Connected Information

As a proponent of XML for legislation, I’m often asked why an XML approach is better than a more traditional approach using a word processor. The answer is simple – it’s all about connected information.

The digital end point in a legislative system can no longer be publication of PDFs. PDFs are nothing but a kludgy way to digitize paper — a way to preserve the old traditions and avoid the future. Try reading a PDF on a cell phone and you see the problem. Try clicking on a citation in a PDF and you see the problem. Try and scrape the information out of a PDF to make it computer readable and you see the problem. The only useful function that PDFs serve is as a bridge to the past.

The future is all about connected information — breaking the physical bounds of what we think of as a document and allowing the nuggets of information found within them to be connected, interrelated, and acted upon. This is the real reason why the future lies with XML and its related technologies.

In my blog last week I provided a brief glimpse into how our future amending tools will work. I explored how legislation could be managed similar to how software is managed with GitHub. This is an example of how useful connected information becomes. Rather than producing bills and amendments as paper documents, the information is stored in a way that it can be efficiently and accurately automated — and made available to the public in a computer readable way.

At Xcential, we’re building our new web-based authoring system — LegisPro. If you take a close look at it, you’ll see that it has two main components. Of course, there is a robust XML editor. However, at the system’s very heart is a linking system — something we call a resolver. It’s this resolver where the true power lies. It’s an HTTP-based system for managing all the linkages that exist in the system. It connects XML repositories, external data sources, and even SQL databases together to form a seamless universe of connected information.

We’re working hard to transform how legislation, and indeed, all government information is viewed. It’s not just about connecting laws and legislation together through simple web links. We talking about providing rich connections between all government information — tying financial data to laws and legislation, connecting regulatory information together, associating people, places, and things to government data, and on and on. We have barely started to scratch the surface, but it’s clear that the future lies with connected information.

While we today position LegisPro as a bill authoring system — it’s much more than that. It’s some of the fundamental underpinnings necessary for a system to transform government documents of today into the connected information of tomorrow.

Connected Information

Can GitHub be used to manage legislation?

Every so often, someone suggests that GitHub would be a great way to manage legislation. Usually, we roll our eyes at the naïve suggestion and that is that.

However, there are a good many similarities that do deserve consideration. What if the amending process was supported by a tool that, while maybe not GitHub, worked on the same principles?

My company, Xcential, built the amending solution for the California Legislature, using a process we like to call Amendments in Context. With this process, a proposed revision of a bill is drafted and then the amendments necessary to produce that revision are extracted as an amendment document. That amendment document, which really becomes an enumeration of proposed changes in a report, is then submitted to the committee for approval. If approved, the revised document that was drafted earlier then becomes the next official version of the bill. This process differs from the traditional process in which an amendment document is drafted, itemizing changes to be made. When the committee approves the amendments, there is a mad rush, usually overnight, to implement (or execute) those amendments to the last version in order to produce the next version. Our Amendments in Context automated approach is more accurate and largely eliminates the overnight bottleneck of having to execute approved amendments before the start of business the following day.

Since implementing this system for California, we’ve been involved in a number of other jurisdictions and efforts that deal with the amending process. This has given us quite a good perspective on the various ways in which bill amendments get handled.

As software developers ourselves, we’ve often been struck by how similar the bill amendment process is to the software development process — the very thing that invariably leads to the suggestion that GitHub could be a great repository for legislation. With this all in mind, let’s compare and contrast the bill amending process with the software development process using GitHub.

(We’ll make suitable procedural simplifications to keep the example clear)

