The CFPB has faced several challenges to its existence over its 11 years in business. In , the Supreme Court ruled that restrictions on when its leader can be removed were unconstitutional, but rejected a plea to strike down the agency as a whole. The most significant fear from progressive lawmakers and consumer groups is that the CFPB could see its resources chopped if left to the whims of Congress.

Public Interest Research Group. The new court decision comes as the CFPB, under Biden-appointed director Rohit Chopra , has taken a more aggressive stance toward the financial industry than his Trump administration predecessors.

Chopra has also promised scrutiny over the way large technology companies are expanding into financial services.

But the agency is also taking up initiatives with fintech industry support, including finally setting up open-banking rules to guide data-sharing between financial institutions and tech companies. What the ruling means for the fintech industry remains to be seen. While regulators and companies can occasionally come into conflict, the agencies also serve an important role in providing rules of the road and certainty for business models. His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr.

The ways Zia Faruqui right has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Veronica Irwin vronirwin is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc. One hundred percent electronic. The author is Magistrate Judge Zia Faruqui. His rulings have made smart references to "The Big Lebowski," "Dr. Strangelove," and "SNL" parodies of the McLaughlin Group.

Rather, before taking the judge position Faruqui was one of a group of prosecutors in the U. There, Faruqui prosecuted cases that involved terrorism, child pornography, and weapons proliferation. But the ways Faruqui has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster. Crypto lawyers have drawn on his prior decisions in the context of the Tornado Cash sanctions, for example.

Faruqui spoke with Protocol about the power of his position, and what people in crypto should understand about the law. There was another prosecutor, Christopher Brown — you know, the other Chris Brown — and he had taken an interest in this when we were both working on financial crime in the Washington, D.

Our U. attorney at the time, Jessie Liu, had this idea of using financial investigations in a way that was not limited to just white collar crime, or even narcotics cases, but also for cyber investigations, to national security investigations, and in civil cases.

A lot of what we were investigating was related to following the money and so she wanted us to be this multidisciplinary unit. But I have to say, we started with the goal of wanting to make T-shirts, and we never did that while I was there.

Your decisions have also gotten a lot of attention. We're public servants! And in order for the public to have faith and trust us, they need to understand what it is that we're doing and what we're saying. Humor is one way, not using a lot of legalese is another way. But I think there are many judges who are trying to make the judiciary more accessible, and so people can see the work that we're doing and understand what we're doing and then make their own opinions about if it's right or wrong.

But at least, if it's understandable, then there's still some trust in the framework even if you don't agree with how our decisions are stated. We are ambassadors for the judiciary to the people in our courtroom — it's a very frightening proposition being in court if you've been federally charged, and people have perceptions of what they think can happen there in terms of fairness or unfairness.

But then it goes far beyond that. I do a lot of work with the Administrative Office of the Courts, our central body doing civic education and outreach to high schools, because I want college and high school students and law students to have an experience where they get a chance to talk to a judge.

So my goal is certainly not just getting to one segment of the population, but it's making decisions accessible to whoever's interested in reading them. What has it felt like for you switching from that prosecutor role to magistrate judge? Lawyers are trying to take different frameworks from one topic and apply them to another, and then convince you that that is or is not appropriate.

Being a judge is very different because you're evaluating what the parties present to you as the applicable legal frameworks, and deciding how new, groundbreaking technology fits into legal frameworks that were written 10 or 15 years ago. But that's not really a place where judges get involved in saying how it ought to be regulated.

There was, famously, a judge in Florida that said cryptocurrency was not money because you couldn't put it underneath your bed, and that's what money is: something that is tangible. So different people are going to have different decisions. And that's not just true for crypto, but also other areas of the law. Your best-known crypto decisions strongly assert that crypto is traceable. One way people try to make it less traceable is with mixers, and Tornado Cash was sanctioned by OFAC not too long ago.

Do you think the legal reasoning was sound enough for similar sanctions to be applied to other mixers, or decentralized exchanges? I don't know. I think there's been some discussion that people may litigate some of these things, so I can't comment, because those frequently do come to our courthouse. And I think there are certainly people opining on that, yes and no. So much of what judges do is that we rely on the parties that are before us to tell us what's right and what's wrong.

And then, you know, obviously, they'll have different views, and we make a decision based on what people say in front of us. Are you aware that some legal analysis of the Tornado Cash sanctions references your recent decision in a cryptocurrency sanctions case? That's what good lawyers will always do. Even legislators might look at that as they try to think about where the gaps are.

As a prosecutor I had a case where we sued three Chinese banks to give us their bank records, and it had never been done before. Afterwards, Congress passed a new law, using the decisions from judges in this court and the D. circuit court, the court above us.

