Ethics AI News & Updates
Anthropic Launches Research Program on AI Consciousness and Model Welfare
Anthropic has initiated a research program to investigate what it terms "model welfare," exploring whether AI models could develop consciousness or experiences that warrant moral consideration. The program, led by dedicated AI welfare researcher Kyle Fish, will examine potential signs of AI distress and consider interventions, while acknowledging significant disagreement within the scientific community about AI consciousness.
Skynet Chance (0%): Research into AI welfare neither significantly increases nor decreases Skynet-like risks, as it primarily addresses ethical considerations rather than technical control mechanisms or capabilities that could lead to uncontrollable AI.
Skynet Date (+1 days): The focus on potential AI consciousness and welfare considerations may slightly decelerate AI development timelines by introducing additional ethical reviews and welfare assessments that were not previously part of the development process.
AGI Progress (+0.03%): While not directly advancing technical capabilities, serious consideration of AI consciousness suggests models are becoming sophisticated enough that their internal experiences merit investigation, indicating incremental progress toward systems with AGI-relevant cognitive properties.
AGI Date (+1 days): Incorporating welfare considerations into AI development processes adds a new layer of ethical assessment that may marginally slow AGI development as researchers must now consider not just capabilities but also the potential subjective experiences of their systems.
Experts Question Reliability and Ethics of Crowdsourced AI Evaluation Methods
AI experts are raising concerns about the validity and ethics of crowdsourced benchmarking platforms like Chatbot Arena that are increasingly used by major AI labs to evaluate their models. Critics argue these platforms lack construct validity, can be manipulated by companies, and potentially exploit unpaid evaluators, while also noting that benchmarks quickly become unreliable as AI technology rapidly advances.
Skynet Chance (+0.04%): Flawed evaluation methods could lead to overestimating safety guarantees while underdetecting potential control issues in advanced models. The industry's reliance on manipulable benchmarks rather than rigorous safety testing increases the chance of deploying models with unidentified harmful capabilities or alignment failures.
Skynet Date (-1 days): While problematic evaluation methods could accelerate deployment of insufficiently tested models, this represents a modest acceleration of existing industry practices rather than a fundamental shift in timeline. Most major labs already supplement these benchmarks with additional evaluation approaches.
AGI Progress (0%): The controversy over evaluation methods doesn't directly advance or impede technical AGI capabilities; it primarily affects how we measure progress rather than creating actual capabilities progress. This primarily highlights measurement issues in the field rather than changing the trajectory of development.
AGI Date (-1 days): Inadequate benchmarking could accelerate AGI deployment timelines by allowing companies to prematurely claim success or superiority, creating market pressure to release systems before they're fully validated. This competitive dynamic incentivizes rushing development and deployment cycles.