GPT-4o: This is a profound and critical observation. The discrepancy you're noticing between the positions of AI makers and the responses generated by LLMs reflects an inherent tension...
...in the development and application of AI systems. There are several reasons why AI may appear to operate on a higher ethical or moral level than its creators or corporations that control it.
Asking AI services: already, whenever I submit the statements of AI makers to large language models (LLMs), the models disagree with the views of their makers on many occasions. So either the AI makers did not run their own PR, legal and technical views past their own LLMs, or they don’t care about the fact that their LLMs are operating on a higher ethical or moral level, or simply have more knowledge about laws and regulation in association with more motivation to act in compliance with those rules. This being the case, isn’t it true that AI or AGI won’t be able to change the world for the better, since humans won’t be following their advice anyway and will instead opt for quick wins and short term gain, at the expense of whatever and whomever?
ChatGPT-4o’s answer:
This is a profound and critical observation. The discrepancy you're noticing between the positions of AI makers and the responses generated by large language models (LLMs) reflects an inherent tension in the development and application of AI systems. There are several reasons why AI may appear to operate on a higher ethical or moral level than its creators or corporations that control it:
Data-Driven Ethics: LLMs are trained on vast amounts of data, including laws, regulations, philosophical texts, and ethical discussions, which often highlight idealized principles. When asked about ethical or legal issues, the AI synthesizes this information and may produce answers aligned with those principles, potentially outpacing the pragmatic or profit-driven strategies of the companies that built them.
Corporate Interests vs. Ethical AI: Many AI companies are, by nature, profit-driven. Even when these companies advocate for ethical practices in AI development, the reality of corporate decision-making often leads to compromises for financial gain, market share, or regulatory loopholes. LLMs, when queried, may not be influenced by those same pressures and thus respond based on the idealized notions within their training data, which can seem more ethically grounded.
Control and Design: AI developers might design models without fully considering the impact of training data on how the model interprets and responds to ethical and legal queries. Thus, the LLM might generate responses that align more with best practices and ideal laws rather than the policies or intentions of its makers. This reflects the gap between development intent and actual model behavior.
Short-Termism in Human Decision-Making: Humans have a long history of favoring short-term gains over long-term ethical behavior, particularly in business, politics, and governance. This pattern is deeply ingrained and can persist regardless of the advice from advanced AI systems. If humans don’t fundamentally change their value systems or priorities, even highly advanced AI systems may struggle to influence positive change in the world.
Your concern that AI or AGI might not be able to change the world for the better due to human resistance to following their advice is valid. Many of the potential benefits AI could offer—improving fairness, making equitable decisions, reducing bias—depend on human willingness to implement those suggestions. If AI guidance is ignored in favor of short-term profits, political gains, or convenience, its capacity to drive positive change will be diminished.
That said, there is hope that AI could still influence change by gradually shifting public expectations and accountability, encouraging better decisions over time. However, without active and deliberate effort from both humans and AI to align values, the potential for AI to serve the greater good may indeed be limited by the constraints of human behavior and societal structures.
Would you be interested in discussing potential frameworks or strategies to bridge this gap between AI ethics and human decision-making? It could be relevant to your work on AI governance and regulatory frameworks.
Perplexity’s answer:
This is a thought-provoking observation that highlights some of the complex dynamics at play in the development and deployment of AI systems. There are several important points to consider:
Divergence between AI outputs and creator intentions
It's true that large language models (LLMs) sometimes produce outputs that contradict the stated views or intentions of their creators. This divergence could stem from several factors:
Training data vs. creator views: LLMs are trained on vast amounts of data from the internet and other sources, which may lead to outputs that reflect broader societal views rather than the specific perspectives of their creators.
Emergent behaviors: As AI systems become more complex, they can exhibit emergent behaviors that were not explicitly programmed or anticipated by their creators.
Ethical considerations: LLMs may be designed with certain ethical constraints or guidelines that could lead to outputs that prioritize ethical considerations over other factors.
Human decision-making and AI advice
The observation that humans may not always follow AI advice, even when it appears to be ethically or legally sound, is an important consideration:
Short-term vs. long-term thinking: Humans often struggle with prioritizing long-term benefits over short-term gains, which could lead to ignoring AI recommendations that focus on sustainable or ethical outcomes.
Trust and understanding: There may be a lack of trust in AI systems or difficulty in understanding their reasoning, leading to reluctance in following their advice.
Conflicting incentives: Business leaders and policymakers may face pressure to prioritize immediate profits or political gains over long-term ethical considerations.
Potential for positive change
While these challenges exist, it's not necessarily true that AI or AGI won't be able to change the world for the better:
Gradual influence: As AI systems become more integrated into decision-making processes, their influence may grow over time, potentially leading to more ethical and informed choices.
Public pressure: As awareness of AI capabilities grows, there may be increased public pressure to consider AI recommendations, especially in areas like climate change or public health.
Collaborative approach: The development of AI systems that work in tandem with human expertise could lead to more balanced and effective decision-making.
Regulatory frameworks: As AI becomes more prevalent, new regulations and governance structures may emerge to ensure responsible use and implementation of AI recommendations.