So I'm sure people look at prior decisions and try to apply them in the ways that they want to. Are there any misconceptions about how the law applies to crypto, or how your decisions should be interpreted, that you wish you could get across? One misconception is that the judges can't understand this technology — we can. People have these views in two extremes. The lawyer's fundamental job is to take super complex and technical things and boil them down to very easily digestible arguments for a judge, for a jury, or whoever it might be.

The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance. Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more.

Financial technology is breaking down barriers to financial services and delivering value to consumers, small businesses, and the economy. Fintech puts American consumers at the center of their finances and helps them manage their money responsibly.

From payment apps to budgeting and investing tools and alternative credit options, fintech makes it easier for consumers to pay for their purchases and build better financial habits. Fintech also arms small businesses with the financial tools for success, including low-cost banking services, digital accounting services, and expanded access to capital.

We advocate for modernized financial policies and regulations that allow fintech innovation to drive competition in the economy and expand consumer choice. Spots are still available for this hybrid event, and you can RSVP here to save your seat. Join us as we discuss how to shape the future of finance.

In its broadest sense, Open Banking has created a secure and connected ecosystem that has led to an explosion of new and innovative solutions that benefit the customer, rapidly revolutionizing not just the banking industry but the way all companies do business. Target benefits are delivered through speed, transparency, and security, and their impact can be seen across a diverse range of use cases.

Sharing financial data across providers can enable a customer individual or business to have real-time access to multiple bank accounts across multiple institutions all in one platform, saving time and helping consumers get a more accurate picture of their own finances before taking on debt, providing a more reliable indication than most lending guidelines currently do. Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need.

Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships. The increased transparency brought about by Open Banking brings a vast array of additional benefits, such as helping fraud detection companies better monitor customer accounts and identify problems much earlier.

The list of new value-add solutions continues to grow. The speed of business has never been faster than it is today. For small business owners, time is at a premium as they are wearing multiple hats every day. Macroeconomic challenges like inflation and supply chain issues are making successful money and cash flow management even more challenging.

This presents a tremendous opportunity that innovation in fintech can solve by speeding up money movement, increasing access to capital, and making it easier to manage business operations in a central place. Fintech offers innovative products and services where outdated practices and processes offer limited options.

For example, fintech is enabling increased access to capital for business owners from diverse and varying backgrounds by leveraging alternative data to evaluate creditworthiness and risk models. This can positively impact all types of business owners, but especially those underserved by traditional financial service models.

When we look across the Intuit QuickBooks platform and the overall fintech ecosystem, we see a variety of innovations fueled by AI and data science that are helping small businesses succeed. By efficiently embedding and connecting financial services like banking, payments, and lending to help small businesses, we can reinvent how SMBs get paid and enable greater access to the vital funds they need at critical points in their journey.

Overall, we see fintech as empowering people who have been left behind by antiquated financial systems, giving them real-time insights, tips, and tools they need to turn their financial dreams into a reality. Innovations in payments and financial technologies have helped transform daily life for millions of people. People who are unbanked often rely on more expensive alternative financial products AFPs such as payday loans, money orders, and other expensive credit facilities that typically charge higher fees and interest rates, making it more likely that people have to dip into their savings to stay afloat.

A few examples include:. Mobile wallets - The unbanked may not have traditional bank accounts but can have verified mobile wallet accounts for shopping and bill payments.

Their mobile wallet identity can be used to open a virtual bank account for secure and convenient online banking. Minimal to no-fee banking services - Fintech companies typically have much lower acquisition and operating costs than traditional financial institutions. They are then able to pass on these savings in the form of no-fee or no-minimum-balance products to their customers.

This enables immigrants and other populations that may be underbanked to move up the credit lifecycle to get additional forms of credit such as auto, home and education loans, etc.

Entrepreneurs from every background, in every part of the world, should be empowered to start and scale global businesses. Most businesses still face daunting challenges with very basic matters. These are still very manually intensive processes, and they are barriers to entrepreneurship in the form of paperwork, PDFs, faxes, and forms. Stripe is working to solve these rather mundane and boring challenges, almost always with an application programming interface that simplifies complex processes into a few clicks.

Stripe powers nearly half a million businesses in rural America. The internet economy is just beginning to make a real difference for businesses of all sizes in all kinds of places. We are excited about this future. The way we make decisions on credit should be fair and inclusive and done in a way that takes into account a greater picture of a person.

Lenders can better serve their borrowers with more data and better math. Zest AI has successfully built a compliant, consistent, and equitable AI-automated underwriting technology that lenders can utilize to help make their credit decisions. While artificial intelligence AI systems have been a tool historically used by sophisticated investors to maximize their returns, newer and more advanced AI systems will be the key innovation to democratize access to financial systems in the future.

D espite privacy, ethics, and bias issues that remain to be resolved with AI systems, the good news is that as large r datasets become progressively easier to interconnect, AI and related natural language processing NLP technology innovations are increasingly able to equalize access. T he even better news is that this democratization is taking multiple forms.

