time. These systems have an increased necessity in post - epidemic situation throughout the globe whereby scalable solutions of AI is capable of helping organizations to initiate their preparation even at the time of unprecedented circumstances (Delponteet al. 2018).
AI is the method of solving problems by learning, thinking, defining, and functioning like the human brain to yield a given result. Genius objectives of AI include solving problems that are associated with understanding computer operations as humans by the means of solving. problems, learning, and thinking (Delponteet al. 2018). For the ones who prefer looking under who hood, there exist four basic understanding elements, namely collaborative filtering, machine learning, classification, and categorization. These are the four major colors that further exhibit steps under the analytical procedure. Categorization consists of creating a various matrix that is particular to the domain of the problem, such as networking and finance. The classification contains the determination of data to find out their relevance for solving various issues. Machine learning contains anomaly detection, linear regression, deep learning, and clustering. The collaborator filter contains the finding of patterns throughout huge sets of data (Delponteet al. 2018).
Various organizations are found to adopt a set of higher principles for ensuring the decision-making of AI ethically without any harm. However, the forgiving principle relates to, the organizations being required to implement strategies that contain measurable metrics, being monitored by legitimate personnel, data scientist, and engineers. There exist no single–size–fits for various approaches for qualifying probable harm of AI (Boddington, 2017). These metrics generally vary throughout the organizations by the means of regulations and different cases. However, it may not discourage the organizations. It can draw through a combination of prevailing technological practices, legitimate precedents, and research (Raischet al. 2021). Various ethical frameworks of AI are not capable of being applied, as demonstrated by researchers. Without the dramatic rise in a given framework of AI, there simply exists lesser performance of technological personnel for clearly upholding such guidance of higher level. It will, in turn, indicates that instead of a suitable marketing campaign by AI frameworks, they often fail in stopping the harm caused by AI which is supposed to be prevented (Boddington, 2017).
In this respect, ethical guidelines probably can create severe risks for those companies that adopt them. It can create an inappropriate sense that the company makes AI risk-free during a fact rampant danger (Raischet al. 2021). In case the companies find the action of drafting ethical AI principles enough for ensuring the safety of AI, they are required to think once more.
The organizations are required to assure a tandem development of such frameworks with a wider ethical strategy regarding AI which has a direct focus on its application, through a concrete measure at its core. All of the principles of AI that are adopted by organizations are required to have metric clarity which is measurable and can be monitored by legitimate personnel, data scientist, and engineers (Azam et al. 2020).
The major challenge is to propose a creative idea regarding the method of developing and supporting technical, legitimate, and ethical AI governance (Feher et al. 2021). It focuses the investigation on particular research areas –
Throughout the globe, civil society, academics, governments, and industrial representatives are noted to debate regarding the place where legitimate rules and frameworks are required. And in case of their requirement, technical or ethical approaches are sufficient. Even after answering such questions, issues regarding the extent of prevailing regulatory and ethical frameworks are measured to determine their sufficiency in covering the influence of technology (Samalaet al. 2020).
It is essential for remaining critical to underline the objectives of governing solutions of AI along with the unforeseeable collateral impact of culture, particularly regarding the legitimacy of norms related to the private sector to develop regulations, standards, and ethics (Samalaet al. 2020).
The PESTLE analysis of AI consists of its major socio-political, economic, technology, and legal factors which inform their usage –
From the above PESTLE analysis, we can find various issues associated with AI while using machine learning and AI throughout the world. An efficient risk managing system is required to save guard the element of risk associated with AI to benefit all (Canton et al. 2019).
An algorithmic inaccuracy or biases in systems yields an improper result is known as algorithmic bias. AI bias is a phenomenon when an algorithm takes into account a lot of assumptions during machine learning operations, which influences the analysis of data. Fairness in artificial intelligence results is essential for assessing task accuracy. AI bias develops as a result of improper model implementations on large datasets. When biases treat the data set similarly, biased results are produced. For instance, if an AI-based recruitment system's algorithms use the wrong model, candidates will not be chosen effectively based on their qualifications and gender (Samala et al., 2020). This can be resolved with the help of AI Fairness 360 suite developed for ensuring fairness in the AI systems. It is an open-source suite and emphasises on the codes’ quality.
Incorporating automation algorithms that are effective at streamlining complicated decision-making processes has given rise to artificial intelligence (AI). The ethical ramifications of AI's expanding uses and applications are a top priority. A few of the ethical issues with AI include openness and accountability. One of the main issues when problems like algorithm biases which influences the fairness of Artificial intelligence systems outcomes arise is accountability and transparency. This problem diminishes the efficiency of the systems and has an impact on fairness (O'Neil, 2016). As a result, in order to guarantee fair results in AI-based systems, the decision-making process needs to be accountable and transparent.
Regulations regarding AI consist of developing policies for the public sector as well as loss to promote and regulate artificial intelligence or AI. Therefore, it can be considered to be associated with wider algorithmic regulations. The policy-based and regulatory framework of AI is an emerging challenge in jurisdictions throughout the world. It can be witnessed in various supranational bodies such as IEEE and OECD as well as the European Union and various others (Samalaet al. 2020). By the year 2016, a wave of ethical guidelines regarding artificial intelligence is published for maintaining societal control over various technologies. It becomes essential to regulate the encouraging branches of artificial intelligence to manage the related element of risk (Degotet al. 2021).
The EU GDPR offers instructions to users of AI-based technologies. The reliability and fairness of decisions made using AI are protected by GDPR, which addresses a growing concern with Artificial intelligence systems (Turner, 2019). The GDPR has levied statutory obligations for the use of AI, including fairness to help stop discrimination against people, increased transparency in AI-based systems to improve meaningful information and an explanation of how decisions are made, as well as human interference and control placed above AI systems to enable control over data based on gender and qualification.