BILL AMENDING PROCESS SOFTWARE ENHANCEMENT PROCESS
Begin a proposed amendment Begin a proposed enhancement
Create a copy of the last version of a bill. In the U.S. and other parts of the world that still use page and line numbers, cleverly annotated page and line number information from the last publication must be included. This copy will be modified to reflect the proposed changes. Create a new software branch. This branch will be modified to implement the proposed enhancement
Make the proposed changes using redlining, showing the changes as insertions and deletions. Carefully craft the changes to obey the drafting rules and any political sensitivities regarding how the changes are shown. Make the proposed changes to the software — testing and debugging as needed.
Redlining Software
Generate the amendment Prepare to commit
The amendment generator examines the redlining (insertions and deletions), carefully grouping changes together to produce a minimized set of amendments. These amendments are expressed in the familiar, at least in the U.S., “on page X, line Y, strike ‘this’ and replace with ‘that'” or something along those lines. (For jurisdictions that don’t use an amendment generator, a manually written amendment document, enumerating the amendments, is the starting point) A differencing engine compares the source code with the prior version, carefully grouping changes together to produce a minimized set of hunks. If you use a tool such as SourceTree by Atlassian, these hunks are shown as source code with lines to be removed and lines to be inserted.
Amendment Hunks
Save the amendment document alongside the revised bill with redlining Commit the changes to GitHub
Vote on the amendments Submit for review
The amendment document goes to committee where it is proposed and then either adopted or rejected. The procedures here may differ, depending on the jurisdiction. In California, multiple competing amendment documents (known as instruction amendments) may be proposed at any one time, but only one can be adopted and it is adopted in whole. Other jurisdictions allow multiple amendment documents to be adopted and individual amendments with any amendment document to be adopted or rejected. The review board considers the proposed enhancement and decides whether or not to incorporate them into the next release. They may choose to adopt the entire enhancement or they may choose to adopt only certain aspects of it.
Execute the amendment Merge into mainline
In California, because only single whole amendments can be adopted, executing an adopted amendment is quite easy — the redlined version of the bill simply becomes the next version. However, in most jurisdictions, this isn’t so easy. Instead, each amendment must be applied to a new copy of the bill, destined to become the next version. Conflicts that arise must be resolved following a prescribed set of procedures. Incorporating an enhancement into the mainline involves a merge of the enhancement branch into the mainline. If an enhancement is not adopted in whole, then approved changes may be cherry picked. When conflicts between different sets of approved enhancements occur, GitHub requires manual intervention to resolve the issues. This process is generally a lot less formal than resolving conflicts in legislation.

So, as you can see, there are a lot of similarities between amending a bill and implementing a software enhancement. The basic process is essentially identical. However, the differences lie in the details.

Git is designed specifically for the software development process. The legislative process has quite a different set of requirements and traditions which must be met. It simply isn’t possible to bend and distort the legislative process to fit the model prescribed by Git. However, that doesn’t mean that something like GitHub is out of the question. What if there was a GitHub for Legislation — a tool with an associated repository, modeled after Git and GitHub, specifically designed for managing legislation?

This example shows the power of adopting XML for drafting legislation. With properly designed XML, legislation becomes a vast store of machine-readable information that can meet the 21st century challenges of accuracy, efficiency, and transparency. We’re not just printing paper anymore — we’re managing digital information.

Can GitHub be used to manage legislation?

Achieving Five Star Open Data

A couple weeks ago, I was in Ravenna, Italy at the LEX Summer School and follow-on Developer’s Workshop. There, the topic of a semantic web came up a lot. Despite cooling in the popular press in recent years, I’m still a big believer in the idea. The problem with the semantic web is that few people actually get it. At this point, it’s such an abstract idea that people invariably jump to the closest analog available today and mistake it for that.

Tim Berners-Lee (@timberners_lee), the inventor of the web and a big proponent of linked data, has suggested a five star deployment scheme for achieving open data — and what ultimately will be a semantic web. His chart can be thought of as a roadmap for how to get there.

Take a look at today’s Data.gov website. Everybody knows the problem with it — it’s a pretty wrapper around a dumping ground of open data. There are thousands and thousands of data sets available on a wide range of interesting topics. But, there is no unifying data model behind all these data dumps. Sometimes you’re directed to another pretty website that, while well-intentioned, hides the real information behind the decorations. Sometimes you can get a simple text file. If you’re lucky, you might even find the information in some more structured format such as a spreadsheet or XML file. Without any unifying model and with much of the data intended as downloads rather than as an information service, this is really still Tim’s first star of open data — even though some of the data is provided as spreadsheets or open data formats. It’s a good start, but there’s an awful long way to go.

So let’s imagine that a better solution is desired, providing information services, but keeping it all modest by using off-the-shelf technology that everyone is familiar with. Imagine that someone with the authority to do so, takes the initiative to mandate that henceforth, all government data will be produced as Excel spreadsheets. Every memo, report, regulation, piece of legislation, form that citizens fill out, and even the U.S. Code will be kept in Excel spreadsheets. Yes, you need to suspend disbelief to imagine this — the complications that would result would be incredibly tough to solve. But, imagine that all those hurdles were magically overcome.

