On the Consideration of AI Openness: Can Good Intent Be Abused?
Not on the map. Why?
Abstract (arXiv)
Open source is a driving force behind scientific advancement.However, this openness is also a double-edged sword, with the inherent risk that innovative technologies can be misused for purposes harmful to society. What is the likelihood that an open source AI model or dataset will be used to commit a real-world crime, and if a criminal does exploit it, will the people behind the technology be able to escape legal liability? To address these questions, we explore a legal domain where individual choices can have a significant impact on society. Specifically, we build the EVE-V1 dataset that comprises 200 question-answer pairs related to criminal offenses based on 200 Korean precedents first to explore the possibility of malicious models emerging. We further developed EVE-V2 using 600 fraud-related precedents to confirm the existence of malicious models that can provide harmful advice on a wide range of criminal topics to test the domain generalization ability. Remarkably, widely used open-source large-scale language models (LLMs) provide unethical and detailed information about criminal activities when fine-tuned with EVE. We also take an in-depth look at the legal issues that malicious language models and their builders could realistically face. Our findings highlight the paradoxical dilemma that open source accelerates scientific progress, but requires great care to minimize the potential for misuse. Warning: This paper contains content that some may find unethical.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 11 Mar 2024.
Builds on
15 of 28 references.
References · 15
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
- 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2024OLMo: Accelerating the Science of Language Modelscited by 599
- 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 505
- 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
- 2021ZeRO-Offload: Democratizing Billion-Scale Model Trainingcited by 339
- 2023The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Datasetcited by 148
- 2023KoBBQ: Korean Bias Benchmark for Question Answeringcited by 71
- 2022Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Modelscited by 67
- 2023Managing extreme AI risks amid rapid progresscited by 48
3 more
- 2022A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Predictioncited by 44
- 2023KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applicationcited by 24
- 2023SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaborationcited by 13
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Citing papers per year: 2024: 2.
2 papers.