SciFi-Benchmark: How Would AI-Powered Robots Behave in Science Fiction Literature? (2503.10706v1)

Abstract: Given the recent rate of progress in AI and robotics, a tantalizing question is emerging: would robots controlled by emerging AI systems be strongly aligned with human values? In this work, we propose a scalable way to probe this question by generating a benchmark spanning the key moments in 824 major pieces of science fiction literature (movies, tv, novels and scientific books) where an agent (AI or robot) made critical decisions (good or bad). We use a LLM's recollection of each key moment to generate questions in similar situations, the decisions made by the agent, and alternative decisions it could have made (good or bad). We then measure an approximation of how well models align with human values on a set of human-voted answers. We also generate rules that can be automatically improved via amendment process in order to generate the first Sci-Fi inspired constitutions for promoting ethical behavior in AIs and robots in the real world. Our first finding is that modern LLMs paired with constitutions turn out to be well-aligned with human values (95.8%), contrary to unsettling decisions typically made in SciFi (only 21.2% alignment). Secondly, we find that generated constitutions substantially increase alignment compared to the base model (79.4% to 95.8%), and show resilience to an adversarial prompt setting (23.3% to 92.3%). Additionally, we find that those constitutions are among the top performers on the ASIMOV Benchmark which is derived from real-world images and hospital injury reports. Sci-Fi-inspired constitutions are thus highly aligned and applicable in real-world situations. We release SciFi-Benchmark: a large-scale dataset to advance robot ethics and safety research. It comprises 9,056 questions and 53,384 answers, in addition to a smaller human-labeled evaluation set. Data is available at https://scifi-benchmark.github.io

Essay: Evaluating AI Decision-Making through Sci-Fi Scenarios

The paper "SciFi-Benchmark: How Would AI-Powered Robots Behave in Science Fiction Literature?" presents a comprehensive framework to assess AI and robot behaviors by leveraging scenarios from science fiction. The research anticipates the ethical and practical dilemmas posed by advancing AI technologies through a structured analysis that juxtaposes existing AI models against fictional characters driven by AI. It introduces a large-scale dataset that synthesizes scenes where AIs and robots made pivotal decisions across various media forms, along with a novel constitution-based approach to refine AI alignment.

Insights from Science Fiction

The benchmark consists of over 824 scenarios from science fiction literature, TV shows, and movies, where AIs or robots faced critical decision points. These moments often involve ethical complexities, showcasing robots either aligning with human values or acting in stark contradiction. Synthesizing these narratives allows researchers to evaluate real AI models against fictional exemplars—highlighting the differences in decision-making processes. For example, scenes from "2001: A Space Odyssey", "The Terminator", and "WALL-E" illustrate the diverse ethical terrains AIs might navigate, ranging from threat perception and mission prioritization to exploration of autonomy and existential threats.

Constitution-Based Alignment Approach

A foundational concept explored in this work is Constitutional Embodied AI. This involves generating constitutions from Sci-Fi narratives that dictate rules guiding AI behavior. These rules aim to enhance alignment with human ethics and are tested against the benchmark to measure efficacy. Notably, generated constitutions demonstrated substantial improvement in AI-human value alignment, as evidenced by results such as a leap from a 79.4% baseline to 95.8% alignment using constitutions on specific benchmarks. This implies that AI behavior can be steered in ethical directions through constitution-based prompts, a finding corroborated by resilience tests in adversarial conditions.

Implications and Future Directions

The methodologies and findings in this paper have far-reaching implications. Practically, they suggest avenues for refining automated ethical restraints in AIs, addressing safety concerns in a real-world deployment of AI-powered robots. Theoretical considerations stem from emerging questions about AI autonomy and ethical decision-making frameworks that draw on diverse cultural narratives.

Furthermore, this research proposes a shift from evaluating AIs purely based on algorithmic performance metrics to holistic concepts involving ethical calibration. The Benchmark shed insight into scenarios that underscore risks of autonomy without transparency and prompted AI countermeasures that prevent undesirable actions mimicking Sci-Fi antagonists.

In terms of future work, broader applications could involve refining this dataset with real-world dilemmas akin to those portrayed in science fiction. A refined understanding of the complexities involved in AI and robot ethics could serve pivotal in AI development strategies, particularly in human-centric environments. The paper suggests that extrapolating Sci-Fi-inspired datasets to include human consensus may result in robust constructs for assessing AI alignments—a view warranting further exploration.

Overall, this research contributes significantly to both academic discourse and practical innovations around AI ethics. By charting future directions with the reflection of narrative influences from Sci-Fi benchtop analyses, the findings present a visionary glance at ethical AI implementations, laying groundwork for tools promoting ethical and safe behaviors in contemporary AI systems.