What would it mean if all government information was stored as spreadsheets? What would be possible if all that information was available throughout the government in predictable and permanent locations? Let’s call the system that would result the Government Information Storehouse – a giant information repository for information regularized as Excel spreadsheets. (BTW, this would be the future of government publishing once paper and PDFs have become relics of the past.)

How would this information look? Think about a piece of legislation, for instance. Each section of the bill might be modeled as a single row in the spreadsheet. Every provision in that section would be it’s own spreadsheet cell (ignoring hierarchical considerations, etc.) Citations would turn into cell references or cell range references. Amending formulas, such as “Section 1234 of Title 10 is amended by…” could be expressed as a literal formula — a spreadsheet formula. It would refer to the specific cell in the appropriate U.S. Code Title and contain programmatic instructions for how to perform the amendment. In short, lots of once complex operations could be automated very efficiently and very precisely. Having the power to turn all government information into a giant spreadsheet has a certain appeal — even if it requires quite a stretch of the imagination.

Now imagine what it would mean if selected parts of this information were available to the public as these spreadsheets – in a regularized and permanent way — say Data.gov 2.0 or perhaps, more accurately, as Info.gov. Think of all the spreadsheet applications that would be built to tease out knowledge from the information that the government is providing through their information portal. Having the ability to programmatically monitor the government without having to resort to complex measures to extract the information would truly enable transparency.

At this point, while the linkages and information services give us some of the attributes of Tim’s four and five star open data solutions, but our focus on spreadsheet technology has left us with a less than desirable two star system. Besides, we all know that having the government publish everything as Excel spreadsheets is absurd. Not everything fits conveniently into a spreadsheet table to say nothing of the scalability problems that would result. I wouldn’t even want to try putting Title 42 of the U.S. Code into an Excel spreadsheet. So how do we really go about achieving this sort of open data and the efficiencies it enables — both inside and outside of government?

In order to realize true four and five star solutions, we need to quickly move on to fulfilling all the parts of Tim’s five star chart. In his chart, a three star solution replaces Excel spreadsheets with an open data format such as a comma separated file. I don’t actually care for this ordering because it sacrifices much to achieve the goal of having neutral file formats — so lets move on to full four and five star solutions. To get there, we need to become proficient in the open standards that exist and we must strive to create ones where they’re missing. That’s why we work so hard on the OASIS efforts to develop Akoma Ntoso and citations into standards for legal documents. And when we start producing real information services, we must ensure that the linkages in the information (those links and formulas I wrote about earlier), exist to the best extent possible. It shouldn’t be up to the consumer to figure out how a provision in a bill relates to a line item in some budget somewhere else — that linkage should be established from the get-go.

We’re working on a number of core pieces of technology to enable this vision and get to full five star open data. We integrating XML repositories and SQL databases into our architectures to give us the information storehouse I mentioned earlier. We’re building resolver technology that allows us to create and manage permanent linkages. These linkages can be as simple as citation references or as complex as instructions to extract from or make modifications to other information sources. Think of our resolver technology as akin to the engine in Excel than handles cell or range references, arithmetic formulas, and database lookups. And finally, we’re building editors that will resemble word processors in usage, but will allow complex sets of information to be authored and later modified. These editors will have many of the sophisticated capabilities such as track changes that you might see in a modern word processor, but underneath you will find a complex structured model rather than the ad hoc data structures of a word processor.

Building truly open data is going to be a challenging but exciting journey. The solutions that are in place today are a very primitive first step. Many new standards and technologies still need to be developed. But, we’re well on our way.

Achieving Five Star Open Data

Imagining Government Data in the 21st Century

After the 2014 Legislative Data and Transparency conference, I came away both encouraged and a little worried. I’m encouraged by the vast amount of progress we have seen in the past year, but at the same time a little concerned by how disjointed some of the initiatives seem to be. I would rather see new mandates forcing existing systems to be rethought rather than causing additional systems to be created – which can get very costly over time. But, it’s all still the Wild Wild West of computing.