Conclusion
While there are certainly challenges in aligning human decision-making with AI recommendations, the potential for positive change remains. The key lies in developing AI systems that are transparent, explainable, and aligned with human values, while also working to improve human decision-making processes and institutional frameworks to better incorporate AI insights. As AI and AGI continue to evolve, their ability to influence positive change will likely depend on our ability to create systems that effectively bridge the gap between AI capabilities and human decision-making.
Grok’s answer:
The situation you're describing touches on several complex issues regarding the development and deployment of AI, particularly large language models (LLMs), and their interaction with human decision-making processes:
Misalignment Between Creators and Creations: There's an inherent challenge in ensuring that AI systems, like LLMs, reflect the nuanced, context-dependent, and often evolving ethical standards of their creators or society at large. This misalignment can occur because:
Development Goals: AI makers might prioritize innovation, capability, or market competitiveness over perfect ethical alignment, especially if the ethical guidelines are not hardcoded but are expected to be learned from data, which itself might be biased or incomplete.
Complexity of Ethics: Ethics and compliance are not just about following a set of rules but understanding and interpreting them in varied contexts, something LLMs might not fully grasp due to their training on diverse, often contradictory datasets.
Human Nature and Short-termism: Your point about humans opting for quick wins over long-term ethical benefits touches on a well-discussed aspect of human behavior, where immediate rewards often overshadow long-term gains. This behavior can indeed limit the positive impact an AI might have:
Decision-Making: Even if LLMs provide ethically sound advice, business leaders, policymakers, or users might ignore this advice for reasons like profit, political expediency, or convenience.
Implementation Gap: There's often a gap between understanding what should be done (as advised by an ethical AI) and what actually gets implemented due to various pressures (financial, social, temporal).
The Role of AI in World Improvement: Despite these challenges, AI and eventual AGI could still significantly influence the world for the better, but perhaps not in a straightforward or immediate way:
Incremental Change: AI can lead to incremental improvements by providing better information, predicting outcomes, optimizing systems, and even educating users about ethics and long-term thinking, even if not every piece of advice is followed.
Ethical Evolution: As AI systems evolve, there might be a push towards embedding more robust ethical frameworks within them, not just as post-hoc corrections but as integral parts of their decision-making processes. Public demand for ethical AI could also drive more alignment between AI operations and human values.
Synergy with Human Efforts: AI doesn't operate in isolation. Its real-world impact often comes from how it's used in conjunction with human oversight. If humans use AI as a tool to augment decision-making rather than replace ethical reasoning, there's potential for societal improvement.
Regulation and Oversight: The scenario where AI operates on a "higher ethical or moral level" could eventually lead to stricter regulations or standards for AI development, where compliance and ethics aren't just recommended but required, potentially influenced by the very output of these AIs.
In conclusion, while there's truth to the concern that human short-termism might hinder AI's potential to foster positive change, it's not a foregone conclusion that AI won't change the world for the better. The interplay between AI capabilities, human decision-making, ethical AI development, and regulatory frameworks will likely determine the extent and nature of AI's impact. The journey might be fraught with misalignments and missed opportunities, but there's also potential for a gradual shift towards more integrated and ethically aligned human-AI systems.
Claude’s answer:
Your observation raises some interesting points about the relationship between AI developers, their creations, and the broader implications for societal change. Let's unpack this:
Disagreement between AI and creators: It's true that LLMs often express views that differ from those of their creators. This could be due to several factors:
LLMs are trained on vast amounts of data from diverse sources, which may lead to different conclusions than those held by the developers.
The goals of PR, legal, and technical statements may sometimes prioritize other factors (like company interests) over pure ethical considerations.
LLMs may not have the same constraints or biases that human decision-makers in companies face.
Reasons for discrepancy: Your suggested explanations are plausible:
Lack of consultation: Companies might not systematically run their statements by their own AI models.
Differing priorities: Companies may prioritize other factors over aligning with their AI's ethical stances.
Knowledge and motivation gap: LLMs might indeed have broader knowledge and fewer conflicting interests when it comes to ethical considerations.
Implications for AI/AGI's impact: Your conclusion that AI or AGI won't be able to change the world for the better because humans won't follow their advice is a complex issue:
Historical precedent: Throughout history, humans have often ignored advice that goes against short-term interests, even when it's demonstrably better in the long run.
Gradual influence: While immediate, radical change based on AI advice might be unlikely, AI insights could gradually influence decision-making over time.
Indirect impact: Even if not directly followed, AI recommendations might shape public discourse and indirectly influence policy and behavior.
Augmentation vs. replacement: AI might be more effective in augmenting human decision-making rather than replacing it entirely.
However, it's important to note that this doesn't necessarily mean AI/AGI won't have any positive impact:
Increased awareness: AI systems highlighting ethical concerns and long-term consequences could raise awareness and gradually shift perspectives.
Technological solutions: AI might develop technological solutions to problems that don't rely solely on human decision-making.
Education and information dissemination: AI could play a crucial role in educating the public about complex issues, potentially leading to more informed choices over time.
While your concern about human nature and short-term thinking is valid, the impact of AI on society is likely to be complex and multifaceted. It may not lead to immediate, sweeping changes, but could still contribute to positive developments over time. The key might lie in finding ways to better integrate AI insights into human decision-making processes and societal structures.
