AI Failures: A Review of Underlying Issues
Instances of Artificial Intelligence (AI) systems failing to deliver consistent, satisfactory performance are legion. We investigate why AI failures occur. We address only a narrow subset of the broader field of AI Safety. We focus on AI failures on account of flaws in conceptualization, design and deployment. Other AI Safety issues like trade-offs between privacy and security or convenience, bad actors hacking into AI systems to create mayhem or bad actors deploying AI for purposes harmful to humanity and are out of scope of our discussion. We find that AI systems fail on account of omission and commission errors in the design of the AI system, as well as upon failure to develop an appropriate interpretation of input information. Moreover, even when there is no significant flaw in the AI software, an AI system may fail because the hardware is incapable of robust performance across environments. Finally an AI system is quite likely to fail in situations where, in effect, it is called upon to deliver moral judgments -- a capability AI does not possess. We observe certain trade-offs in measures to mitigate a subset of AI failures and provide some recommendations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks
As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are failures of the benchmark itself: broken specifications, implicit assumptions, and rigid evaluation scripts t…
A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends
The complex dependencies and propagative faults inherent in microservices, characterized by a dense network of interconnected services, pose significant challenges in identifying the underlying causes of issues. Prompt i…
Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
Large language models (LLMs) frequently generate defective outputs in code generation tasks, ranging from logical bugs to security vulnerabilities. While these generation failures are often treated as model-level limitat…
Code GenerationThe problem with DDPG: understanding failures in deterministic environments with sparse rewards
In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in environments that seem trivial, but the reaso…
Reinforcement LearningPredicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
In principle, Continuous Integration (CI) pipeline failures provide valuable feedback to developers on code-related errors. In practice, however, pipeline jobs often fail intermittently due to non-deterministic tests, ne…
Few-Shot Learning