Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

1. Automation of Routine Tasks: AI-powered automation can take the place of human workers in the more accurate and efficient performance of repetitive and routine tasks. Data entry, customer service, transportation, and other industries are all impacted by this automation, which could result in job losses there. 2. Industry Disruption: By replacing human labor with AI systems and robots, technological advances in AI have the potential to upend whole sectors. Self- driving cars, for instance, may have an influence on the delivery and transportation industries, while AI- powered chatbots may eliminate the need for customer support agents. 3. Skill Requirements and Job Transformation: AI technology integration in the workplace frequently necessitates the development of new skills and competences. Jobs requiring repetition and manual labor may become less popular, while those requiring knowledge of AI programming, data analysis, and machine learning may become more popular. If a worker does not possess the requisite abilities for the new job market, this change in skill requirements may result in their unemployment. 4. Structural Unemployment: AI-driven automation may result in structural unemployment, as workers struggle to move from lagging to growing industries. Displaced people may need some time to develop the skills required for new employment possibilities, which could lead to underemployment or unemployment. o) Cyber Security Issues due to Artificial Intelligence While artificial intelligence (AI) has many advantages, it also poses certain cybersecurity risks. The following are some significant AI-related cyber security concerns: 1. Adversarial Attacks: When hostile individuals deliberately alter input data to fool or confuse an AI system, AI models are susceptible. Data categorization errors brought on by these assaults may jeopardize the integrity and dependability of AI- powered systems. 2. Breach of Data Privacy: The functioning and training of AI rely heavily on data. Unauthorised access, disclosure, or misuse of sensitive information can be caused by insufficient data protection mechanisms, which can result in privacy violations and identity theft. 3. AI-Powered Cyberattacks: Cybercriminals can use AI technology to carry out sophisticated and automated cyberattacks. AI-enabled malware and botnets are more difficult to protect against since they can alter their behavior, avoid detection, and exploit weaknesses more effectively. 4. Bias and Discrimination: Discriminatory outcomes might result from biases in the training data that AI algorithms can inherit. This might have serious ramifications for hiring, lending, and law enforcement, reinforcing prejudices in society and raising ethical questions. 5. Lack of Transparency and Explainability: A subset of AI called deep learning algorithms frequently operates as a "black box," making it challenging to comprehend the decision-making process. Concerns regarding accountability and the capacity to identify and correct potential biases or weaknesses are brought up by this lack of openness. p) Accountability Issues due to Artificial Intelligence Concerns about accountability are very important when using artificial intelligence (AI). The following are some significant AI-related accountability concerns: 1. Lack of Clear Accountability: Assigning unambiguous accountability for the decisions and actions taken by AI systems can be difficult because to the involvement of numerous stakeholders, including developers, data scientists, and organizations using AI systems. This raises questions about who should be responsible for any undesirable outcomes or mistakes. 2. Unintentional Biases and Discrimination: AI systems have the potential to reinforce biases found in training data, producing discriminating results. It becomes difficult to hold people or organizations responsible for biased decisions produced by AI systems because doing so requires identifying and fixing the biases present in the data and algorithms. 3. Transparency and Explainability: Deep learning models frequently function as "black boxes," making it challenging to comprehend the decision-making process. Accountability can be hampered by a lack of transparency and explicability since it becomes difficult to track and defend the logic underlying AI- driven actions. 4. Legal and Regulatory Issues: The specific issues that AI technologies provide may not be sufficiently addressed by current legal systems. In situations where AI systems are engaged, determining legal liability, and defining precise guidelines for accountability can be challenging and call for legislative reforms. 5. Ethics: AI systems have the potential to have significant effects on people and society. Clear accountability frameworks and adherence to ethical principles are necessary for ensuring ethical usage of AI and addressing ethical issues like privacy violations, data exploitation, or biased decision- making. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 31 ( )D Year 2023 Journey of Artificial Intelligence Frontier: A Comprehensive Overview