AI can be used to provide risk assessments necessary to bank those under-served or denied access. AI systems can also retrieve troves of data not used in traditional credit reports, including personal cash flow, payment applications usage, on-time utility payments, and other data buried within large datasets, to create fair and more accurate risk assessments essential to obtain credit and other financial services.

By expanding credit availability to historically underserved communities, AI enables them to gain credit and build wealth. Additionally, personalized portfolio management will become available to more people with the implementation and advancement of AI. Sophisticated financial advice and routine oversight, typically reserved for traditional investors, will allow individuals, including marginalized and low-income people, to maximize the value of their financial portfolios.

Moreover, when coupled with NLP technologies, even greater democratization can result as inexperienced investors can interact with AI systems in plain English, while providing an easier interface to financial markets than existing execution tools.

Open finance technology enables millions of people to use the apps and services that they rely on to manage their financial lives — from overdraft protection, to money management, investing for retirement, or building credit.

More than 8 in 10 Americans are now using digital finance tools powered by open finance. This is because consumers see something they like or want — a new choice, more options, or lower costs. What is open finance? At its core, it is about putting consumers in control of their own data and allowing them to use it to get a better deal.

The · base type · of normalizedString is string. normalizedString has the following · constraining facets · :. The following · built-in · datatypes are · derived · from normalizedString :. The · value space · of token is the set of strings that do not contain the carriage return xD , line feed xA nor tab x9 characters, that have no leading or trailing spaces x20 and that have no internal sequences of two or more spaces. The · lexical space · of token is the set of strings that do not contain the carriage return xD , line feed xA nor tab x9 characters, that have no leading or trailing spaces x20 and that have no internal sequences of two or more spaces.

The · base type · of token is normalizedString. token has the following · constraining facets · :. The following · built-in · datatypes are · derived · from token :.

The · value space · of language is the set of all strings that are valid language identifiers as defined [RFC ]. The · base type · of language is token. language has the following · constraining facets · :.

The · value space · of NMTOKEN is the set of tokens that · match · the Nmtoken production in [XML 1. The · lexical space · of NMTOKEN is the set of strings that · match · the Nmtoken production in [XML 1.

The · base type · of NMTOKEN is token. NMTOKEN has the following · constraining facets · :. The following · built-in · datatypes are · derived · from NMTOKEN :. The · value space · of NMTOKENS is the set of finite, non-zero-length sequences of · NMTOKEN · s.

The · lexical space · of NMTOKENS is the set of space-separated lists of tokens, of which each token is in the · lexical space · of NMTOKEN. The · itemType · of NMTOKENS is NMTOKEN. NMTOKENS has the following · constraining facets · :. The · value space · of Name is the set of all strings which · match · the Name production of [XML 1. The · lexical space · of Name is the set of all strings which · match · the Name production of [XML 1. The · base type · of Name is token.

Name has the following · constraining facets · :. The following · built-in · datatypes are · derived · from Name :. The · value space · of NCName is the set of all strings which · match · the NCName production of [Namespaces in XML]. The · lexical space · of NCName is the set of all strings which · match · the NCName production of [Namespaces in XML].

The · base type · of NCName is Name. NCName has the following · constraining facets · :. The following · built-in · datatypes are · derived · from NCName :. The · value space · of ID is the set of all strings that · match · the NCName production in [Namespaces in XML]. The · lexical space · of ID is the set of all strings that · match · the NCName production in [Namespaces in XML]. The · base type · of ID is NCName.

ID has the following · constraining facets · :. The · value space · of IDREF is the set of all strings that · match · the NCName production in [Namespaces in XML]. The · lexical space · of IDREF is the set of strings that · match · the NCName production in [Namespaces in XML]. The · base type · of IDREF is NCName. IDREF has the following · constraining facets · :. The following · built-in · datatypes are · derived · from IDREF :. The · value space · of IDREFS is the set of finite, non-zero-length sequences of IDREF s.

The · lexical space · of IDREFS is the set of space-separated lists of tokens, of which each token is in the · lexical space · of IDREF. The · itemType · of IDREFS is IDREF. IDREFS has the following · constraining facets · :. The · value space · of ENTITY is the set of all strings that · match · the NCName production in [Namespaces in XML] and have been declared as an unparsed entity in a document type definition.

The · lexical space · of ENTITY is the set of all strings that · match · the NCName production in [Namespaces in XML]. The · base type · of ENTITY is NCName. ENTITY has the following · constraining facets · :.

The following · built-in · datatypes are · derived · from ENTITY :. The · value space · of ENTITIES is the set of finite, non-zero-length sequences of · ENTITY · s that have been declared as unparsed entities in a document type definition. The · lexical space · of ENTITIES is the set of space-separated lists of tokens, of which each token is in the · lexical space · of ENTITY.

The · itemType · of ENTITIES is ENTITY. ENTITIES has the following · constraining facets · :. This results in the standard mathematical concept of the integer numbers.

