TLDR: RAMP is an HTTP/3 fork and its aspirations lie at its regulatory instrumental features to regulate AI at server distribution not at its capabilities. As a specific protocol for AI, RAMP may evolve on its own and protect today http www internet by keeping further AI regulations separated from Internet regulations.
Note: In this revisited version I have removed all user and principles requirements from RAMP, everything takes place at server tier, the ramp:// doesn't even exist anymore and same as most people don't have no idea HTTP/2 and HTTP/3 even exist, they won't be about RAMP. Protocol specification is available on its GitHub and can be read on its text version here: https://raw.githubusercontent.com/RoboNetRAMP/RAMP/main/draft-antoniomax-robonet-2.txt
1. Introduction
RAMP is an experimental Internet protocol built “on top” of the HTTP/3IETF standard specifically introduced for serving most of AI generated content and AI systems that impersonates human behaviour(in a somewhat more encompassing terminology than “generative agents”,) with a focus on transparency and algorithmic accountability. The protocol is a response for a challenge of our times, and it aims to update the Internet itself as a response for its AI era.
The year of 2023 sets the bar for the last days of a World Wide Web (www) where the majority of the content inside it is still mostly “human madecontent”, and its digital counterparts arrive with many challenges regarding its cultural and intellectual integrity, authenticity and operational accountability. RAMP explores a novel approach to address the technical conditions that enable most of today’s issues with misinformation, disinformation and content provenance conflicts, and RAMP offers a way to do so before the www becomes a place where the distinction between human and synthetically made content gets blurred forever.
Figure 1: Internet content reactive tendency
While a custom and better tuned protocol would perhaps be the optimal choice in an ideal world, RAMP does not intent to enable a new functionality to the www itself nor it belongs to any proprietary software ecosystem that would justify a more exclusive set of capabilities, but what RAMP does intent, is to provide a future proof dual-function instrument, combining technical and regulatory instrumentation for the AI service delivery stack, and to best prove its capabilities it takes a highly unusual approach: it is basically an HTTP/3 clone, so that both initiatives can help each other succeed and accelerate their adoption by Internet industry stakeholders. Standards such as the HTTP don’t have many reasons to be cloned, but as RAMP aims to be as frictionless as possible on this first moment, the use of HTTP/3 as boilerplate can offer all RAMP needs to offer regulators its instrumental advantages “asap”.
Figure 2: HTTP protocol was left untouched for most of its life and 1.1 still stands
Standardized by the IETF as RFC 9114 in June 2022, the latest HTTP protocol update isn’t really new, the HTTP/3 has been around since 2016 and while it is slowly being adopted by Internet websites, 2023 browsers used by more than 70% of all Internet users already support its specifications and features. By forking HTTP/3 standards in its initial phase, RAMP fast-tracks its adoption rate and also counts on the expertise of thousands of professionals who already have HTTP/3 experience. And a fork also offers the chance for a more smooth transition over RAMP’s own evolutionary pace, so albeit unusual, the approach should be enough for RAMP initial aspirations.
RAMP’s bootstrapped adoption also aims to accelerate the pace for a common language that can catalyze synthetic content regulations, standard developments, and friendlier AI stakeholder collaborations, as RAMP takes shape as this common instrument that isolates the technical language used to discuss AI technologies by targeting AI delivery method, not its capabilities, in a moment where great AI professionals of all walks of life are asking for optimal political response for AI risks:
“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” - 2023, Center for AI Safety.
And as RAMP technical specifications encompass the entirety of AI service delivery stack, RAMP simplifies issues that are at the core of the international misalignment regarding new AI regulations as it addresses the lack of a common ground to target the misuse of AI technologies of today and tomorrow and it does so without impacting the HTTP Internet, a remarkably clean instrument for regulators.
By working in tandem with media metadata initiatives such as the International Press Telecommunications Council (IPTC) and the Coalition for Content Provenance and Authenticity (C2PA) RAMP may automatically classify and process synthetic media in an automated fashion, where protocol provenance controls exposed at Operational System (OS) level (here Android/iOS/Windows/Mac and Linux for most cases) can even become an end-user feature, here illustrated as a toggle button with 3 states:
Figure 3 - Conceptual RAMP Filter mockup as an iOS like toggle button.
HTTP/ON [only human content Internet]
RAMP1/Mixed [mix /composite human /AI content] [IPTC 1]
RAMP2/OFF [human, mixed and 100% digital content] [IPTC 1 - 2]
The middle /default option is there as equivalent to today's 2023 Internet, with no retroactive changes and Mixed AI generated content coming from RAMP1 services are served as default on this choice. The distinction between RAMP1 and RAMP2 media allows by side effect, other types of media to be understood as “human made”, not as empirically as desirable, but definitely in an approach that can be improved.
These new conceptual features (ON and OFF states) can treat RAMP traffic in a similar fashion to how any phone Airplane Mode toggle button works, and may eventually allow RAMP to in fact expose an Internet where people have the ability to make informed decisions on the type of AI content they want to consume, as RAMP then allows the creation of this clear holistic distinction for how AI content is served and consumed on the Internet, creating the mechanisms for such buttons/features to exist one day.
But above its implied end user benefits or technical capabilities that are indeed something to be explored, RAMP aims to become an instrumental catalyst for the regulation and standard developments of AI services, so that countries regulators, policymakers and other Standards Developing Organisations (SDOs) may share a common technical object that catalizes initiatives aimed at the several of the known and yet unknown AI technologies capabilities, at the same time that it isolates www Internet from ad hoc AI oriented interventions.
2. AI-generated content: A perspective
Given the recent developments of GenAI systems such as Stable Diffusion and ChatGPT, a remarkable amount of all our online systems are now exposed to the misuse of similar and new AI technologies that empower those using such appps with malicious intent, with capabilities yet to be properly understood. And while RAMP does not aim to provide an ultimate solution to all the old and new AI problems, its sharp focus on synthetic media and AI services delivery enables RAMP to interface with such issues before these may impact institutions, organizations and other ecosystems dynamics, giving RAMP a timely set of advantages over other regulatory approaches.
The following list samples two key implications of AI content and systems that arrive at distinct, non chronological moments of AI interactions lifecycle: the type* of issues and how* these are delivered;
The type of issues enabled by AI-generated media:
content that lacks watermarking or basic authorship consensus;
training data IP, copyright, fair use and licensing matters;
collection methods, source biases and information trustfulness;
alignment, control and misuse;
The how issues, as AI-generated content:
can be used to attack, corrupt and misuse legacy Internet services;
can impersonate biometrics such as voice or face and other credentials;
can automate misuse of data, techniques and credentials at scale;
can influence elections, media and social dynamics;
Figure 4: Left - Internet as is today with no protocol distinction for AI services. Right: RAMP claims AI services leaving minimal AI overlap with HTTP services.
