Introduction to Adaptive Algorithms

Initial Collaborative Discussion Post

The rise of deep learning models may feel like an old story, simply because the pace of innovation has been so fast. Image generation has evolved from the amusing experiments of early tools like DALL-E 2, which produced strange but entertaining images to share with friends, into a flood of platforms and mobile applications capable of creating content so realistic that scrolling through social media now demands extra attention just to be able to identify whether or not what we see is real.

This increase in realism raises serious ethical concerns. Deepfakes and other generated media blur the boundaries of authenticity, threatening trust in online communication and traditional media content, since journalists are also having a hard time identifying fabricated content (Thomson et al., 2025). This uncertainty can have psychological consequences, especially for teenagers whose self-image is warped by online media exposure (Antonina, 2025). Older adults may also struggle to navigate increasingly deceptive online platforms, deepening their digital exclusion and confusion (Harris et al., 2022).

Another ethical dilemma lies in AI-generated music. In 2023 a song using cloned vocals of Drake and The Weeknd began circulating social media platforms and quickly went viral without anyone noticing its inauthenticity (The Guardian, 2023). This highlights questions of consent, authorship, and artistic integrity. The situation is even more challenging for smaller musicians, having to compete with algorithms capable of producing entire albums overnight.

In conclusion, while deep learning technologies have been showcasing remarkable creativity recently, they also demand urgent ethical attention and a legal framework that protects existing art and artists. Issues of authenticity, mental health, and artistic fairness must be addressed if society wishes to benefit from AI without forgetting the very idea of creativity.