Update README.md

README.md

@@ -15,7 +15,7 @@ The named authors collected, validated and categorized this dataset. They were p
 
 <strong>Composition and Collection Process<br></strong>
 
-The instances in this dataset represent a project that develops or uses AI in systems for the public interest. It does not contain information about individual people. For us this means that it serves “the long term survival and well-being of a collective, construed as a public”, which is a definition by Barry Bozeman (2007) and is not profit driven at its core. A more detailed description of the understanding about what is “public interest” you can find in this paper, that reflects on public interest theory in its relation to AI, or on the website of the project. 
+The instances in this dataset represent a project that develops or uses AI in systems for the public interest. It does not contain information about individual people. For us this means that it serves “the long term survival and well-being of a collective, construed as a public”, which is a definition by Barry Bozeman (2007) and is not profit driven at its core. A more detailed description of the understanding about what is “public interest” you can find in this <a href="https://link.springer.com/article/10.1007/s00146-022-01480-5">paper</a>, that reflects on public interest theory in its relation to AI, or on the <a href="https://publicinterest.ai/">website</a> of the project. 
 The dataset is a not representative sample of projects. However, it is the biggest such collection of public interest AI projects that we know of, since it requires a lot of detailed research and work. 
 The projects were collected in two manners. The first methodological approach was a network-based search method (called “Network Search” in the Dataset) to identify relevant projects. Leveraging the professional networks and research of the research group members, information about projects was collected through research, direct communication and referrals. Any project identified through these interactions was documented and vetted against the minimal definition in the dataset. The categorization was based on the online documentation of the project. This approach was considered to be the most effective and reliable means of capturing relevant information, given the lack of pre-existing datasets or comprehensive repositories specifically focused on public interest AI projects. The second approach employed a structured web-based search methodology to identify relevant projects (called “Google Search” in the Dataset). Initially, a predefined set of search terms was used to query an online search engine. The first 20 search results returned were systematically reviewed, and for each result, hyperlinks within the identified pages were followed up to a depth of two levels. Each page at this depth was examined for the presence of relevant projects, which, if found, were documented in this dataset. This approach ensured a consistent and replicable process for capturing a broad yet focused scope of information.
 
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 <strong>Maintenance<br></strong>
 The authors will maintain the dataset to the best of their ability. This might mean that updates are not regular and data might be outdated within the status quo of the dataset. 
 Contact for questions: [email protected]
-If you want to contribute to this dataset by pointing to projects that are now yet included you can submit new projects to this Google Form and we will vet them and include them if they fit into our methodology. 
+If you want to contribute to this dataset by pointing to projects that are now yet included you can submit new projects to this <a href=https://forms.gle/TmcY1NRHYgYAokLD9>Google Form</a> and we will vet them and include them if they fit into our methodology. 
 
 
 Source as guideline for this datasheet: Gebru, T., Morgenstern, J., Vecchione, B., Wortman Vaughan, J., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. arXiv. https://arxiv.org/abs/1803.09010