RAMP aims to be both technologically and semantically inserted at the digital services stack as a proper delivery mechanism that catalyzes how AI services and content are exposed on the www along with the “legacy” HTTP, providing a clear legal instrument that allows targeted mitigation responses aimed at the AI issues stack.
3. The non-RoboNet approach
Recent American, Canadian and European developments on AI policymaking tackle major societal wide AI harms by using Risk Analysis principles as an holistic instrument in a very valid fashion, but treating AI traffic/apps/services/content as same as any other HTTP traffic comes with heavy societal-scale risks, that even with our ever improving regulations, means governments and citizens have to continue to rely on private sector companies and third parties to classify and moderate AI systems and content, demanding new sub-systems, procedures and institutional policies to tackle misuse at subsequent key moments of digital services lifecycle, in a particularly diffuse approach, where effective common governance mechanisms at global scale are virtually non existent and even live a moment of infighting for international influence in a standard setting warzone, where private sector and state-led AI governance initiatives aiming for "AI leadership", compete with non-state-led initiatives (here most notably OECD) and even SDOs such as ISO and the IEEE.
Laws, regulations and SMEs of all fields ask for content providers and platforms on the Internet to label synthetic media, to identify chatbots, to address disinformation posted and promoted by automated systems, create content filters for influence-as-a-service entities, address manipulative algorithmic systems, curb the use of deep fakes and AI-enabled disinformation operations. The ever growing list imposes costs at all operational levels for service providers and their counterparts /partners, with no industry wide established QoS standards, largely because same as the very problem list is new, the way societies react to it is pretty much the same age, in a domain where even actual experts are rare animals, and forget lawmakers and politicians that can speak the field lingo with basic technical proficiency, always a painful watch.
While some regulatory conformity mechanisms such as marked certifications may offer economic incentives for voluntary adherence on policies between economic allies, bad actors don’t share domestic or political benefits for compliance, particularly in the defense industry, where the treats of cyberwarfare and digital espionage find even less reasons to play fair.
RAMP vision for the near future is that new impersonation businesses of the good type should eventually become something highly casual in modern online environments, particularly for personal assistant AI companies or even in distinct virtual locations such as the Metaverse, where AI powered NPCs acting as people may be an entire new business on its own. By adopting the RAMP2 as the proper delivery protocol for all types of automation businesses that explore legal impersonation techniques, optimal impersonation models and techniques should emerge within RAMP2 semantic conformity, isolating older (today’s) impersonation approaches on the HTTP to over time get detected by the ever so improving network analysis inspection tools, that today do work significantly better on HTTP/1.1 and HTTP/2 than on HTTP/3 and of course on RAMP. A new industry wide standard setting capability should accelerate adoption of such good practices and help this optimal environment to emerge. RAMP believes on a future where its existence may help the Internet “patch” legacy friction points explored by those who exploit Internet’s structural weaknesses.
4. RoboNet Artificial Media Protocol
While a real world Internet-Draft RAMP specification may see different definitions than its experimental kin, RAMP allows the provision of AI media and systems without Internet users noticing anything different from everything they already do today. Same as HTTP/3 users don’t type http3:// on their browsers and apps when consuming HTTP/3 services, RAMP users won’t have to type ramp:// or ever even notice the difference from a regular HTTP service if nobody tells them.
Figure 5: RAMP integrates transparently with users' browsers, enabling access to AI content without requiring 'ramp://' in the URL (note: not an OSI accurate graph)
As specified today, all RAMP requests implicitly have a protocol version of “5.0” while RAMP responses have 4 distinct versioning flavors: "5.10" for text/html content, "5.11" for mixed AI content, "5.12" for 100% synthetic content and AI automated systems and “5.0” for everything else, as defined in section 4.3.2 of the RAMP experimental RFC:
4.3.2. Response Pseudo-Header Fields
The following pseudo-header fields are defined for responses:
":status": Carries the RAMP status code; see Section 15 of [HTTP].
This pseudo-header field MUST be included in all responses;
otherwise, the response is malformed (see Section 4.1.2).
":version": Contains major and the minor version number. RAMP
responses implicitly have a protocol version of:
"5.10" for text/html content,
"5.11" for mixed AI content,
"5.12" for 100% synthetic content and AI automated systems
"5.0" is assumed for anything else as default.
This pseudo-header field MUST NOT be empty for "http" or "https"
URIs; A version component MUST include at least the value of
"5.0" as default.
The minor number is not meant to imply extra protocol capabilities but to offer straightforward content classification independent of status response numbers, so that OS level AI classification may be consumed at any RAMP implementation complexity, no matter how servers/proxies/gateways applications react to RAMP requests.
As an HTTP/3 fork, this article doesn’t further elaborate on RAMP QUIC capabilities, how handshakes take place on first visits and apps, etc. Readers more interested on RAMP technical specifications might as well refer to specific HTTP/3 readings. If you’re interested on following how of how RAMP RFC evolve over time, you may compare RAMP and HTTP/3 specifications side by side using IETF iddiff tool.
Figure 6: Detail of an HTTP/3 and RAMP comparison
The RAMP protocol consists on 5 Core Principles:
RAMP serves Mixed and 100% AI generated content only;
RAMP serves all AI systems and services that provide human like behavior;
RAMP service modelling should promote academic data cooperatives and trusts;
RAMP content served by providers using other protocols should be discouraged;
RAMP compliance should have legal and/or binding regulatory incentives;
These principles are aspirational and non vital for either RAMP deployment or even its voluntary compliance /certification by industry stakeholders.
5. Content Provenance and Verification
Guided by RAMP core principles, RAMP updates the Internet specifically for AI so that the very Internet keeps up with the times, as emerging AI technologies don’t ask for permission to explore the pitfalls of legacy governance models in all places digital. RAMP aims to provide a clear content type distinction mechanism for how today’s AI systems offer their services, a distinction that may eventually even become an unavoidable necessity.
The benefits for bootstrapping RAMP architecture are many, e.g.: artists showing their art online can then have 2 proprietary streams to pitch their craft with the provenance mechanisms offered by HTTP and RAMP1 (for mixed authorship) all while 100% AI art is safely isolated at the RAMP2 access tier, so users browsing for art can safely identity what kind of artwork they are seeing. So at the same time that AI generated content provenance and authorship information gets isolated from its contents for both end users and websites, AI distinction is available without relying on vendor-locked watermarking, content scanning by third parties, and other privacy threatening techniques, as RAMP servers serve media with this type of classification at the request response header level.
Figure 7: Left - A request for C2PA authenticity flow over an HTTP/1.1 application / Right - A RAMP request returns an IPTC friendly RAMP1 id two hops earlier
As RAMP provides transparent classification of AI content types getting served by applications at such earlier stage, RAMP promotes a resilient offering of specific types of AI data and systems, in a way that reduces overhead to deliver specific propositions, such as specific apps that consume only 100% AI generated media.
