ible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
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If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11.
Safeguards
Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [N/A]
Justification: No additional safeguards were required, as the dataset is used strictly for its intended academic purpose of evaluating model performance. The datasets used in our study are all publicly available, originally sourced from YouTube videos. We do not introduce any new high-risk data or models. Our benchmark is constructed purely for academic evaluation purposes and does not contain sensitive, private, or potentially misusable content.
Guidelines:
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The answer NA means that the paper poses no such risks.
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Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
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Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
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We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
12.
Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: All referenced datasets have been properly cited.
Guidelines:
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The answer NA means that the paper does not use existing assets.
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The authors should cite the original paper that produced the code package or dataset.
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The authors should state which version of the asset is used and, if possible, include a URL.
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The name of the license (e.g., CC-BY 4.0) should be included for each asset.
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For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
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If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.
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For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
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If this information is not available online, the authors are encouraged to reach out to the asset’s creators.
13.
New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: All new assets introduced in the paper, including our benchmark dataset and evaluation scripts, are well documented.
Guidelines:
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The answer NA means that the paper does not release new assets.
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Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
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The paper should discuss whether and how consent was obtained from people whose asset is used.
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At submission time, remember to a