What I want to do with my blog this week is try and define what I believe transparency is all about:

  1. The data must be available. First and foremost, the most important thing is that the data be provided at the very least – somehow, anyhow.
  2. The data must be provided in such a way that it is accessible and understandable by the widest possible audience. This means providing data formats that can be read by ubiquitous tools and, ensuring the coding necessary to support all types of readers including those with disabilities.
  3. The data must be provided in such a way that it should be easy for a computer to digest and analyze. This means using data formats that are easily parsed by a computer (not PDF, please!!!) and using data models that are comprehensible to widest possible audience of data analysts. Data formats that are difficult to parse or complex to understand should be discouraged. A transparent data format should not limit the visibility of the data to only those with very specialized tools or expertise.
  4. The data provided must be useful. This means that the most important characteristics of the data must be described in ways that allow it to be interpreted by a computer without too much work. For instance, important entities described by the data should be marked in ways that are easily found and characterized – preferably using broadly accepted open standards.
  5. The data must be easy to find. This means that the location at which data resides should be predictable, understandable, permanent, and reliable. It should reflect the nature of the data rather than the implementation of the website serving the data. URLs should be designed rather than simply fallout from the implementation.
  6. The data should be as raw as possible – but still comprehensible. This means that the data should have undergone as little processing as possible. The more that data is transformed, interpreted, or rearranged, the less like the original data it becomes. Processing data invariably damages its integrity – whether intentional or unintentional. There will always be some degree of healthy mistrust in data that has been over-processed.
  7. The data should be interactive. This means that it should be possible to search the data at its source – through both simple text search and more sophisticated data queries. It also means that whenever data is published, there should be an opportunity for the consumer to respond back – be it simple feedback, a formal request for change, or some other type of two way interaction.

How can this all be achieved for legislative data? This is the problem we are working to solve. We’re taking a holistic approach by designing data models that are both easy to understand and can be applied throughout the data life cycle. We’re striving to limit data transformations by designing our data models to present data in ways that are both understandable to humans and computers alike. We are defining URL schemes that are well thought out and could last for as long as URLs are how we find data in the digital era. We’re defining database solutions that allow data to not only be downloaded, but also searched and queried in place. We’re building tools that will allow the data to not only be created but also interacted with later. And finally, we’re working with standards bodies such as the LegalDocML and LegalCiteM technical committees at OASIS to ensure well thought out world wide standards such as Akoma Ntoso.

Take a look at Title 1 of the U.S. Code. If you’re using a reasonably modern web browser, you will notice that this data is very readable and understandable – its meant to be read by a human. Right click with the mouse and view the source. This is the USLM format that was released a year ago. If you’re familiar with the structure of the U.S. Code and you’re reasonably XML savvy, you should feel at ease with the data format. It’s meant to be understandable to both humans and to computer programs trying to analyze it. The objective here is to provide a single simple data model that is used from initial drafting all the way through publishing and beyond. Rather than transforming the XML into PDF and HTML forms, the XML format can be rendered into a readable form using Cascading Style Sheets (CSS). Modern XML repositories such as eXist allow documents such as this to be queried as easily as you would query a table in a relational database – using a query language called XQuery.

This is what we are doing – within the umbrella of legislative data. It’s a start, but ultimately there is a need for a broader solution. My hope is that government agencies will be able to come together under a common vision for our information should be created, published, and disseminated – in order to fulfill their evolving transparency mandates efficiently. As government agencies replace old systems with new systems, they should design around a common open framework for transparent data rather building new systems in the exact same footprint as the old systems that they demolish. The digital era and transparency mandates that have come with it demand new thinking far different than the thinking of the paper era which is now drawing to a close. If this can be achieved, then true data transparency can be achieved.

Imagining Government Data in the 21st Century

What is Transparency?

I’ve been thinking a lot about transparency lately. The disappearance of Malaysian Airline Flight 370 (MH370) provided an interesting case to look at – and some important lessons. Releasing data which requires great expertise to decipher isn’t transparency.

My boss, when I worked on process research at the Boeing Company many years ago, used to drill into me the difference between information and data. To him, data was raw – and meaningless unless you knew how to interpret it. Information, on the other hand, had the meaning applied so you could understand it – information, to him, was meaningful.

Let’s recall some of the details of the MH370 incident. The plane disappeared without a trace – for reasons that remain a mystery. The only useful information, after radar contact was lost, was a series of pings received by Inmarsat’s satellite. Using some very clever mathematics involving Doppler shifts, Inmarsat was able to use that data to plot a course for the lost plane. That course was revealed to the world and the search progressed. However, when that course failed to turn up the missing plane, there were increasingly angry calls for more transparency from Inmarsat – to reveal the raw data. Inmarsat’s response was that they had released the information, in the form of a plotted course, to the public and to the appropriate authorities, However, they chose to withhold the underlying data, claiming it wouldn’t be useful. The demands persisted, primarily from the press and the victims’ families. Eventually Inmarsat gave in and agreed to release the data. With great excitement, the press reported this as “Breaking News”. Then, a bewildered look seemed to come across everyone and the story quickly faded away. Inmarsat had provided the transparency in the form it was demanded, releasing the raw data along with a brief overview and the relevant data highlighted, but it still wasn’t particularly useful. We’re still waiting to hear if anyone will ever be able to find any new insights into whatever happened to MH370 using this data. Most likely though, that story has run its course – you simply need Inmarsat’s expertise to understand the data.