Servers understand the type of media they are serving, particularly those that are serving specific AI applications that generate AI media. RAMP doesn’t change how end users download images or music, but requires servers — no matter the application they are serving — to be clear to clients at the “response header level” if that response brings some mixed AI, AI systems or 100% AI generated media. This allows for plenty of technical room for sub classifications at application levels at the most distinct stages of “Alice X Bob” interactions. Further investigation on how having another RAMP version identifier such as "1.13" exclusively for the text generated by Large Language Models (LLMs) applications is required to better understand the relationship between provenance and privacy, but in theory identifying LLMs based applications traffic may offer opportunities for a broader ecosystem of apps that inspect or interact with this type of media. RAMP positions itself at the AI service delivery stack as an application agnostic requirement that in time should become fine tuned for compliance of most popular AI product types.
New standards for Generative AI media metadata are still largely unknown by the public, but Google, Midjourney and Microsoft already adoptedIPTC /C2PA specifications for media generated by AI and the RAMP protocol encapsulates these with its standardized provenance with zero friction, offering even more compliance mechanisms for digital assets consumption and training by AI systems, such as those specified by C2PA metadata, here sampled in CBOR diagnostic format:
// Assertion for specifying whether the associated asset and its data
may be used for training an AI/ML model or mined for its data (or both).
{
"entries":
"c2pa.ai_inference" : {
"use" : "allowed"
},
"c2pa.ai_generative_training" : {
"use" : "notAllowed"
},
"c2pa.data_mining" : {
"use" : "constrained",
"constraint_info" : "may only be mined on days whose names end in 'y'"
}
}
Figure 8: Today’s data mining pipelines are analogous to common web browsing
As seen in Figure 8, when we look inside the hood of a scrapper engine crawling the www, servers see not many distinctions from average browsing, user agents and other things come to rescue but Internet wise, nobody really watches what bots do or not as long as they don’t cross the barrier to be considered a treat, given that as definitions go, defining a bot characteristics and intentions can be vastly tricky.
Figure 9: HTTP/3 request where scrapper abides content provenance metadata
HTTP/3 can do lots things better than its predecessors, including serving common HTML pages for scrappers. On Figure 9, a scrapper is configured to abide assets inference metadata information that allows it to be consumed for AI inference use. Here RAMP goes a step further: it offers a chance for AI made and Mixed AI content to be served as such, a much more granular and particularly relevant classification that is specific for AI.
Figure 10: RAMP responses can carry IPTC friendly media type classification, enabling granular AI/Non AI media consumption strategies / policies
With clear provenance distinction, trust can be broadcasted at protocol level so that Internet users may no longer need to rely on domestic laws or corporate policies to trust if a content is authentically human made or not, classification complexity is exposed on RAMP protocol response to clients.RAMP provides an unique opportunity for AI service providers to benchmark and build trust on AI systems, as its robust framework calls for responsible and ethical practices, where Internet users should then enjoy a chance of no longer have to get exposed to artificial content or artificial systems by mistake or malicious intent. RAMP reframes ad hoc AI content provenance policymaking, so that Internet platforms such as those of social media apps can rely on an up-to-date standard to build their user experience upon, at the same time that regulators may then stop relying on same platforms as intermediaries for AI content classification.
Protocol distinctions between RAMP and HTTP/3 are the foundation on how AI regulation may then act on specific moments of AI lifecycle without impacting how the www is experienced by end users. More specifically, RAMP allows for both service providers and end customers to interact with RAMP services as a) Defined by RAMP standard specifications b) Defined by sovereign legal entities c) Defined at corporate or parental policies and d) Defined as end user setting; in this order.
Figure 11: RAMP AI provenance capabilities allow policymakers and SDOs to interact at several specific moments of AI lifecycle deployments
Over time, RAMP independence from other protocols should expand the possibilities of what field stakeholders can build with the protocol characteristics and behavior, allowing a set of solutions to be built atop of these capabilities, at both ends of RAMP interactions with other protocols. The entire field of network anomaly detection should also naturally grow in a way where today techniques focused on the abuse of platforms terms and conditions would eventually need to get reinvented. And as the understanding on how RAMP ecosystem behavior settles in, optimal protection mechanisms specifically oriented at AI data and systems have then this proper delivery mechanism to grow upon.
But where RAMP unexpectedly excels is that by being a distinct protocol for AI systems with OS level compliance capabilities, even offline AI systems can then abide to the device protocol policies, which safeguards that offline devices still abides to corporate, parental and user chosen settings regarding AI content, a paramount feature that no version of the HTTP protocol was ever designed to offer, as RAMP can work independently or not from devices Airplane modes.
Finally, there are likely even more complex and possibly unforeseen scenarios involving the misuse of AI in offline modes, particularly given that today's AI systems and apps network traffic have no distinction from those of any other type of app. By offering this enforceable* and distinct protocol, phones and computers OS settings can expose RAMP extra security layer for any apps, that without exposing user privacy or relying on online services, may in the future even enable authorities to use the RAMP to define geofencing policies that may completely disable the use of these AI systems in specific geographic areas, such as schools and prisons, even for offline sideloaded apps, a capability no technology or regulation currently available can offer or much less enforce. RAMP introduces the technical conditions that can enable such capabilities with virtually zero technological complexity, streamlining a novel human centred security approach that while still imperfect, refines a method and common language for the discussion of these scenarios that simply does not need to impact Internet users on the www on its AI specific interventions.
*Enforcing compliance won't address malicious users from compiling AI systems that bypass or disguise RAMP traffic in HTTP or similar, or even more advanced treats that are specifically engineered to bypass compliance and restrictions, but by having an entire protocol for the delivery of AI content and AI systems such as RAMP in place, offers this unique insight instrument for its traffic behavioral fingerprinting and for the training of algorithms and Firewalls or similar tools that can be used to enforce stronger assurances against the misuse of AI technologies — even under the most challenging scenarios — in an automation friendly approach.
6. Regulation and Standards
RAMP aims to enable a technical and regulatory instrument where online automated services can no longer hide their tracks whenever interacting with the www Internet by being more of common HTTP traffic noise. As the protocol itself handshakes other systems as as what it is, not as anything else, RAMP isolates AI from other types of digital events.
Not by accident, RAMP should be able to address common risks that arrive with the misuse of new AI systems built with no industry wide standards, as by having no common ground for targeting AI technologies, regulators impose AI content classification and detection on service providers technological choices, budgets and incentives for compliance. RAMP eliminates this step and the usual implications of its misuse such as biases and behavioral doctoring, AI provenance is then no longer a matter of detection, but a mechanism that can be enforced, audited and classified by domestic and international regulations and policies.
