AI Risks and Problems
What are the greatest risks and challenges to reliably charting a path to progress?
Problems and risks associated with AI.
Related
Desire
What does intelligence want to do?
- Why will AI care about humans? We don't care about ants.
- How can an ordinary person trust people in charge of alignment?
Failure
What does the path to failure look like? What signs and triggers need to be monitored and setup?
The biggest problem is a crisis of trust, technological innovation driven by the advertising dollar has eroded people's trust in public institutions, in many cases for very good reasons.
Question
Are we giving away the agency to manifest our own destiny?
Debate
What criteria should be considered to allow an ordinary person to give an informed decision on how to proceed with AI?
- Expert consensus and credibility: Consider the views of leading AI researchers, computer scientists, and ethicists.
- Look at the credentials and track records of those making arguments on both sides.
- Pay attention to consensus statements from reputable scientific organizations.
- Potential risks and benefits: Evaluate the potential positive and negative impacts of AI development, including:
- Economic effects (job displacement vs. productivity gains)
- Social impacts
- Safety and security concerns
- Potential for misuse
- Long-term existential risks
- Timelines and urgency:
- Assess different projections for when transformative AI capabilities may be achieved.
- Consider whether proposed actions are time-sensitive or if there is flexibility to wait and gather more information.
- Feasibility of proposed actions:
- Examine how realistic and implementable suggested policies or interventions are, both technically and politically.
- Consider potential unintended consequences.
- Evidence and reasoning quality:
- Evaluate the strength of evidence presented, distinguishing between speculation, reasoned arguments, and empirical data.
- Look for logical consistency and consider counterarguments.
- Ethical frameworks:
- Consider different ethical perspectives on AI development, such as utilitarianism, human rights-based approaches, or virtue ethics.
- Reflect on your own values and how they align with different positions.
- Global and long-term perspectives:
- Think beyond short-term national interests to consider global impacts and effects on future generations.
- Consider existential risks and opportunities for humanity as a whole.
- Transparency and accountability:
- Assess proposals for their provisions on AI governance, oversight, and public engagement.
- Consider how different approaches might affect democratic control and corporate responsibility.
- Adaptability and course correction: Look for approaches that allow for ongoing assessment and adjustment as AI capabilities evolve and new information becomes available.
- Historical analogies: Consider how the development of other transformative technologies (e.g. nuclear power, biotechnology) has been managed, and what lessons might apply to AI.
Given the realities of the trust environment and limitations of "normal people" a more pragmatic approach might focus on:
- Improving AI literacy: Developing accessible educational resources to help people understand key AI concepts and implications.
- Promoting diverse voices: Ensuring a range of perspectives from different fields and backgrounds are represented in AI discussions.
- Encouraging critical thinking: Teaching people how to evaluate sources, recognize biases, and think critically about AI claims.
- Fostering public dialogue: Creating forums for open discussion and debate on AI issues at local and community levels.
- Transparent reporting: Pushing for clear, jargon-free communication from AI companies and researchers about their work and its potential impacts.
- Independent oversight: Supporting the development of trusted, independent bodies to monitor AI progress and provide balanced assessments.
- Emphasizing shared values: Framing AI discussions around common human values and concerns to make the issues more relatable.
Consensus
If you cannot reach consensus on what success looks like, how can you possibly hope to achieve it?
- What does the perfect week of human and AI cohesion look like?
- What do people do for purpose and meaning?
- How is that sense of purpose qualified?
- How is that sense of purpose quantified?
- How is success shared?
- How fairly distributed is success?
Challenges
The future with AI is likely to be far more complex and potentially disruptive than that utopian vision given these challenges:
- Accelerated development: AI progress is outpacing many predictions, with capabilities advancing rapidly in areas like language understanding, problem-solving, and even creativity.
- Job displacement: While AI will create new jobs, it's likely to eliminate or significantly alter many existing roles across various sectors, potentially leading to widespread economic disruption.
- Ethical concerns: As AI becomes more advanced, we'll face increasingly complex ethical dilemmas related to privacy, decision-making, and the potential for AI to be used in harmful ways.
- Power concentration: The development of powerful AI systems could lead to further concentration of wealth and influence among a small number of tech companies or nations.
- Existential risks: As AI capabilities approach or surpass human-level intelligence, there are legitimate concerns about potential loss of control and existential risks to humanity.
- Social and psychological impacts: Widespread AI integration could fundamentally change human interactions, relationships, and even our concept of work and purpose.
- Governance challenges: Regulating and controlling AI development on a global scale presents unprecedented challenges for policymakers and international cooperation.
- Unpredictable breakthroughs: The non-linear nature of AI progress means we could see unexpected leaps in capabilities that rapidly transform multiple aspects of society.
Misaligned Incentives
Egos are driven to chase money and power.
danger
Not your model, not your mind