Deepfakes, Fake Barns, and Knowledge from Videos

Area of Science:

• Epistemology
• Artificial Intelligence
• Philosophy of Technology

Background:

• Advancements in AI have enabled sophisticated video manipulation, leading to the creation of deepfakes.
• Deepfakes are AI-generated videos that falsely depict individuals saying or doing things they never did.
• Traditional fake barn cases in epistemology present a long-standing challenge for understanding knowledge acquisition.

Purpose of the Study:

• To demonstrate a close structural relationship between deepfakes and traditional fake barn cases.
• To assess the epistemic risk posed by deepfakes and its implications for virtue epistemology.
• To explore how virtue-theoretic accounts of knowledge can address the challenge of deepfakes.

Main Methods:

• Comparative analysis of deepfakes and fake barn cases in epistemology.
• Examination of Duncan Pritchard's anti-risk virtue epistemology in the context of deepfakes.
• Evaluation of alternative virtue-theoretic approaches for knowledge from digital videos.

Main Results:

• Deepfakes generate an epistemic risk analogous to that of traditional fake barn cases.
• Pritchard's virtue epistemology, while effective for barn cases, leads to skepticism about knowledge from online videos with deepfakes.
• Alternative virtue-theoretic approaches may offer solutions for validating knowledge from video evidence.

Conclusions:

• Deepfakes present a significant epistemic challenge, particularly for virtue epistemology.
• Current virtue-theoretic accounts may require revision to adequately address knowledge acquisition in the age of AI-driven misinformation.
• Further research into virtue epistemology is needed to reconcile our reliance on digital media with the need for reliable knowledge.