If the day comes where conditions enable OS makers to adopt RAMP filter capabilities as described on Chapter 1, RAMP may then finally empower Internet users, and not corporations, to chose how Internet’s AI era may be consumed by them, as RAMP facilitates optimal conditions for mitigation strategies to be put in place to tackle the abuse and misuse of people’s www “public” data by today and tomorrow AI systems, at the same time that it enables a common technical framework for companies to expand on more adequate and standardized AI B2B practices.
As RAMP is a protocol that can deliver the distribution of human, mixed and synthetic media with standardized provenance, Internet Service Providers may also benefit from having two novel revenue streams that can deliver distinct and unique experiences for each of these business propositions, thanks to how RAMP automates provenance for these types of content. Over time, this business architecture should create the proper conditions to foster a less toxic and less geared by poor incentives Internet.
Figure 12 - RAMP protocol offers clear traffic distinction between HTTP and RAMP services/data while still providing the foundation for future AI use cases
As an Internet protocol, RAMP is also less exposed to domestic political frictions: As it is not a law, its definitions can remain technical in nature, removing the burden of asking for Social Media Platforms and Internet Service Providers to take a side on political stands regarding computational and artificial advantages of their home jurisdictions political moods for addressing AI misuse. Law may or may not enforce RAMP certification, Internet giants may or may not self regulate by adopting RAMP standards alongside HTTP/3 standards, these are all desirable political responses from regulators, SDOs and other stakeholders, not requirements for RAMP to be deployed nor adopted.
Also, to the best of my knowledge AI regulations, even the most recent ones such as the EU AI Act, are protocol agnostic, so RAMP still inherits legal provisions as fluidly as possible, but unlike a law, RAMP doesn't need to be sanctioned or not by Russia to work in Russian browsers, it is a software update and if countries decide to keep on regulating AI the same way as they already are, nothing needs to be changed.
RAMP aims to promote frictionless technical and legal characterizations for the delivery of AI systems and AI generated media, but by doing so at Internet protocol level, RAMP remains as apolitical as the very HTTP is, an instrument with valuable politico-regulatory benefits.
Lastly, RAMP allows for lawmakers and regulators to work on constitutional guidelines that cover the whole AI service delivery stack by targeting RAMP alone and how it engages with other Internet protocols or devices at the jurisdictions they’re at. RAMP empowers AI policymakers to legislate specifically on AI systems and media, leaving AI interactions with the www Internet as one of the possible many AI delivery and consumption methods that should eventually emerge, revealing RAMP as a valuable legal instrument for the future of AI.
7. Challenges and Limitations
Not even the HTTP/3 itself is that much of a popular chap, not many servers and websites use it at this moment and apparently the use of UDP instead of the TCP allows routers, switches, firewalls, data centers, networks and many other corporate environments to simply block UDP over ports 443 and 80 altogether, which by default makes requests downgrade themselves to HTTP/2 or HTTP/1.1 versions, which ultimately negates all HTTP/3 benefits such as its speed or security. Tying RAMP to HTTP/3 adoption may accelerate industry compatibility and adoption for both, but that assumes a good amount of coordinated efforts, but this yes, may benefit everyone.
QUIC, the underlying technology empowering HTTP/3 (and by consequence RAMP as well) is much more secure for end users such as you and me, mainly because QUIC does not allow plain text (read insecure) communications to take place, everything is encrypted by default and apparently even TLS Proxy Firewalls still can’t assess HTTP/3 data. This raises issues regarding compliance (specially in corporate networks,) and may even empower bad actors to chose HTTP/3 and RAMP as optimal delivery route for yet unknown treats.
RAMP also does not magically replaces Intellectual Property rights discussions, as RAMP is merely an encompassing asset that catalizes AI regulatory instrumentation by providing a common delivery asset for the matters of attribution, privacy and other legal dispositions such as ownership and licensing related to AI systems and AI media, it still a regulator job to work on better practices that put such mechanisms to use, same as still platforms job to clearly display RAMP provenance information. RAMP offers a common standard these parts can work on.
While AI traffic analysis own evolutionary pace tied to the AI traffic segmentation provided by RAMP may arguably eventually promote an ever so optimal ecosystem of security practices targeted at automated accounts and other methodologies that abuse and misuse digital services, it would be still be service providers job to address these, but RAMP fosters elements that allow stakeholders to share an even more powerful set of common security and compliance practices.
RAMP asks for Internet giant stakeholders not for a pause on AI developments, but for an opportunity to update the Internet itself for its AI era, as RAMP allows for a more consistent and encompassing approach that is human centered by design, one that is crafted for cooperation between AI providers, a proactive decision that acts as a building block for initiatives that look towards the fair and safe use of AI, a most needed foundation where the Internet then have a chance to grow with a plan.
RAMP updates Internet’s own structure, as shown in figures 13 and 14, so that users, companies and governments may enjoy optimal controls over AI on their devices.
Figure 13 - Without RAMP, the Internet will end up with AI services/clones in every aspect of our digital lives
Figure 14 - RAMP allows AI content and systems to have their own expression in their own particular space, in a way that allows Internet users to interact with AI content as they chose to, while preserving today's HTTP www as is
RAMP enables a valuable set of foundational technical assets aimed at addressing today issues related to how AI systems interact with Internet users in a prosocial approach, enabling a safer and more coherent online experience in the long term, it protects Internet users from geographically disconnected regulatory pace and the lack of incentives Internet companies have budgeted for the misuse and abuse of their services. But that’s its limits, countries and their regulatory bodies would surely still face unintended consequences and challenges for their actions same as for their inaction, RAMP merely changes the dynamics, doesn’t eliminates problems.
Also, while RAMP presents a promising solution for addressing AI regulation and provenance, it is crucial to recognize that, as a novel approach, ongoing research and collaboration are essential to refine and enhance its capabilities. RAMP should be treated as a technical instrument, similar to how HTTP is treated, focusing on its functional aspects rather than engaging in political debates. This approach ensures a safe and apolitical deployment of RAMP, enabling its full potential to be realized in promoting fairness, transparency, and accountability in AI systems. By engaging in continuous research and collaboration, we can further develop and optimize RAMP, adapting it to the evolving landscape of AI technologies and regulations, and fostering an environment where the benefits of AI can be harnessed responsibly and ethically.
Finally, RAMP empowers policymakers, regulators, academics, and institutions to closely observe and analyze the behavior and evolution of the protocol itself. This unique perspective allows for a comprehensive understanding of how AI is collectively consumed worldwide. By embracing RAMP, these stakeholders can monitor the protocol's characteristics over time and gain valuable insights into the responsible deployment and usage of AI. RAMP enables AI systems to operate and evolve at their own digital pace, while providing the necessary framework for policymakers to shape regulations and foster a safer and more accountable AI ecosystem.
