⬥ Level of Understanding
These are more of a sketch, a brain dump of the most relevant areas as I see them. I hope to elaborate on each of these individually in the future.
The following is a collection of problematic observations around the mass adoption of generative AI. This list is by no means exhaustive, and it does not present any clear-cut solutions. Rather, this list serves as a framework for addressing the complex issues surrounding this topic. Each point merely provides a foundation for further discussion and exploration. The problems outlined here are in part addressed in my AI guidelines and broader intentions.
- Curation: as the volume of AI-generated content increases, curating "good" or "relevant" material becomes complex and subjective. Existing systems may struggle to cater to diverse opinions and perspectives, requiring a multifaceted approach, such as expert reviews, user feedback, and community-driven platforms for more effective curation.
- Copyright and consent: the explosion of AI-generated content can muddy the waters when it comes to copyright and consent. It becomes imperative to establish frameworks for attribution, licensing, and fair use. Additionally, explicit consent from individuals whose data is used for training is non-negotiable for some resemblance of ethical standards.
- Identity crisis in art: in a world where AI can whip up a masterpiece with a few lines of code, we have to question — what’s the point? Generative AI pushes us to reexamine why we create in the first place and what truly gives our art soul and meaning. It’s not about skill versus machine; it’s about rediscovering the essence of our artistic journey.
- Access and control: the democratization of AI technology is crucial to prevent a concentration of power. Open-source technology and broad educational initiatives can facilitate a more equitable distribution of AI capabilities.
- Entrenched biases: the data used to train AI can inadvertently perpetuate existing norms and biases, especially concerning beauty standards and cultural viewpoints. Efforts must be made to curate diverse and fair training data. This is an incredibly complicated process and requires international cooperation.
- Risk of apathy: generative AI has both the power to elevate human potential, but also the risk of falling into complacency in a world where AI can do everything "better". History often shows us that while tools can be used to enrich our lives, they can also be misused to evade personal growth and work. With AI, the stakes are higher and the point is not whether AI can perform tasks better than humans, but whether we are leveraging it to expand our own horizons or allowing it to narrow them.
- Ethical and environmental footprint: in the AI race to the top, we are consuming enormous amounts of energy, exacerbating the planet’s already dire climate situation. But it doesn’t stop there. The AI revolution is standing on the shoulders of underprivileged communities. The AI revolution is increasingly being powered by the sweat of underpaid, exploited workers. It’s their hands that are laboriously tagging data sets for machine learning algorithms. This revolution is not just an environmental concern, it’s a social justice issue as well.