There is an important lesson to be learned – for better or worse. Raw data can be released, but without the tools and expertise necessary to intepret it, it’s meaningless. Is that transparency? Alternatively, raw data can be interpreted into meaningful information, but that opens up questions as to the honesty and accuracy of the interpretation. Is that transparency? It’s very easy to hide the facts in plain sight – by delivering it in a convoluted and indecipherable data format or by selectively interpreting it to tell an incomplete story. How do we manage transparency to achieve the objective of providing the public with an open, honest, and useful view of government activities?

Next week, I want to describe my vision for how government information should be made public. I want to tackle the conflicting needs of providing information that is both unfiltered yet comprehensible. While I don’t have the answers, I do want to start the process of clarifying what better transparency is really going to achieve.

What is Transparency?

Improving Legal References

In my blog last week, I talked a little about our efforts to improve how citations are handled. This week, I want to talk about this in some more detail. I’ve been participating on a few projects to improve how citations and references to legal citations are handled.

Let’s start by looking at the need. Have you noticed how difficult it is to lookup many citations found in legislation published on the web? Quite often, there is no link associated with the citation. You’re left to do your own legwork if you want to lookup that citation – which probably means you’ll take the author’s word for it and not bother to follow the citation. Sometimes, if you’re lucky, you will find a link (or reference) associated with the citation. It will point to a location, chosen by the author, that contains a copy of the legal text being referenced.

What’s the problem with these references?

  • If you take a look at the reference, chances are it’s a crufty URL containing all sorts of gibberish that’s either difficult or impossible to interpret. The URL reflects the current implementation of the data provider. It’s not intended to be meaningful. It follows no common conventions for how to describe a legal provision.
  • Wait a few years and try and follow that link again. Chances are, that link will now be broken. The data provider may have redesigned their site or it might not even exist anymore. You’re left with a meaningless link that points to nowhere.
  • Even if the data does exist, what’s the quality of the data at the other end of the link. Is the text official text, a copy, or even a derivative of the official text? Has the provision been amended? Has it been renumbered? Has it been repealed? What version of that provision are you looking at now? These questions are all hard to answer.
  • When you retrieve the data, what format does it come in? Is it a PDF? What if you want the underlying XML? If that is available, how do you get it?
  • The object of our efforts, both at the standards committee and within the projects we’re working on at Xcential, is to tackle this problem. The approach being taken involves properly designing meaningful URLs which are descriptive, unambiguous, and can last for a very long time – perhaps decades or longer. These URLs are independent of the current implementation – they may not reflect how the data is stored at all. The job of figuring out how to retrieve the data, using the current underlying content management system, is the job of a “resolver”. A resolver is simply an adapter that is attached to a web server. It intercepts the properly designed URL references and then transparently maps them into the crufty old URLs which the content management system requires. The data is retrieved from the content management system, formatted appropriately, and returned as if it really did exist at the property designed URL which you see. As the years go by and technologies advance, the resolver can be adapted to handle new generations of content management system. The references themselves will never need to change.

    There are many more details to go into. I’ll leave those for future blogs. Some of the problems we are tackling involve mapping popular names into real citations, working through ambiguities (including ones created in the future), handling alternate data sources, and allowing citations to be retrieved at varying degrees of granularity.

    I believe that solving the legal references problem is just about the most important progress we can make towards improving the legal informatics field. It’s an exciting time to be working in this field.

Improving Legal References

Transparent legislation should be easy to read

Legislation is difficult to read and understand. So difficult that it largely goes unread. This is something I learned when I first started building bill drafting systems over a decade ago. It was quite a let down. The people you would expect to read legislation don’t actually do that. Instead they must rely on analyses, sometimes biased, performed by others that omits many of the nuances found within the legislation itself.

Much of the problem is how legislation is written. Legislation is often written so as to concisely describe a set of changes to be made to existing law. The result is a document that is written to be executed by a law compilation team deep within the government rather than understood by law makers or the general public. This article, by Robert Potts, rather nicely sums up the problem.