The · value space · of integer is the infinite set { The · base type · of integer is decimal. integer has a lexical representation consisting of a finite-length sequence of decimal digits x x39 with an optional leading sign. The canonical representation for integer is defined by prohibiting certain options from the Lexical representation §3.

integer has the following · constraining facets · :. The following · built-in · datatypes are · derived · from integer :. This results in the standard mathematical concept of the non-positive integers. The · value space · of nonPositiveInteger is the infinite set { The · base type · of nonPositiveInteger is integer.

nonPositiveInteger has a lexical representation consisting of an optional preceding sign followed by a finite-length sequence of decimal digits x x For example: -1, 0, , The canonical representation for nonPositiveInteger is defined by prohibiting certain options from the Lexical representation §3.

In the canonical form for zero, the sign must be omitted. Leading zeroes are prohibited. nonPositiveInteger has the following · constraining facets · :. The following · built-in · datatypes are · derived · from nonPositiveInteger :.

This results in the standard mathematical concept of the negative integers. The · value space · of negativeInteger is the infinite set { The · base type · of negativeInteger is nonPositiveInteger. negativeInteger has a lexical representation consisting of a negative sign "-" followed by a finite-length sequence of decimal digits x x For example: -1, , The canonical representation for negativeInteger is defined by prohibiting certain options from the Lexical representation §3.

Specifically, leading zeroes are prohibited. negativeInteger has the following · constraining facets · :. The · base type · of long is integer. long has a lexical representation consisting of an optional sign followed by a finite-length sequence of decimal digits x x The canonical representation for long is defined by prohibiting certain options from the Lexical representation §3.

long has the following · constraining facets · :. The following · built-in · datatypes are · derived · from long :. The · base type · of int is long. int has a lexical representation consisting of an optional sign followed by a finite-length sequence of decimal digits x x The canonical representation for int is defined by prohibiting certain options from the Lexical representation §3.

int has the following · constraining facets · :. The following · built-in · datatypes are · derived · from int :. The · base type · of short is int. short has a lexical representation consisting of an optional sign followed by a finite-length sequence of decimal digits x x The canonical representation for short is defined by prohibiting certain options from the Lexical representation §3.

short has the following · constraining facets · :. The following · built-in · datatypes are · derived · from short :. The · base type · of byte is short.

byte has a lexical representation consisting of an optional sign followed by a finite-length sequence of decimal digits x x The canonical representation for byte is defined by prohibiting certain options from the Lexical representation §3. byte has the following · constraining facets · :. This results in the standard mathematical concept of the non-negative integers.

The · value space · of nonNegativeInteger is the infinite set {0,1,2, The · base type · of nonNegativeInteger is integer. nonNegativeInteger has a lexical representation consisting of an optional sign followed by a finite-length sequence of decimal digits x x The canonical representation for nonNegativeInteger is defined by prohibiting certain options from the Lexical representation §3.

nonNegativeInteger has the following · constraining facets · :. The following · built-in · datatypes are · derived · from nonNegativeInteger :. The · base type · of unsignedLong is nonNegativeInteger. unsignedLong has a lexical representation consisting of a finite-length sequence of decimal digits x x For example: 0, , The canonical representation for unsignedLong is defined by prohibiting certain options from the Lexical representation §3.

unsignedLong has the following · constraining facets · :. The following · built-in · datatypes are · derived · from unsignedLong :. The · base type · of unsignedInt is unsignedLong. unsignedInt has a lexical representation consisting of a finite-length sequence of decimal digits x x The canonical representation for unsignedInt is defined by prohibiting certain options from the Lexical representation §3. unsignedInt has the following · constraining facets · :.

The following · built-in · datatypes are · derived · from unsignedInt :. The · base type · of unsignedShort is unsignedInt. unsignedShort has a lexical representation consisting of a finite-length sequence of decimal digits x x The canonical representation for unsignedShort is defined by prohibiting certain options from the Lexical representation §3.

Specifically, the leading zeroes are prohibited. unsignedShort has the following · constraining facets · :. The following · built-in · datatypes are · derived · from unsignedShort :.

The · base type · of unsignedByte is unsignedShort. unsignedByte has a lexical representation consisting of a finite-length sequence of decimal digits x x The canonical representation for unsignedByte is defined by prohibiting certain options from the Lexical representation §3.

unsignedByte has the following · constraining facets · :. This results in the standard mathematical concept of the positive integer numbers. The · value space · of positiveInteger is the infinite set {1,2, The · base type · of positiveInteger is nonNegativeInteger.

The canonical representation for positiveInteger is defined by prohibiting certain options from the Lexical representation §3. positiveInteger has the following · constraining facets · :. The following sections provide full details on the properties and significance of each kind of schema component involved in datatype definitions.