8. Conclusion
This article introduced RAMP as a common technical instrument where AI content origins can be reliably traced, verified, and authenticated, empowering Internet users and regulators to discern between AI-generated and human-created information and systems. By shifting the burden of AI content classification from Internet Service Providers internal technologies for an uniform global Internet Protocol standard, RAMP offers a novel approach for AI policymaking that can in fact deliver fairness, transparency, and accountability, all while granting semantic mechanisms that can better protect human rights and freedom of expression online. Provision of the RAMP protocol asks for small political synergy and regulatory effort towards its specification and deployment, but even so, it still ends up being a political and technical effort that is orders of magnitude smaller than orchestrating common and meaningful international regulations for today and tomorrow AI challenges. RAMP offers the Internet an unique set of benefits for its AI era, one that can be crafted and delivered for a relatively small political and temporal effort, that also imposes no cost and no impacts on Internet users everyday life.
Cross-posted from my personal substack article.
TLDR: RAMP is an HTTP/3 fork and its aspirations lie at its regulatory instrumental features to regulate AI at server distribution not at its capabilities. As a specific protocol for AI, RAMP may evolve on its own and protect today http www internet by keeping further AI regulations separated from Internet regulations.
Note: In this revisited version I have removed all user and principles requirements from RAMP, everything takes place at server tier, the ramp:// doesn't even exist anymore and same as most people don't have no idea HTTP/2 and HTTP/3 even exist, they won't be about RAMP. Protocol specification is available on its GitHub and can be read on its text version here: https://raw.githubusercontent.com/RoboNetRAMP/RAMP/main/draft-antoniomax-robonet-2.txt
1. Introduction
RAMP is an experimental Internet protocol built “on top” of the HTTP/3 IETF standard specifically introduced for serving most of AI generated content and AI systems that impersonates human behaviour (in a somewhat more encompassing terminology than “generative agents”,) with a focus on transparency and algorithmic accountability. The protocol is a response for a challenge of our times, and it aims to update the Internet itself as a response for its AI era.
The year of 2023 sets the bar for the last days of a World Wide Web (www) where the majority of the content inside it is still mostly “human made content”, and its digital counterparts arrive with many challenges regarding its cultural and intellectual integrity, authenticity and operational accountability. RAMP explores a novel approach to address the technical conditions that enable most of today’s issues with misinformation, disinformation and content provenance conflicts, and RAMP offers a way to do so before the www becomes a place where the distinction between human and synthetically made content gets blurred forever.
Figure 1: Internet content reactive tendency
While a custom and better tuned protocol would perhaps be the optimal choice in an ideal world, RAMP does not intent to enable a new functionality to the www itself nor it belongs to any proprietary software ecosystem that would justify a more exclusive set of capabilities, but what RAMP does intent, is to provide a future proof dual-function instrument, combining technical and regulatory instrumentation for the AI service delivery stack, and to best prove its capabilities it takes a highly unusual approach: it is basically an HTTP/3 clone, so that both initiatives can help each other succeed and accelerate their adoption by Internet industry stakeholders. Standards such as the HTTP don’t have many reasons to be cloned, but as RAMP aims to be as frictionless as possible on this first moment, the use of HTTP/3 as boilerplate can offer all RAMP needs to offer regulators its instrumental advantages “asap”.
Figure 2: HTTP protocol was left untouched for most of its life and 1.1 still stands
Standardized by the IETF as RFC 9114 in June 2022, the latest HTTP protocol update isn’t really new, the HTTP/3 has been around since 2016 and while it is slowly being adopted by Internet websites, 2023 browsers used by more than 70% of all Internet users already support its specifications and features. By forking HTTP/3 standards in its initial phase, RAMP fast-tracks its adoption rate and also counts on the expertise of thousands of professionals who already have HTTP/3 experience. And a fork also offers the chance for a more smooth transition over RAMP’s own evolutionary pace, so albeit unusual, the approach should be enough for RAMP initial aspirations.
RAMP’s bootstrapped adoption also aims to accelerate the pace for a common language that can catalyze synthetic content regulations, standard developments, and friendlier AI stakeholder collaborations, as RAMP takes shape as this common instrument that isolates the technical language used to discuss AI technologies by targeting AI delivery method, not its capabilities, in a moment where great AI professionals of all walks of life are asking for optimal political response for AI risks:
And as RAMP technical specifications encompass the entirety of AI service delivery stack, RAMP simplifies issues that are at the core of the international misalignment regarding new AI regulations as it addresses the lack of a common ground to target the misuse of AI technologies of today and tomorrow and it does so without impacting the HTTP Internet, a remarkably clean instrument for regulators.
By working in tandem with media metadata initiatives such as the International Press Telecommunications Council (IPTC) and the Coalition for Content Provenance and Authenticity (C2PA) RAMP may automatically classify and process synthetic media in an automated fashion, where protocol provenance controls exposed at Operational System (OS) level (here Android/iOS/Windows/Mac and Linux for most cases) can even become an end-user feature, here illustrated as a toggle button with 3 states:
Figure 3 - Conceptual RAMP Filter mockup as an iOS like toggle button.
The middle /default option is there as equivalent to today's 2023 Internet, with no retroactive changes and Mixed AI generated content coming from RAMP1 services are served as default on this choice. The distinction between RAMP1 and RAMP2 media allows by side effect, other types of media to be understood as “human made”, not as empirically as desirable, but definitely in an approach that can be improved.
These new conceptual features (ON and OFF states) can treat RAMP traffic in a similar fashion to how any phone Airplane Mode toggle button works, and may eventually allow RAMP to in fact expose an Internet where people have the ability to make informed decisions on the type of AI content they want to consume, as RAMP then allows the creation of this clear holistic distinction for how AI content is served and consumed on the Internet, creating the mechanisms for such buttons/features to exist one day.
But above its implied end user benefits or technical capabilities that are indeed something to be explored, RAMP aims to become an instrumental catalyst for the regulation and standard developments of AI services, so that countries regulators, policymakers and other Standards Developing Organisations (SDOs) may share a common technical object that catalizes initiatives aimed at the several of the known and yet unknown AI technologies capabilities, at the same time that it isolates www Internet from ad hoc AI oriented interventions.
2. AI-generated content: A perspective
Given the recent developments of GenAI systems such as Stable Diffusion and ChatGPT, a remarkable amount of all our online systems are now exposed to the misuse of similar and new AI technologies that empower those using such appps with malicious intent, with capabilities yet to be properly understood. And while RAMP does not aim to provide an ultimate solution to all the old and new AI problems, its sharp focus on synthetic media and AI services delivery enables RAMP to interface with such issues before these may impact institutions, organizations and other ecosystems dynamics, giving RAMP a timely set of advantages over other regulatory approaches.
