Concerns about Transparency and Bias

Lately, there’s been a growing skepticism about the ethics behind AI, and it’s easy to see why. For one, transparency is a significant issue. Many AI systems, especially the more advanced ones, operate like black boxes. It’s difficult to understand how they make decisions, which raises questions about accountability and trust.

Bias in AI is another major concern. These systems learn from data, and if that data is biased, the AI will be too. This doesn’t just lead to biased decisions; it can spill over into real life, influencing how younger generations view different social groups. When AI systems consistently reflect and reinforce existing prejudices, it can shape perceptions and attitudes, leading to a more divided and unequal society. This is particularly concerning for younger people who are still forming their understanding of the world and can be heavily influenced by biased technology they interact with daily.

Concerns about Creativity and Concentration of Power

The way AI models are trained is also problematic. These models often rely on vast amounts of data scraped from the internet, much of which is protected by copyright. The use of copyrighted content without proper consent or compensation raises significant legal and ethical issues. Content creators, such as writers and musicians, are understandably worried that their work is being exploited without recognition or remuneration, which could stifle creativity and innovation.

There’s also the issue of power concentration. A few tech giants lead in AI research and development and control vast amounts of data. This concentration of power raises concerns about monopolistic practices and the potential for these entities to influence markets and societies in ways that may not align with the public good.

Even though ethical guidelines for AI are being developed, I’m skeptical about their effectiveness. Voluntary guidelines and self-regulation by companies often lack enforcement mechanisms, making them insufficient to address the ethical challenges posed by AI. There is a growing call for robust regulatory frameworks to ensure that AI development and deployment are aligned with ethical principles. (Elon Musk: https://www.youtube.com/watch?v=qFJaTG_A3NM)

Notes

  1. Although not for the end user, there is technically a way to see what the AI is thinking, observing the feedback loops: https://arxiv.org/pdf/1706.03741. But again, LLMs are just generating tokens, the black box I’m referring to is both, the training data and the trained model.
  2. Something interesting to think about, and probably write about later is how bias gets amplified. If we have a system (in this case, AI) which is biased, every interaction with the system has the potential to spread that bias. And within a few generations, the bias gets amplified (Btw, this is exactly how NNs work as well, and it’s even called bias)