For each property, the kinds of values it is allowed to have is specified. Any property not identified as optional is required to be present; optional properties which are not present have absent as their value.

Any property identified as a having a set, subset or · list · value may have an empty value unless this is explicitly ruled out: this is not the same as absent.

Any property value identified as a superset or a subset of some set may be equal to that set, unless a proper superset or subset is explicitly called for. For more information on the notion of datatype schema components, see Schema Component Details of [XML Schema Part 1: Structures]. Datatypes are identified by their {name} and {target namespace}. Except for anonymous datatypes those with no {name} , datatype definitions · must · be uniquely identified within a schema.

If {variety} is · atomic · then the · value space · of the datatype defined will be a subset of the · value space · of {base type definition} which is a subset of the · value space · of {primitive type definition}. If {variety} is · list · then the · value space · of the datatype defined will be the set of finite-length sequence of values from the · value space · of {item type definition}.

If {variety} is · union · then the · value space · of the datatype defined will be the union of the · value space · s of each datatype in {member type definitions}. If {variety} is · atomic · then the {variety} of {base type definition} must be · atomic ·. If {variety} is · list · then the {variety} of {item type definition} must be either · atomic · or · union ·.

If {variety} is · union · then {member type definitions} must be a list of datatype definitions. The value of {facets} consists of the set of · facet · s specified directly in the datatype definition unioned with the possibly empty set of {facets} of {base type definition}. The value of {fundamental facets} consists of the set of · fundamental facet · s and their values.

If {final} is the empty set then the type can be used in deriving other types; the explicit values restriction , list and union prevent further derivations by · restriction · , · list · and · union · respectively. The correspondences between the properties of the information item and properties of the component are as follows:.

A · derived · datatype can be · derived · from a · primitive · datatype or another · derived · datatype by one of three means: by restriction , by list or by union.

A · list · datatype must be · derived · from an · atomic · or a · union · datatype, known as the · itemType · of the · list · datatype. This yields a datatype whose · value space · is composed of finite-length sequences of values from the · value space · of the · itemType · and whose · lexical space · is composed of space-separated lists of literals of the · itemType ·.

As mentioned in List datatypes §2. regardless of the · constraining facet · s that are applicable to the · atomic · datatype that serves as the · itemType · of the · list ·.

A · union · datatype can be · derived · from one or more · atomic · , · list · or other · union · datatypes, known as the · memberTypes · of that · union · datatype. As mentioned in Union datatypes §2. regardless of the · constraining facet · s that are applicable to the datatypes that participate in the · union ·. There is a simple type definition nearly equivalent to the simple version of the ur-type definition present in every schema by definition.

It has the following properties:. Every · value space · supports the notion of equality, with the following rules:. A · partial order · has the following properties:. A · total order · has all of the properties specified above for · partial order · , plus the following property:. When {variety} is · atomic · , {value} is inherited from {value} of {base type definition}. For all · primitive · types {value} is as specified in the table in Fundamental Facets §C.

When {variety} is · list · , {value} is false. When {variety} is · union · , {value} is partial unless one of the following:. When {variety} is · atomic · , if one of · minInclusive · or · minExclusive · and one of · maxInclusive · or · maxExclusive · are among {facets} , then {value} is true ; else {value} is false.

When {variety} is · list · , if · length · or both of · minLength · and · maxLength · are among {facets} , then {value} is true ; else {value} is false. When {variety} is · union · , if {value} is true for every member of {member type definitions} and all members of {member type definitions} share a common ancestor, then {value} is true ; else {value} is false.

Some · value space · s are finite, some are countably infinite while still others could conceivably be uncountably infinite although no · value space · defined by this specification is uncountable infinite. A datatype is said to have the cardinality of its · value space ·. It is sometimes useful to categorize · value space · s and hence, datatypes as to their cardinality.

There are two significant cases:. When {variety} is · atomic · and {value} of {base type definition} is finite , then {value} is finite. When {variety} is · atomic · and {value} of {base type definition} is countably infinite and either of the following conditions are true, then {value} is finite ; else {value} is countably infinite :. When {variety} is · list · , if · length · or both of · minLength · and · maxLength · are among {facets} , then {value} is finite ; else {value} is countably infinite.

When {variety} is · union · , if {value} is finite for every member of {member type definitions} , then {value} is finite ; else {value} is countably infinite. When {variety} is · union · , if {value} is true for every member of {member type definitions} , then {value} is true ; else {value} is false. The value of length · must · be a nonNegativeInteger. For string and datatypes · derived · from string , length is measured in units of character s as defined in [XML 1.

For anyURI , length is measured in units of characters as for string. For hexBinary and base64Binary and datatypes · derived · from them, length is measured in octets 8 bits of binary data.

For datatypes · derived · by · list · , length is measured in number of list items. If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for length other than {value}. The use of · length · on datatypes · derived · from QName and NOTATION is deprecated.