The following list samples two key implications of AI content and systems that arrive at distinct, non chronological moments of AI interactions lifecycle: the type* of issues and how* these are delivered;
The type of issues enabled by AI-generated media:
The how issues, as AI-generated content:
Figure 4: Left - Internet as is today with no protocol distinction for AI services. Right: RAMP claims AI services leaving minimal AI overlap with HTTP services.
RAMP aims to be both technologically and semantically inserted at the digital services stack as a proper delivery mechanism that catalyzes how AI services and content are exposed on the www along with the “legacy” HTTP, providing a clear legal instrument that allows targeted mitigation responses aimed at the AI issues stack.
3. The non-RoboNet approach
Recent American, Canadian and European developments on AI policymaking tackle major societal wide AI harms by using Risk Analysis principles as an holistic instrument in a very valid fashion, but treating AI traffic/apps/services/content as same as any other HTTP traffic comes with heavy societal-scale risks, that even with our ever improving regulations, means governments and citizens have to continue to rely on private sector companies and third parties to classify and moderate AI systems and content, demanding new sub-systems, procedures and institutional policies to tackle misuse at subsequent key moments of digital services lifecycle, in a particularly diffuse approach, where effective common governance mechanisms at global scale are virtually non existent and even live a moment of infighting for international influence in a standard setting warzone, where private sector and state-led AI governance initiatives aiming for "AI leadership", compete with non-state-led initiatives (here most notably OECD) and even SDOs such as ISO and the IEEE.
Laws, regulations and SMEs of all fields ask for content providers and platforms on the Internet to label synthetic media, to identify chatbots, to address disinformation posted and promoted by automated systems, create content filters for influence-as-a-service entities, address manipulative algorithmic systems, curb the use of deep fakes and AI-enabled disinformation operations. The ever growing list imposes costs at all operational levels for service providers and their counterparts /partners, with no industry wide established QoS standards, largely because same as the very problem list is new, the way societies react to it is pretty much the same age, in a domain where even actual experts are rare animals, and forget lawmakers and politicians that can speak the field lingo with basic technical proficiency, always a painful watch.
While some regulatory conformity mechanisms such as marked certifications may offer economic incentives for voluntary adherence on policies between economic allies, bad actors don’t share domestic or political benefits for compliance, particularly in the defense industry, where the treats of cyberwarfare and digital espionage find even less reasons to play fair.
RAMP vision for the near future is that new impersonation businesses of the good type should eventually become something highly casual in modern online environments, particularly for personal assistant AI companies or even in distinct virtual locations such as the Metaverse, where AI powered NPCs acting as people may be an entire new business on its own. By adopting the RAMP2 as the proper delivery protocol for all types of automation businesses that explore legal impersonation techniques, optimal impersonation models and techniques should emerge within RAMP2 semantic conformity, isolating older (today’s) impersonation approaches on the HTTP to over time get detected by the ever so improving network analysis inspection tools, that today do work significantly better on HTTP/1.1 and HTTP/2 than on HTTP/3 and of course on RAMP. A new industry wide standard setting capability should accelerate adoption of such good practices and help this optimal environment to emerge. RAMP believes on a future where its existence may help the Internet “patch” legacy friction points explored by those who exploit Internet’s structural weaknesses.
4. RoboNet Artificial Media Protocol
While a real world Internet-Draft RAMP specification may see different definitions than its experimental kin, RAMP allows the provision of AI media and systems without Internet users noticing anything different from everything they already do today. Same as HTTP/3 users don’t type http3:// on their browsers and apps when consuming HTTP/3 services, RAMP users won’t have to type ramp:// or ever even notice the difference from a regular HTTP service if nobody tells them.
Figure 5: RAMP integrates transparently with users' browsers, enabling access to AI content without requiring 'ramp://' in the URL (note: not an OSI accurate graph)
As specified today, all RAMP requests implicitly have a protocol version of “5.0” while RAMP responses have 4 distinct versioning flavors: "5.10" for text/html content, "5.11" for mixed AI content, "5.12" for 100% synthetic content and AI automated systems and “5.0” for everything else, as defined in section 4.3.2 of the RAMP experimental RFC:
The minor number is not meant to imply extra protocol capabilities but to offer straightforward content classification independent of status response numbers, so that OS level AI classification may be consumed at any RAMP implementation complexity, no matter how servers/proxies/gateways applications react to RAMP requests.
As an HTTP/3 fork, this article doesn’t further elaborate on RAMP QUIC capabilities, how handshakes take place on first visits and apps, etc. Readers more interested on RAMP technical specifications might as well refer to specific HTTP/3 readings. If you’re interested on following how of how RAMP RFC evolve over time, you may compare RAMP and HTTP/3 specifications side by side using IETF iddiff tool.
Figure 6: Detail of an HTTP/3 and RAMP comparison
The RAMP protocol consists on 5 Core Principles:
These principles are aspirational and non vital for either RAMP deployment or even its voluntary compliance /certification by industry stakeholders.
5. Content Provenance and Verification
Guided by RAMP core principles, RAMP updates the Internet specifically for AI so that the very Internet keeps up with the times, as emerging AI technologies don’t ask for permission to explore the pitfalls of legacy governance models in all places digital. RAMP aims to provide a clear content type distinction mechanism for how today’s AI systems offer their services, a distinction that may eventually even become an unavoidable necessity.
The benefits for bootstrapping RAMP architecture are many, e.g.: artists showing their art online can then have 2 proprietary streams to pitch their craft with the provenance mechanisms offered by HTTP and RAMP1 (for mixed authorship) all while 100% AI art is safely isolated at the RAMP2 access tier, so users browsing for art can safely identity what kind of artwork they are seeing. So at the same time that AI generated content provenance and authorship information gets isolated from its contents for both end users and websites, AI distinction is available without relying on vendor-locked watermarking, content scanning by third parties, and other privacy threatening techniques, as RAMP servers serve media with this type of classification at the request response header level.
Figure 7: Left - A request for C2PA authenticity flow over an HTTP/1.1 application / Right - A RAMP request returns an IPTC friendly RAMP1 id two hops earlier
As RAMP provides transparent classification of AI content types getting served by applications at such earlier stage, RAMP promotes a resilient offering of specific types of AI data and systems, in a way that reduces overhead to deliver specific propositions, such as specific apps that consume only 100% AI generated media.
