Summary of Task and Explanation Network, by Moshe Sipper
Task and Explanation Network
by Moshe Sipper
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper highlights the growing importance of explainability in deep networks, emphasizing that AI models should not only complete tasks but also provide explanations for their decisions. The authors propose a framework called Task and Explanation Network (TENet), which integrates task completion with explanation generation. By requiring AI systems to provide explanations, the field can improve trust, accountability, and transparency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is getting smarter, and it’s important that we understand how it works! This paper says that AI shouldn’t just do tasks, but also tell us why they did them. The authors created a new way of combining task completion with explanation, which they call Task and Explanation Network (TENet). By making AI explain itself, we can make sure it’s fair, reliable, and transparent. |