Future versions of this specification may remove this facet for these datatypes. The value of minLength · must · be a nonNegativeInteger. For string and datatypes · derived · from string , minLength is measured in units of character s as defined in [XML 1. For hexBinary and base64Binary and datatypes · derived · from them, minLength is measured in octets 8 bits of binary data. For datatypes · derived · by · list · , minLength is measured in number of list items.

If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for minLength other than {value}. The use of · minLength · on datatypes · derived · from QName and NOTATION is deprecated.

The value of maxLength · must · be a nonNegativeInteger. For string and datatypes · derived · from string , maxLength is measured in units of character s as defined in [XML 1.

For hexBinary and base64Binary and datatypes · derived · from them, maxLength is measured in octets 8 bits of binary data. For datatypes · derived · by · list · , maxLength is measured in number of list items. If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for maxLength other than {value}.

The use of · maxLength · on datatypes · derived · from QName and NOTATION is deprecated. The value of pattern · must · be a · regular expression ·.

enumeration does not impose an order relation on the · value space · it creates; the value of the · ordered · property of the · derived · datatype remains that of the datatype from which it is · derived ·. The value of whiteSpace must be one of {preserve, replace, collapse}. whiteSpace is applicable to all · atomic · and · list · datatypes. For all · atomic · datatypes other than string and types · derived · by · restriction · from it the value of whiteSpace is collapse and cannot be changed by a schema author; for string the value of whiteSpace is preserve ; for any type · derived · by · restriction · from string the value of whiteSpace can be any of the three legal values.

For all datatypes · derived · by · list · the value of whiteSpace is collapse and cannot be changed by a schema author. For all datatypes · derived · by · union · whiteSpace does not apply directly; however, the normalization behavior of · union · types is controlled by the value of whiteSpace on that one of the · memberTypes · against which the · union · is successfully validated.

If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for whiteSpace other than {value}. The value of maxInclusive · must · be in the · value space · of the · base type ·. If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for maxInclusive other than {value}.

The value of maxExclusive · must · be in the · value space · of the · base type · or be equal to {value} in {base type definition}. If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for maxExclusive other than {value}. The value of minExclusive · must · be in the · value space · of the · base type · or be equal to {value} in {base type definition}.

If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for minExclusive other than {value}. The value of minInclusive · must · be in the · value space · of the · base type ·. If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for minInclusive other than {value}.

The value of totalDigits · must · be a positiveInteger. The term totalDigits is chosen to reflect the fact that it restricts the · value space · to those values that can be represented lexically using at most totalDigits digits.

Note that it does not restrict the · lexical space · directly; a lexical representation that adds additional leading zero digits or trailing fractional zero digits is still permitted.

If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for totalDigits other than {value}. The value of fractionDigits · must · be a nonNegativeInteger. The term fractionDigits is chosen to reflect the fact that it restricts the · value space · to those values that can be represented lexically using at most fractionDigits to the right of the decimal point.

Note that it does not restrict the · lexical space · directly; a non- · canonical lexical representation · that adds additional leading zero digits or trailing fractional zero digits is still permitted. If {fixed} is true , then types for which the current type is the {base type definition} cannot specify a value for fractionDigits other than {value}. This specification describes two levels of conformance for datatype processors.

The first is required of all processors. Support for the other will depend on the application environments for which the processor is intended. The following table shows the values of the fundamental facets for each · built-in · datatype.

The · primitive · datatypes duration , dateTime , time , date , gYearMonth , gMonthDay , gDay , gMonth and gYear use lexical formats inspired by [ISO ].

Following [ISO ] , the lexical forms of these datatypes can include only the characters 20 through 7F. This appendix provides more detail on the ISO formats and discusses some deviations from them for the datatypes defined in this specification.

The proleptic Gregorian calendar includes dates prior to the year it came into use as an ecclesiastical calendar. It should be pointed out that the datatypes described in this specification do not cover all the types of data covered by [ISO ] , nor do they support all the lexical representations for those types of data.

The allowed decimal digits are x x For the primitive datatypes dateTime , time , date , gYearMonth , gMonthDay , gDay , gMonth and gYear.

these characters have the following meanings:. For all the information items indicated by the above characters, leading zeros are required where indicated. In addition to the above, certain characters are used as designators and appear as themselves in lexical formats. In the lexical format for duration the following characters are also used as designators and appear as themselves in lexical formats:. The values of the Year, Month, Day, Hour and Minutes components are not restricted but allow an arbitrary integer.

Similarly, the value of the Seconds component allows an arbitrary decimal. Thus, the lexical format for duration and datatypes derived from it does not follow the alternative format of § 5. Truncated formats are, in general, not permitted for the datatypes defined in this specification with three exceptions. The time datatype uses a truncated format for dateTime which represents an instant of time that recurs every day.