Servers understand the type of media they are serving, particularly those that are serving specific AI applications that generate AI media. RAMP doesn’t change how end users download images or music, but requires servers — no matter the application they are serving — to be clear to clients at the “response header level” if that response brings some mixed AI, AI systems or 100% AI generated media. This allows for plenty of technical room for sub classifications at application levels at the most distinct stages of “Alice X Bob” interactions. Further investigation on how having another RAMP version identifier such as "1.13" exclusively for the text generated by Large Language Models (LLMs) applications is required to better understand the relationship between provenance and privacy, but in theory identifying LLMs based applications traffic may offer opportunities for a broader ecosystem of apps that inspect or interact with this type of media. RAMP positions itself at the AI service delivery stack as an application agnostic requirement that in time should become fine tuned for compliance of most popular AI product types.
New standards for Generative AI media metadata are still largely unknown by the public, but Google, Midjourney and Microsoft already adopted IPTC /C2PA specifications for media generated by AI and the RAMP protocol encapsulates these with its standardized provenance with zero friction, offering even more compliance mechanisms for digital assets consumption and training by AI systems, such as those specified by C2PA metadata, here sampled in CBOR diagnostic format:
Figure 8: Today’s data mining pipelines are analogous to common web browsing
As seen in Figure 8, when we look inside the hood of a scrapper engine crawling the www, servers see not many distinctions from average browsing, user agents and other things come to rescue but Internet wise, nobody really watches what bots do or not as long as they don’t cross the barrier to be considered a treat, given that as definitions go, defining a bot characteristics and intentions can be vastly tricky.
Figure 9: HTTP/3 request where scrapper abides content provenance metadata
HTTP/3 can do lots things better than its predecessors, including serving common HTML pages for scrappers. On Figure 9, a scrapper is configured to abide assets inference metadata information that allows it to be consumed for AI inference use. Here RAMP goes a step further: it offers a chance for AI made and Mixed AI content to be served as such, a much more granular and particularly relevant classification that is specific for AI.
Figure 10: RAMP responses can carry IPTC friendly media type classification, enabling granular AI/Non AI media consumption strategies / policies
With clear provenance distinction, trust can be broadcasted at protocol level so that Internet users may no longer need to rely on domestic laws or corporate policies to trust if a content is authentically human made or not, classification complexity is exposed on RAMP protocol response to clients. RAMP provides an unique opportunity for AI service providers to benchmark and build trust on AI systems, as its robust framework calls for responsible and ethical practices, where Internet users should then enjoy a chance of no longer have to get exposed to artificial content or artificial systems by mistake or malicious intent. RAMP reframes ad hoc AI content provenance policymaking, so that Internet platforms such as those of social media apps can rely on an up-to-date standard to build their user experience upon, at the same time that regulators may then stop relying on same platforms as intermediaries for AI content classification.
Protocol distinctions between RAMP and HTTP/3 are the foundation on how AI regulation may then act on specific moments of AI lifecycle without impacting how the www is experienced by end users. More specifically, RAMP allows for both service providers and end customers to interact with RAMP services as a) Defined by RAMP standard specifications b) Defined by sovereign legal entities c) Defined at corporate or parental policies and d) Defined as end user setting; in this order.
Figure 11: RAMP AI provenance capabilities allow policymakers and SDOs to interact at several specific moments of AI lifecycle deployments
Over time, RAMP independence from other protocols should expand the possibilities of what field stakeholders can build with the protocol characteristics and behavior, allowing a set of solutions to be built atop of these capabilities, at both ends of RAMP interactions with other protocols. The entire field of network anomaly detection should also naturally grow in a way where today techniques focused on the abuse of platforms terms and conditions would eventually need to get reinvented. And as the understanding on how RAMP ecosystem behavior settles in, optimal protection mechanisms specifically oriented at AI data and systems have then this proper delivery mechanism to grow upon.
But where RAMP unexpectedly excels is that by being a distinct protocol for AI systems with OS level compliance capabilities, even offline AI systems can then abide to the device protocol policies, which safeguards that offline devices still abides to corporate, parental and user chosen settings regarding AI content, a paramount feature that no version of the HTTP protocol was ever designed to offer, as RAMP can work independently or not from devices Airplane modes.
Finally, there are likely even more complex and possibly unforeseen scenarios involving the misuse of AI in offline modes, particularly given that today's AI systems and apps network traffic have no distinction from those of any other type of app. By offering this enforceable* and distinct protocol, phones and computers OS settings can expose RAMP extra security layer for any apps, that without exposing user privacy or relying on online services, may in the future even enable authorities to use the RAMP to define geofencing policies that may completely disable the use of these AI systems in specific geographic areas, such as schools and prisons, even for offline sideloaded apps, a capability no technology or regulation currently available can offer or much less enforce. RAMP introduces the technical conditions that can enable such capabilities with virtually zero technological complexity, streamlining a novel human centred security approach that while still imperfect, refines a method and common language for the discussion of these scenarios that simply does not need to impact Internet users on the www on its AI specific interventions.
*Enforcing compliance won't address malicious users from compiling AI systems that bypass or disguise RAMP traffic in HTTP or similar, or even more advanced treats that are specifically engineered to bypass compliance and restrictions, but by having an entire protocol for the delivery of AI content and AI systems such as RAMP in place, offers this unique insight instrument for its traffic behavioral fingerprinting and for the training of algorithms and Firewalls or similar tools that can be used to enforce stronger assurances against the misuse of AI technologies — even under the most challenging scenarios — in an automation friendly approach.
6. Regulation and Standards
RAMP aims to enable a technical and regulatory instrument where online automated services can no longer hide their tracks whenever interacting with the www Internet by being more of common HTTP traffic noise. As the protocol itself handshakes other systems as as what it is, not as anything else, RAMP isolates AI from other types of digital events.
Not by accident, RAMP should be able to address common risks that arrive with the misuse of new AI systems built with no industry wide standards, as by having no common ground for targeting AI technologies, regulators impose AI content classification and detection on service providers technological choices, budgets and incentives for compliance. RAMP eliminates this step and the usual implications of its misuse such as biases and behavioral doctoring, AI provenance is then no longer a matter of detection, but a mechanism that can be enforced, audited and classified by domestic and international regulations and policies.
If the day comes where conditions enable OS makers to adopt RAMP filter capabilities as described on Chapter 1, RAMP may then finally empower Internet users, and not corporations, to chose how Internet’s AI era may be consumed by them, as RAMP facilitates optimal conditions for mitigation strategies to be put in place to tackle the abuse and misuse of people’s www “public” data by today and tomorrow AI systems, at the same time that it enables a common technical framework for companies to expand on more adequate and standardized AI B2B practices.
As RAMP is a protocol that can deliver the distribution of human, mixed and synthetic media with standardized provenance, Internet Service Providers may also benefit from having two novel revenue streams that can deliver distinct and unique experiences for each of these business propositions, thanks to how RAMP automates provenance for these types of content. Over time, this business architecture should create the proper conditions to foster a less toxic and less geared by poor incentives Internet.
