Summary of Addressing Social Misattributions Of Large Language Models: An Hcxai-based Approach, by Andrea Ferrario et al.
Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach
by Andrea Ferrario, Alberto Termine, Alessandro Facchini
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework is proposed to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. The Social Transparency (ST) framework aims to make socio-organizational context accessible to users, but its limitations are discussed, highlighting potential mismatches between designers’ intentions and users’ perceptions. To mitigate these issues, a new ‘W-question’ is proposed to clarify social attributions assigned by designers and users, promoting ethically responsible development and use of LLM-based technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make AI explanations more transparent and accountable. It’s about making sure that AI systems don’t misunderstand people’s social roles or intentions. The researchers are worried that Large Language Models might be used in a way that manipulates people’s emotions or promotes unfair behavior. To fix this, they suggest adding a new question to the Social Transparency framework, which would help designers and users agree on what kind of social role or intention a language model is intended for. |
Keywords
» Artificial intelligence » Language model » Machine learning