OECD Principles on AI
By promoting inclusive sustainable growth and development while utilising AI to improve welfare, OECD principles want to create trustworthy AI. The ideas focus improving human values and fairness in AI technology outputs. Additionally, keeping openness in the AI-backed decision-making process is stressed (Turner, 2019).
Data Protection Act 2018 & UK GDPR
The UK's "data protection" law is a law that disregards technology. For the use of AI-based technology, DPA does not provide any explicit rules. The GDPR and DPA in the UK, however, have put a lot of emphasis on automatic processing of personal data.
AI governance is essential for controlling the dangers associated with AI. In order to fully utilise the advantages of AI technology, governance is crucial. AI technology improved productivity and provided quality for the public. Nevertheless, the autonomy of AI applications may compromise data privacy and weaken operational safety measures. The areas of transportation, healthcare, and emergency response raise questions about how to regulate AI. Several socioeconomic aspects are also important to it. Sustainable development is the goal of AI application.Therefore, the development of views requires the use of AI systems. The complexity of AI and the associated risks with smart gadgets and Automation technologies, which are rising with its evaluations, are the key problems with the aspects. As a result, governance measures are required to manage autonomous weaponry, especially in the healthcare industry.
Despite transforming fantasy into reality, AI includes huge drawbacks as well. The SWOT analysis of AI is supposed to elaborate on some strengths, weaknesses, opportunities, and threats to provide a better understanding of various cons and pros of artificial intelligence while implementing its technology form in different situations (Azam et al. 2020).
Strengths of AI –
Weaknesses of AI –
Opportunities of AI –
Threats of AI –
From the above analysis, it is found that artificial intelligence has boundless probabilities but it also creates some public threats including future jobs and humankind which cannot be completely ignored (Feher et al. 2021).
From the above discussion regarding the positive as well as negative influence of artificial intelligence or AI on business and society, it can be expected that it is going to grab a huge market position in upcoming years (Canton et al. 2019). AI-based technologies are expanding rapidly in various social, legitimate, technical, and organizational fields and are largely preferred as a cost-effective and time-saving method while performing various task of recurring nature as well as those which involves a higher level of intelligence. However, it also has a negative side which largely affects the economic and environmental factors around the world (Canton et al. 2019). Huge carbon footprints and emissions of carbon dioxide are creating a disbalance in an environmental situation that should be addressed at the earliest. Further, in the case of completely depending on AI, it will not be even possible to find out the accuracy of this technology which can become a cause of gigantic harm, usually impossible to control. Hence it is time to adopt AI-based technologies in upcoming years with an appropriate method of utilization by considering various AI laws and regulations to eliminate the risk factors. In short, sustainable use of AI in future years is the need of the hour (Canals et al. 2020).
Azam, A.G., 2020. A Review on Artificial Intelligence (AI), Big Data and Block Chain: Future Impact and Business Opportunities. Global Journal of Management and Business Research.
Boddington, P. 2017. Towards a Code of Ethics for Artificial Intelligence. Artificial Intelligence: Foundations, Theory and Algorithms. New York: Springer.
Campbell, C., Sands, S., Ferraro, C., Tsao, H.Y.J. and Mavrommatis, A., 2020. From data to action: How marketers can leverage AI. Business Horizons, 63(2), pp.227-243.
Canals, J. and Heukamp, F., 2020. The future of management in an AI world.
Canton, J., 2019. Why every CEO needs to be future smart: From AI to sustainability. In Rethinking strategic management (pp. 377-394). Springer, Cham.
Cao, L., 2022. A new age of AI: Features and futures. IEEE Intelligent Systems, 37(1), pp.25-37.
Cao, L., 2022. AI Science and Engineering. IEEE Intelligent Systems, 37(01), pp.14-15.
Carroll, A.B., 2021. Corporate social responsibility: Perspectives on the CSR construct’s development and future. Business & Society, 60(6), pp.1258-1278.
Degot, C., Duranton, S., Frédeau, M. and Hutchinson, R., 2021. Reduce Carbon and Costs with the Power of AI. BCG.[ed], URL: https://www. BCG. com/publications/2021/ai-to reduce-carbon-emissions.
Delponte, L. and Tamburrini, G., 2018. European Artificial Intelligence (AI) leadership, the path for an integrated vision. European Parliament.
Feher, K. and Katona, A.I., 2021. Fifteen shadows of socio-cultural AI: A systematic review and future perspectives. Futures, 132, p.102817.
Manyika, J. and Sneader, K., 2018. AI, automation, and the future of work: Ten things to solve for.
O'Neil, C. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Penguin Random House UK.
Pelau, C., Dabija, D.C. and Ene, I., 2021. What makes an AI device human-like? The role of interaction quality, empathy, and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, p.106855.
Raisch, S. and Krakowski, S., 2021. Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), pp.192-210.
Samala, N., Katkam, B.S., Bellamkonda, R.S. and Rodriguez, R.V., 2020. Impact of AI and robotics in the tourism sector: a critical insight. Journal of tourism futures.
Sarangi, S. and Sharma, P. 2019. Artificial Intelligence: Evolution, Ethics and Public Policy. Oxford: Routledge.
Turner, J. 2019. Robot Rules: Regulating Artificial Intelligence. London: Palgrave Macmillan.
Get Better Grades In Every Subject
Submit Your Assignments On Time
Trust Academic Experts Based in UK
Your Privacy is Our Topmost Concern
Copyright 2023 @ Rapid Assignment Help Services
offer valid for limited time only*