Figure 12 - RAMP protocol offers clear traffic distinction between HTTP and RAMP services/data while still providing the foundation for future AI use cases
As an Internet protocol, RAMP is also less exposed to domestic political frictions: As it is not a law, its definitions can remain technical in nature, removing the burden of asking for Social Media Platforms and Internet Service Providers to take a side on political stands regarding computational and artificial advantages of their home jurisdictions political moods for addressing AI misuse. Law may or may not enforce RAMP certification, Internet giants may or may not self regulate by adopting RAMP standards alongside HTTP/3 standards, these are all desirable political responses from regulators, SDOs and other stakeholders, not requirements for RAMP to be deployed nor adopted.
Also, to the best of my knowledge AI regulations, even the most recent ones such as the EU AI Act, are protocol agnostic, so RAMP still inherits legal provisions as fluidly as possible, but unlike a law, RAMP doesn't need to be sanctioned or not by Russia to work in Russian browsers, it is a software update and if countries decide to keep on regulating AI the same way as they already are, nothing needs to be changed.
RAMP aims to promote frictionless technical and legal characterizations for the delivery of AI systems and AI generated media, but by doing so at Internet protocol level, RAMP remains as apolitical as the very HTTP is, an instrument with valuable politico-regulatory benefits.
Lastly, RAMP allows for lawmakers and regulators to work on constitutional guidelines that cover the whole AI service delivery stack by targeting RAMP alone and how it engages with other Internet protocols or devices at the jurisdictions they’re at. RAMP empowers AI policymakers to legislate specifically on AI systems and media, leaving AI interactions with the www Internet as one of the possible many AI delivery and consumption methods that should eventually emerge, revealing RAMP as a valuable legal instrument for the future of AI.
7. Challenges and Limitations
Not even the HTTP/3 itself is that much of a popular chap, not many servers and websites use it at this moment and apparently the use of UDP instead of the TCP allows routers, switches, firewalls, data centers, networks and many other corporate environments to simply block UDP over ports 443 and 80 altogether, which by default makes requests downgrade themselves to HTTP/2 or HTTP/1.1 versions, which ultimately negates all HTTP/3 benefits such as its speed or security. Tying RAMP to HTTP/3 adoption may accelerate industry compatibility and adoption for both, but that assumes a good amount of coordinated efforts, but this yes, may benefit everyone.
QUIC, the underlying technology empowering HTTP/3 (and by consequence RAMP as well) is much more secure for end users such as you and me, mainly because QUIC does not allow plain text (read insecure) communications to take place, everything is encrypted by default and apparently even TLS Proxy Firewalls still can’t assess HTTP/3 data. This raises issues regarding compliance (specially in corporate networks,) and may even empower bad actors to chose HTTP/3 and RAMP as optimal delivery route for yet unknown treats.
RAMP also does not magically replaces Intellectual Property rights discussions, as RAMP is merely an encompassing asset that catalizes AI regulatory instrumentation by providing a common delivery asset for the matters of attribution, privacy and other legal dispositions such as ownership and licensing related to AI systems and AI media, it still a regulator job to work on better practices that put such mechanisms to use, same as still platforms job to clearly display RAMP provenance information. RAMP offers a common standard these parts can work on.
While AI traffic analysis own evolutionary pace tied to the AI traffic segmentation provided by RAMP may arguably eventually promote an ever so optimal ecosystem of security practices targeted at automated accounts and other methodologies that abuse and misuse digital services, it would be still be service providers job to address these, but RAMP fosters elements that allow stakeholders to share an even more powerful set of common security and compliance practices.
RAMP asks for Internet giant stakeholders not for a pause on AI developments, but for an opportunity to update the Internet itself for its AI era, as RAMP allows for a more consistent and encompassing approach that is human centered by design, one that is crafted for cooperation between AI providers, a proactive decision that acts as a building block for initiatives that look towards the fair and safe use of AI, a most needed foundation where the Internet then have a chance to grow with a plan.
RAMP updates Internet’s own structure, as shown in figures 13 and 14, so that users, companies and governments may enjoy optimal controls over AI on their devices.
Figure 13 - Without RAMP, the Internet will end up with AI services/clones in every aspect of our digital lives
Figure 14 - RAMP allows AI content and systems to have their own expression in their own particular space, in a way that allows Internet users to interact with AI content as they chose to, while preserving today's HTTP www as is
RAMP enables a valuable set of foundational technical assets aimed at addressing today issues related to how AI systems interact with Internet users in a prosocial approach, enabling a safer and more coherent online experience in the long term, it protects Internet users from geographically disconnected regulatory pace and the lack of incentives Internet companies have budgeted for the misuse and abuse of their services. But that’s its limits, countries and their regulatory bodies would surely still face unintended consequences and challenges for their actions same as for their inaction, RAMP merely changes the dynamics, doesn’t eliminates problems.
Also, while RAMP presents a promising solution for addressing AI regulation and provenance, it is crucial to recognize that, as a novel approach, ongoing research and collaboration are essential to refine and enhance its capabilities. RAMP should be treated as a technical instrument, similar to how HTTP is treated, focusing on its functional aspects rather than engaging in political debates. This approach ensures a safe and apolitical deployment of RAMP, enabling its full potential to be realized in promoting fairness, transparency, and accountability in AI systems. By engaging in continuous research and collaboration, we can further develop and optimize RAMP, adapting it to the evolving landscape of AI technologies and regulations, and fostering an environment where the benefits of AI can be harnessed responsibly and ethically.
Finally, RAMP empowers policymakers, regulators, academics, and institutions to closely observe and analyze the behavior and evolution of the protocol itself. This unique perspective allows for a comprehensive understanding of how AI is collectively consumed worldwide. By embracing RAMP, these stakeholders can monitor the protocol's characteristics over time and gain valuable insights into the responsible deployment and usage of AI. RAMP enables AI systems to operate and evolve at their own digital pace, while providing the necessary framework for policymakers to shape regulations and foster a safer and more accountable AI ecosystem.
8. Conclusion
This article introduced RAMP as a common technical instrument where AI content origins can be reliably traced, verified, and authenticated, empowering Internet users and regulators to discern between AI-generated and human-created information and systems. By shifting the burden of AI content classification from Internet Service Providers internal technologies for an uniform global Internet Protocol standard, RAMP offers a novel approach for AI policymaking that can in fact deliver fairness, transparency, and accountability, all while granting semantic mechanisms that can better protect human rights and freedom of expression online. Provision of the RAMP protocol asks for small political synergy and regulatory effort towards its specification and deployment, but even so, it still ends up being a political and technical effort that is orders of magnitude smaller than orchestrating common and meaningful international regulations for today and tomorrow AI challenges. RAMP offers the Internet an unique set of benefits for its AI era, one that can be crafted and delivered for a relatively small political and temporal effort, that also imposes no cost and no impacts on Internet users everyday life.