Similarly, the gMonthDay and gDay datatypes use left-truncated formats for date. The datatype gMonth uses a right and left truncated format for date. Right truncated formats are also, in general, not permitted for the datatypes defined in this specification with the following exceptions: right-truncated representations of dateTime are used as lexical representations for date , gMonth , gYear.

An optional minus sign is allowed immediately preceding, without a space, the lexical representations for duration , dateTime , date , gYearMonth , gYear. To accommodate year values greater than , more than four digits are allowed in the year representations of dateTime , date , gYearMonth , and gYear.

This follows [ISO Second Edition]. The lexical representations for the datatypes date , gYearMonth , gMonthDay , gDay , gMonth and gYear permit an optional trailing time zone specificiation. Given a dateTime S and a duration D, this appendix specifies how to compute a dateTime E where E is the end of the time period with start S and duration D i. Such computations are used, for example, to determine whether a dateTime is within a specific time period.

This appendix also addresses the addition of duration s to the datatypes date , gYearMonth , gYear , gDay and gMonth , which can be viewed as a set of dateTime s. In such cases, the addition is made to the first or starting dateTime in the set. The calculation uses the notation S[year] to represent the year field of S, S[month] to represent the month field, and so on.

It also depends on the following functions:. If the day is out of range, it is pinned to be within range. Thus April 31 turns into April This latter addition can cause the year and month to change. Leap seconds are handled by the computation by treating them as overflows. Essentially, a value of 60 seconds in S is treated as if it were a duration of 60 seconds added to S with a zero seconds field.

All calculations thereafter use 60 seconds per minute. Thus the addition of either PT1M or PT60S to any dateTime will always produce the same result. This is a special definition of addition which is designed to match common practice, and -- most importantly -- be stable over time.

A definition that attempted to take leap-seconds into account would need to be constantly updated, and could not predict the results of future implementation's additions.

The decision to introduce a leap second in UTC is the responsibility of the [International Earth Rotation Service IERS ]. They make periodic announcements as to when leap seconds are to be added, but this is not known more than a year in advance.

For more information on leap seconds, see [U. Naval Observatory Time Service Department]. The following is the precise specification. These steps must be followed in the same order. If a field in D is not specified, it is treated as if it were zero. If a field in S is not specified, it is treated in the calculation as if it were the minimum allowed value in that field, however, after the calculation is concluded, the corresponding field in E is removed set to unspecified.

The order of addition of durations to instants is significant. For example, there are cases where:. A · regular expression · R is a sequence of characters that denote a set of strings L R.

When used to constrain a · lexical space · , a regular expression R asserts that only strings in L R are valid literals for values of that type. These characters have special meanings in · regular expression · s, but can be escaped to form · atom · s that denote the sets of strings containing only themselves, i. In · regular expression · s, a normal character is an atom that denotes the singleton set of strings containing only itself.

Note that a · normal character · can be represented either as itself, or with a character reference. The set of strings L R denoted by a character class R contains one single-character string " c " for each character c in C R. A character class is either a · character class escape · or a · character class expression ·. A positive character group identifies the set of characters containing all of the characters in all of the sets identified by its constituent ranges or escapes. For any · positive character group · or · negative character group · G , and any · character class expression · C , G-C is a valid · character class subtraction · , identifying the set of all characters in C G that are not also in C C.

A single XML character is a · character range · that identifies the set of characters containing only itself. All XML characters are valid character ranges, except as follows:. A · character range · · may · also be written in the form s-e , identifying the set that contains all XML characters with UCS code points greater than or equal to the code point of s , but not greater than the code point of e.

The valid character class escapes are the · single character escape · s, the · multi-character escape · s, and the · category escape · s including the · block escape · s. The following table specifies the recognized values of the "General Category" property.

The following table specifies the recognized block names for more information, see the "Blocks. txt" file in [Unicode Database]. The listing below is for the benefit of readers of a printed version of this document: it collects together all the definitions which appear in the document above.

Co-editor Ashok Malhotra's work on this specification from March until February was supported by IBM. Amherst, Massachusetts: Amherst College.

Archived PDF from the original on 10 May David Mariner. Archived from the original on 3 February Let's Queer Things Up! Archived from the original on 22 December Retrieved 24 June LGBTQ Nation. Archived from the original on 25 October Archived from the original on 25 June Retrieved 25 June Gay Star News.

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Nobody Passes: Rejecting the Rules of Gender and Conformity. Emeryville : Seal Press. Bornstein, Kate ; Bergman, S. Bear, eds. Gender Outlaws: The Next Generation Reprint ed. Berkeley: Seal Press. Fine, Cordelia Delusions of Gender: How Our Minds, Society, and Neurosexism Create Difference Reprint ed. New York: W. Fineman, Martha Albertson Nevada Law Journal. Archived from the original on 11 December Retrieved 11 December Hines, Melissa Brain Gender.

