Ethical Considerations of Using AI

Before we examine AI ethics specifically, it's important to understand ethics itself from a conceptual perspective. Ethics are the principles and moral values that guide an individual's or group's behavior and judgments about what is right and wrong. Over many years, humanity has arrived at certain ethical principles that have structured our understanding of good and evil. However, these ethics are constantly evolving as we collectively grow and face new challenges.

AI ethics is becoming an increasingly interesting and complex domain. As AI systems advance from rule-based to reasoning-based models like large language models (LLMs) and move us closer to artificial general intelligence (AGI) – the hypothetical ability of an AI system to have human-level intelligence and potentially sentience – our ethical considerations must evolve in parallel.

One of the most contentious ethical issues in AI is the potential for discrimination and bias. AI systems are trained on data that reflects the biases and worldviews present in society. This can result in the AI perpetuating and amplifying biases related to gender, race, and other attributes. Addressing this issue is extremely challenging, as it requires either "unlearning" these biases from the AI or meticulously cleaning the training data before ingestion – a monumental task.

Another critical ethical concern is the lack of explainability and transparency in modern AI systems, especially LLMs. When an AI provides an output, it is often difficult to trace the exact sources and reasoning behind that output. This lack of transparency raises questions around accountability, governance, and even potential copyright infringement if the AI's output incorporates copyrighted material in an unattributed way.

The use of AI in weapons and autonomous systems is also a deeply troubling ethical quandary. Are we training these systems to avoid harming civilians, women, and children? Or are we creating emotionless, ruthless "soldiers" with no ethical constraints? The potential for such powerful technologies to be used indiscriminately is terrifying.

Environmental impact is another underappreciated ethical consideration. The immense computational power and data centers required to train and run large AI models like LLMs have a staggering carbon footprint and water usage. For example, training a single LLM model can consume as much water as is required to produce 370 BMW cars. As we increasingly rely on AI for even minor tasks, we must question whether the environmental cost is justified.