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

an advanced AI model that operates as a "black box," making it difficult to comprehend how it makes decisions. Concerns regarding potential biases, mistakes, or unethical decision-making are brought up by this lack of openness. To solve this problem and give people the capacity to comprehend and query AI outputs, efforts are being made to develop interpretable AI approaches and explainable AI methodologies. The privacy issue is very important in the context of AI. In order to be trained and perform better, AI systems frequently need access to enormous volumes of personal data. This data's collection, storage, and processing raise concerns about data security, consent, and the possibility of abuse. A careful balance must be struck between protecting individual private rights and using data for AI development. To do this, strict privacy laws, data anonymization methods, and informed permission mechanisms are needed. A wider range of ethical issues are connected to AI. Concerns regarding mass surveillance and privacy invasion are raised by the emergence of AI-powered surveillance technology. Deepfake technology raises moral questions regarding false information and its capacity to sway public opinion because it can produce incredibly realistic fake media. The outsourcing of the ability to make fatal decisions to machines to autonomous weapons powered by AI raises ethical concerns. l) Security and Transparency Issues due to Artificial Intelligence AI technology brings both security and transparency challenges that need to be addressed. Here are some key issues related to security and transparency in AI: 1. Security Vulnerabilities : AI systems can be susceptible to attacks, such as adversarial attacks, where malicious actors manipulate inputs to deceive or mislead AI algorithms. These attacks can have serious consequences, especially in critical domains like finance, healthcare, and autonomous vehicles. 2. Privacy Concerns: AI systems often rely on collecting and analyzing large amounts of data, raising concerns about the privacy of individuals. It is crucial to handle and protect sensitive data appropriately to ensure privacy rights are respected. 3. Bias and Fairness: AI systems trained on biased data can perpetuate and amplify existing biases, leading to unfair outcomes and discrimination. Ensuring fairness and mitigating bias in AI algorithms is a significant challenge that requires careful attention. 4. Explainability and accountability: Many AI algorithms, particularly those based on deep learning, are often regarded as black boxes, making it challenging to understand their decision-making process. It is crucial to develop methods for explaining AI decisions and making algorithms more transparent to ensure accountability and build trust. 5. Cybersecurity Risks: The increasing use of AI in critical infrastructure and autonomous systems introduces new cybersecurity risks. AI-powered systems may become targets for cyber-attacks, leading to potential disruptions and threats to safety and security. m) Socio-Economic Issues due to Artificial Intelligence Numerous socio-economic difficulties are raised by the broad usage of AI technology and must be taken into consideration. Here are a few major issues: 1. Employment Displacement: As machines begin to perform some functions that have historically been done by people, it is possible that AI automation will disrupt entire industries and result in employment loss. This may lead to unemployment and economic inequality, especially for those with routine or low- skill jobs. 2. Skills Gap: The quick development of AI technology necessitates a workforce with the abilities to comprehend, create, and oversee AI systems. The demand for AI-related talents, however, outpaces the supply, and this skills gap is widening. This emphasizes the necessity for programs that help people upskill and reskill in order to ensure that they are prepared for the occupations of the future. 3. Economic Inequality: AI can make already existent economic inequities worse. Access to AI technology and data may provide organizations a competitive edge, which could result in a concentration of power and money. Addressing these disparities is essential to ensuring that the advantages of AI are distributed more fairly. 4. Algorithmic Bias and Discrimination: AI systems may unintentionally reinforce prejudices found in the training data, producing discriminating results. These include hiring, lending, and the criminal justice system. Fairness assurance and algorithmic discrimination prevention are a serious problem that necessitates rigorous AI system design and evaluation. 5. Ethics: The ethical ramifications of AI create significant socioeconomic issues. It is crucial to give critical thought to and act on issues like privacy, consent, accountability, and transparency. To direct the development and application of AI technology in a responsible and socially beneficial manner, ethical frameworks and guidelines are required. n) Unemployment Issues due to Artificial Intelligence The development of AI technology has sparked worries about potential job loss and unemployment. Here are some important causes of the problems with AI-related unemployment. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 30 ( ) Year 2023 D Journey of Artificial Intelligence Frontier: A Comprehensive Overview