Thank you for bringing the string similarity review topic to discussion. It is clear that the process needs an update, but there are many possibilities for how to do it.

The topic brought up needs an update in the context of national legislation because of its dependence on the national legislative system of a particular state. The probability that the name before TLD is a trademark in a specific nation governed by national immaterial laws and not in another, or even a worldwide brand, is high. 

The best model possible would be that ICANN reviews all international domains, but states can review national domain names because of the potential use of national trademark legislation. I do not think the review committee can analyze all the national legislation of all states that use the Internet. The highest possible level for examining similarities in the national domain names is the regional name authority, but the best is the national registrar authority. The means to simplify the task could be that ICANN advocates and guides the national registrar authorities on the proceeding. 

The name similarity recognition process is crucial in all situations. It could help to eliminate the possibility of creating fake domains with similar domain names, for example, confusing users to click for phishing. The recognition process must extend to all redirect requests in the DNS ecosystem because of the use of redirects because they point to other servers. Also, names in the "Leet" language (as f4c3b00k.com) should be considered a similarity. 

When a domain is well known (facebook.com is a good example), the parsing of the TLD should be considered because facebook.biz or facebook.xyz is probably a scam. 

The emergence of AI technologies could help enhance the process of detection because of the powerful parsing capabilities of modern ML models. I think a dedicated task model could do the task of detection, and the committee could review detections of AI, which would enhance the whole process. I would not rely on AI in the final stage of the review process because sometimes they give wrong results. But in the initial stages, such as review list creation, the AI could be an essential choice for process enhancement and speedup. 

I would like you to review the following additions exceptionally to process description parts:

1. Possible use of AI parsing technologies to simplify the parsing process

2. A new approach to the process. Split the name into parts. (http/s://)(example)(.xyz) in first parse operation (only splitting into parts), ((example).(xyz)) for example, compare two parts as a whole, name, and TLD, if they are similar to existing domains as a second operation. Protocol comparison may not be the most important thing to do, but I noticed many fake sites not using HTTPS. It could be an indicator of a non-reliable website. Why is this optimal? The name could be the same but registered with another TLD, or the name could be similar and TLD same, or both similar, but the name is slightly different, resulting in three possible combinations. 

3. The importance of using the process to stop online scams. 

The similarity parsing process brought up for review is a milestone in the development process of DNS policies. The process likely needs an update to address modern problems within the network because many fake but similar domain registrations operate to scam people. 

If considering only domain names, legal aspects must be reviewed, such as national intellectual property legislation and regional data policies. Therefore, the best institution for parsing the local (state) domain names for similarities would be the national registrar of a particular state. 

The submission contains a detailed description of the above topics, how to enhance the process with AI, a proposal for an update to the detection algorithm, and the importance of string similarity detection for scam detection.