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.
Published in Chapter:
An In-Depth Qualitative Interview: The Impact of Artificial Intelligence (AI) on Privacy Challenges and Opportunities
Sharon L. Burton (Capitol Technology University, USA),
Darrell N. Burrell (Marymount University, USA),
Yoshino W. White (Florida State University, USA), Calvin Nobles (Illinois Institute of Technology, USA),
Maurice E. Dawson (Illinois Institute of Technology, USA),
Kim L. Brown-Jackson (Illinois Institute of Technology, USA),
S. Rachid Muller (Arizona State University, USA), and
Dustin I. Bessette (Mt. Hood Community College, USA)
Copyright: © 2024
|Pages: 21
DOI: 10.4018/979-8-3693-1127-1.ch002
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.