Note: There is a technical error in the article by Robert Potts. The author states “These statutes are law, but since Congress has not written them directly to the Code, they are added to the Code as ‘notes,’ which are not law. So even when there is a positive law Title, because Congress has screwed it up, amendments must still be written to individual statutes.” This is not accurate. Statutory notes are law. This is explained in Part IV (E) of the DETAILED GUIDE TO THE CODE CONTENT AND FEATURES.

So how can legislation be made more readable and hence more transparent? The change must come in how amendments are written – with an intent to communicate the changes rather than just to describe them. Let’s start by looking at a few different ways that amendments can be written:

1) Cut-and-Bite Amendments

Many jurisdiction around the world use the cut-and-bite approach to amending, also known as amendments by reference. This includes Congress here in the U.S., but it is also common to most of the other jurisdictions I work with. Let’s take a look at a hypothetical cut-and-bite amendment:

SECTION 1. Section 1234 of the Labor Code is amended by repealing “$7.50” and substituting “$8.50”.

There is no context to this amendment. In order to understand this amendment, someone is going to have to go look up Section 1234 of the Labor Code and manually make apply the change to see what it is all about. While this contrived example is simple, it already involves a fair amount of work. When you extrapolate this problem to a real bill and the sometimes convoluted state of the law, the effort to understand a piece of legislation quickly becomes mind-boggling. For a real bill, few people are going to have either the time or the resources to adequately research all the amendments to truly understand how they will affect the law.

2) Amendments Set Out in Full

I’ve come to appreciate the way the California Legislature handles this problem. The cut-and-bite style of amending, as described above, is simply disallowed. Instead, all amendments must be set out in full – by re-enacting the section in full as amended. This is mandated by Article 4, section 9 of the California Constitution. What this means is that the amendment above must instead be written as:

Section 1. Section 1234 of the Labor Code is amended to read:

1234. Notwithstanding any other provision of this part, the minimum wage for all industries shall be not less than $8.50 per hour.

This is somewhat better. Now we can see that we’re affecting the minimum wage – we have the context. The wording of the section, as amended, is set out in full. It’s clear and much more transparent.

However, it’s still not perfect. While we can see how the amended law will read when enacted, we don’t actually know what changed. Actually, in California, if you paid attention to the bill redlining through its various stages, you could have tracked the changes through the various versions to arrive at the net effect of the amendment. (See note on redlining) Unfortunately, the redlining rules are a bit convoluted and not nearly as apparent as they might seem to be – they’re misleading to the uninitiated. What’s more, the resulting statute at the end of the process has no redlining so the effect of the change is totally hidden in the enacted result.

Setting out amendments in full has been adopted by many states in addition to California. It is both more transparent and greatly eases the codification process. The codification process becomes simple because the new sections, set out in full, are essentially prefabricated blocks awaiting insertion into the law at enactment time. Any problems which may result from conflicting amendments are, by necessity, resolved earlier rather than later. (although this does bring along its own challenges)

3) Amendments in Context

There is an even better approach – which is adopted to varying degrees by a few legislatures. It is to build on the approach of setting out sections in full, but adds a visible indication of what has changed using strike and insert notation. I’ll refer to this as Amendments in Context.

This problem is partially addressed, at the federal level, by the Ramseyer Rule which requires that a separate document be published which essentially does shows all amendments in context. The problem is that this second document isn’t generally available – and it’s yet another separate document.

Why not just write the legislation showing the amendments in context to begin with? I can think of no reason other than tradition why the law, as proposed and enacted, shouldn’t show all amendments in context. Let’s take a look at this approach:

Section 1. Section 1234 of the Labor Code is amended to read:

1234. Notwithstanding any other provision of this part, the minimum wage for all industries shall be not less than $7.50 $8.50 per hour.

Isn’t this much clearer? At a glance we can see that the minimum wage is being raised a dollar. It’s obvious – and much more transparent.

At Xcential, we address this problem in California by providing an amendments in context view for all state legislation within our LegisWeb bill tracking service. We call this feature As Amends the LawTM and it is computed on-the-fly.

Governments are spending a lot of time, energy, and money on legislative transparency. The progress we see today is in making the data more accessible to computer analysis. Amendments in context would make the legislation not only more accessible to computer analysis – but also more readable and understandable to people.

Redlining Note: If redlining is a new term to you, it is similar to, but subtly different, to track changes in a word processor.

Transparent legislation should be easy to read