Merge pull request #1 from intarchboard/wk_initial_grammar

Some initial grammar nits.

Author: mnot

draft-iab-ai-control-report.md

@@ -102,29 +102,29 @@ Furthermore, the content of the report comes from presentations given by worksho
 
 The workshop began by surveying the state of AI control.
 
-Currently, Internet publishers express their preferences for how their content is treated for purposes of AI training using a variety of mechanisms, including declarative ones, such as terms of service and robots.txt {{RFC9309}}, and active ones, such as use of paywalls and selective blocking of crawlers (e.g., by IP address, User-Agent).
+Currently, Internet publishers express their preferences for how their content is treated for purposes of AI training using a variety of mechanisms, including declarative ones, such as terms of service and robots.txt {{RFC9309}}, and active ones, such as the use of paywalls and selective blocking of crawlers (e.g., by IP address, User-Agent).
 
 There was disagreement about the implications of AI opt-out overall. Research indicates that the use of such controls is becoming more prevalent, reducing the availability of data for AI training. Some of the participants expressed concern about the implications of this -- although at least one AI vendor seemed less concerned by this, indicating that "there are plenty of tokens available" for training, even if many opt out. Others expressed a need to opt out of AI training because of how they perceive its effects on their control over content, seeing AI as usurping their relationships with customers and a potential threat to whole industries.
 
 However, there was quick agreement that both viewpoints were harmed by the current state of AI opt-out -- a situation where "no one is better off" (in the words of one participant).
 
-Much of that dysfunction was attributed to the lack of coordination and standards for AI opt out. Currently, content publishers need to consult with each AI vendor to understand how to opt out of training their products, as there is significant variance in each vendor's behaviour. Furthermore, publishers need to continually monitor both for new vendors, and for changes to the policies of the vendors they are aware of.
+Much of that dysfunction was attributed to the lack of coordination and standards for AI opt-out. Currently, content publishers need to consult with each AI vendor to understand how to opt out of training their products, as there is significant variance in each vendor's behaviour. Furthermore, publishers need to continually monitor both for new vendors, and for changes to the policies of the vendors they are aware of.
 
 Underlying those immediate issues, however, are significant constraints that could be attributed to uncertainties in the legal context, the nature of AI, and the implications of needing to opt out of crawling for it.
 
 ## Crawl Time vs. Inference Time
 
-Perhaps most significant is the "crawl time vs. inference time" problem. Statements of preference are apparent at crawl time, bound to content either by location (e.g. robots.txt) or embedded inside the content itself as metadata. However, the target of those directives is often disassociated from the crawler, either because the crawl data is not only used for training AI models, or because the preferences are applicable at inference time.
+Perhaps most significant is the "crawl time vs. inference time" problem. Statements of preference are apparent at crawl time, bound to content either by location (e.g., robots.txt) or embedded inside the content itself as metadata. However, the target of those directives is often disassociated from the crawler, either because the crawl data is not only used for training AI models, or because the preferences are applicable at inference time.
 
 ### Multiple Uses for Crawl Data
 
-A crawl's data might have multiple uses because the vendor also has another product that uses it (e.g., a search engine), or because the crawl is performed by a party other than the AI vendor. Both are very common patterns: operators of many Internet search engines also train AI models, and many AI models use third party crawl data. In either case, conflating different uses can change the incentives for publishers to cooperate with the crawler.
+A crawl's data might have multiple uses because the vendor also has another product that uses it (e.g., a search engine), or because the crawl is performed by a party other than the AI vendor. Both are very common patterns: operators of many Internet search engines also train AI models, and many AI models use third-party crawl data. In either case, conflating different uses can change the incentives for publishers to cooperate with the crawler.
 
-Well-established uses of crawling such as Internet search were seen by participants as at least partially aligned with the interests of publishers: they allow their sites to be crawled, and in return they receive higher traffic and attention due to being in the search index. However, several participants pointed out that this symbiotic relationship does not exist for AI training uses -- with some viewing AI as hostile to publishers, because it has the capacity to take traffic away from their sites.
+Well-established uses of crawling, such as Internet search, were seen by participants as at least partially aligned with the interests of publishers: they allow their sites to be crawled, and in return they receive higher traffic and attention due to being in the search index. However, several participants pointed out that this symbiotic relationship does not exist for AI training uses -- with some viewing AI as hostile to publishers, because it has the capacity to take traffic away from their sites.
 
 Therefore, when a crawler has multiple uses that include AI, participants observed that "collateral damage" was likely for non-AI uses, especially when publishers take more active control measures such as blocking or paywalls to protect their interests.
 
-Several participants expressed concerns about this phenomenon's effects on the ecosystem, effectively "locking down the Web" with one opining that there were implications on freedom of expression overall.
+Several participants expressed concerns about this phenomenon's effects on the ecosystem, effectively "locking down the Web" with one opining that there were implications for freedom of expression overall.
 
 ### Application of Preferences
 
@@ -140,7 +140,7 @@ Compounding this issue was the observation that preferences change over time, wh
 
 This disconnection between the statement of preferences and its application was felt by participants to contribute to a lack of trust in the ecosystem, along with the typical lack of attribution for data sources in LLMs, lack of an incentive for publishers to contribute data, and finally (and most noted) a lack of any means of monitoring compliance with preferences.
 
-This lack of trust led some participations to question whether communicating preferences is sufficient in all cases without an accompanying way to mitigate or track cases of those preferences being followed. Some participants also indicated that lack of trust was the primary cause of increasingly prevalent blocking of AI crawler IP addresses, among other measures.
+This lack of trust led some participants to question whether communicating preferences is sufficient in all cases without an accompanying way to mitigate or track cases of those preferences being followed. Some participants also indicated that lack of trust was the primary cause of increasingly prevalent blocking of AI crawler IP addresses, among other measures.
 
 ## Attachment
 
@@ -184,7 +184,7 @@ However, several participants pointed out issues with deploying registries at In
 
 ## Vocabulary
 
-Another major focus area for the workshop was on _vocabulary_ -- the specific semantics of the opt-out signal. Several participants noted that there are already many proposals for vocabularies, as well as many conflicting vocabularies already in use. Several examples were discussed, including where existing terms were ambiguous, did not address common use cases, or were used in conflicting way by different actors.
+Another major focus area for the workshop was on _vocabulary_ -- the specific semantics of the opt-out signal. Several participants noted that there are already many proposals for vocabularies, as well as many conflicting vocabularies already in use. Several examples were discussed, including where existing terms were ambiguous, did not address common use cases, or were used in conflicting ways by different actors.
 
 Although no conclusions regarding exact vocabulary were reached, it was generally agreed that a complex vocabulary is unlikely to succeed.