Abstract
AI (artificial intelligence) is impacting privacy positively and negatively. While AI has the potential to enhance privacy through improved security measures and data protection, it is creating new types of digital privacy harms. The chapter focuses on privacy risks and challenges (i.e., data breaches, profiling and surveillance, algorithmic bias, deepfakes and technology, and predictive analytics), including how AI can bolster security measures and data protection. Regulatory responses, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), are discussed as attempts to address these challenges. Through this chapter, this researcher applies a qualitative and in-depth interview methodology and design to explain the multifaceted relationship between AI and privacy in the digital age. Research results offer avenues to address privacy risks and challenges. Benefactors of this research are practitioners, academics, and learners in AI, cybersecurity, and criminology/criminal justice.
TopIntroduction
Artificial Intelligence (AI) offers the transformative potential to democratize access to rare skills, invaluable insights, and vast reservoirs of knowledge, ushering in a new era of possibilities. This technological advancement holds the key to numerous positive outcomes benefiting society in multifaceted ways. First, AI empowers healthcare professionals and researchers to enhance disease diagnosis and treatment (Kumar et al., 2023). By swiftly analyzing complex medical data, AI systems can assist in early disease detection, recommend personalized treatment plans, and even predict outbreaks, ultimately saving lives and improving healthcare accessibility worldwide (Kumar et al., 2023). AI-driven innovations revolutionize product and service accessibility (Johnson et al., 2022). According to Johnson et al., AI breaks down language barriers and facilitates inclusive access to information and technology through automated language translation, voice assistants, and adaptive user interfaces. This inclusivity extends to the differently-abled, making digital content and services more accessible to disabled individuals (Henneborn, 2023).
Furthermore, AI accelerates the pace of work across industries, not just for the sake of progress, but for the betterment of society. Automation and intelligent process optimization reduce manual labor, streamline operations, and boost productivity (Johnson et al., 2022). This shift consequently allows human resources to concentrate on creative and strategic initiatives, encouraging innovation and promoting economic development (Henneborn, 2023). Additionally, AI-driven data analytics and predictive modeling empower businesses to make informed decisions, optimize supply chains, and create tailored customer experiences, increasing efficiency and competitiveness (Burton, 2022; Demirel, 2022; Simon, 2019). AI's democratizing influence extends far and wide, fostering advancements in healthcare, technology, and other sectors; accessibility, productivity, innovation, and economic prosperity (Kumar et al., 2023; Ryck et al., 2020; von Gravrock, 2022). This technology is a powerful force for positive change, poised to reshape our lives and work for the better.
Recent advancements in AI, particularly in machine learning and deep learning, have significantly enhanced its capabilities, allowing for real-world applications that were once deemed unfeasible. These technologies now play a critical role in sectors ranging from autonomous driving and personalized medicine to advanced manufacturing and intelligent urban planning, laying the groundwork for this paper's exploration of AI's broader societal implications (Gruetzemacher & Whittlestone, 2022). Also, these cutting-edge technologies have enabled AI systems to handle complex datasets and perform tasks with precision and efficiency that mimic human cognitive functions, thus opening up new avenues for research and application that directly impact societal infrastructure and public services (Xu & Gao, 2024). This evolution is a crucial backdrop for discussing the dual-edged nature of AI's influence on society.
Key Terms in this Chapter
In-Depth Interviewing: In-depth interviewing is a qualitative research method that involves conducting detailed and open-ended interviews with individuals or subjects to gain a deep understanding of their perspectives, experiences, and opinions on a particular topic. It is often used in social sciences and market research.
General Data Protection Regulation (GDPR): The General Data Protection Regulation is a comprehensive European Union (EU) regulation that governs the protection of the personal data of EU citizens. It establishes rules for the collection, processing, and storage of personal data and grants individuals control over their data, including the right to access, rectify, and erase their information.
Predictive Analytics: Predictive analytics is the process of using data analysis and statistical algorithms to make predictions or forecasts about future events, outcomes, or trends based on historical data. It is commonly used in business for decision-making, risk assessment, and resource optimization.
Qualitative Research: Qualitative research is a research method that focuses on gathering non-numerical data, such as text, images, or narratives, to explore and understand social phenomena, human behaviors, and motivations. It aims to provide insights into the context and meaning behind various phenomena, often through methods like interviews, observations, or content analysis.
Fairness and Bias Mitigation: Fairness and bias mitigation refers to efforts made in AI and machine learning to ensure that algorithms and models do not discriminate against individuals or groups based on race, gender, age, or other protected attributes. It involves techniques and strategies to reduce and eliminate biases in data and algorithms.
AI Algorithms: AI (Artificial Intelligence) algorithms are sets of rules and statistical models that enable computers and machines to perform tasks or make decisions without explicit programming. These algorithms are used in various AI applications, such as machine learning and deep learning, to process data and generate intelligent responses or predictions.
California Consumer Privacy Act (CCPA): The California Consumer Privacy Act is a state-level privacy law in California, USA, designed to protect the personal information of California residents. It grants consumers certain rights over their personal data, including the right to know what data is collected, request deletion of data, and opt-out of the sale of their data.
Deepfake and Technology: Deepfake technology refers to the use of deep learning algorithms, particularly generative adversarial networks (GANs) and other advanced machine learning techniques, to create highly realistic and convincing fake videos, audio recordings, or other forms of digital media. This technology can manipulate and synthesize content in a way that makes it appear as if it is generated by real individuals, often including celebrities or public figures. Deepfake technology can be used to superimpose someone's likeness onto another person's body in videos, alter their facial expressions or voice, and create fabricated content that can be challenging to distinguish from genuine recordings. It has raised concerns due to its potential for misuse in creating deceptive or fraudulent content, spreading misinformation, and compromising the credibility of digital media.