Oxford University Press. Nestle, Joan ; Howell, Clare; Wilchins, Riki Anne , eds. GenderQueer: Voices from Beyond the Sexual Binary. Los Angeles: Alyson Books. Peterson, Tim Trace; Tolbert, T. Troubling the Line: Trans and Genderqueer Poetry and Poetics. Callicoon : Nightboat Books. Richards, C. Genderqueer and non-binary genders. London: Palgrave Macmillan. Scout, Ph. The Huffington Post. Retrieved 2 August Stryker, Susan ; Whittle, Stephen , eds. The Transgender Studies Reader.

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A third-person pronoun is a pronoun that refers to an entity other than the speaker or listener. A few languages with gender-specific pronouns, such as English, Afrikaans , Defaka , Khmu , Malayalam , Tamil , and Yazgulyam , lack grammatical gender; in such languages, gender usually adheres to " natural gender ", which is often based on biological gender. In languages with pronominal gender, problems of usage may arise in contexts where a person of unspecified or unknown social gender is being referred to but commonly available pronouns are gender-specific.

Different solutions to this issue have been proposed and used in various languages. Many languages of the world including most Austronesian languages , many East Asian languages , the Quechuan languages , and the Uralic languages [1] do not have gender distinctions in personal pronouns, just as most of them lack any system of grammatical gender. In others, such as many of the Niger—Congo languages , there is a system of grammatical gender or noun classes , but the divisions are not based on sex.

What matters in this case is that the referent belongs to the animate class i. e humans or non-human animals as opposed to an inanimate class. In other languages — including most Indo-European and Afro-Asiatic languages — third-person personal pronouns at least those used to refer to people intrinsically distinguish male from female. This feature commonly co-exists with a full system of grammatical gender, where all nouns are assigned to classes such as masculine, feminine and neuter.

In languages with grammatical gender, even pronouns which are semantically gender-neutral may be required to take a gender for such purposes as grammatical agreement. Thus in French , for example, the first- and second-person personal pronouns may behave as either masculine or feminine depending on the sex of the referent ; and indefinite pronouns such as quelqu'un 'someone' and personne 'no one' are treated conventionally as masculine, even though personne as a noun 'person' is only feminine regardless of the sex of the referent.

See Grammatical gender § Grammatical gender can be realized on pronouns. There are both direct and indirect options for nonbinary referents, although the use of some forms is contested. Example of agreement in a language with grammatical gender [8]. A grammatical gender system can erode as observed in languages such as Odia formerly Oriya , English and Persian. This means that the relation between pronouns and nouns is no longer syntactically motivated in the system at large.

Instead, the choice of anaphoric pronouns is controlled by referential gender or social gender. Example of agreement in English [11]. Issues concerning gender and pronoun usage commonly arise in situations where it is necessary to choose between gender-specific pronouns, even though the sex of the person or persons being referred to is not known, not specified, or for plurals mixed.

In English and many other languages, the masculine form has sometimes served as the default or unmarked form; that is, masculine pronouns have been used in cases where the referent or referents are not known to be all female.

This leads to sentences such as 5a in English, and 6a in French. As early as , dissatisfaction with the convention of the collective masculine led to calls for gender-neutral pronouns, and attempts to invent pronouns for this purpose date back to at least , although the use of singular they as a natural gender-neutral pronoun in English has persisted since the 14th century. The English language has gender-specific personal pronouns in the third- person singular. The masculine pronoun is he with the related forms him , his and himself ; the feminine is she with the related forms her , hers and herself ; the neuter is it with the related forms its and itself.

The third-person plural they and its related forms them , their , themselves are gender-neutral and can also be used to refer singular, personal antecedents, as in 7. Generally speaking, he refers to males, and she refers to females. When a person has adopted a persona of a different gender such as when acting or performing in drag , pronouns with the gender of the persona are used. In gay slang , the gender of pronouns is sometimes reversed gender transposition. He and she are normally used for humans; use of it can be dehumanizing, and, more importantly, implies a lack of gender even if one is present, and is usually, thus, inappropriate.

It is sometimes used to refer to a baby or a child in a generic sense as in response to the question What is it? However, when talking to parents of intersex babies, some doctors are advised to use your baby instead.

He or she are also for a non-human animal who is referred to by a proper name, as in 8 where Fido is understood to be the name of a dog. The other English pronouns the first- and second-person personal pronouns I , we , you , etc. The only distinction made is between personal and non-personal reference someone vs.

something , anyone vs. anything , who vs. what , whoever vs. whatever , etc. She is sometimes used for named ships and countries, but this may be considered old-fashioned and is in decline. In some local dialects and casual speech he and she are used for various objects and named vehicles like a personal car. Animate objects like robots and voice assistants are often assumed to have a gender and sometimes have a name with a matching gender.

See Gender in English § Metaphorical gender. For people who are transgender , style guides and associations of journalists and health professionals advise use of the pronoun preferred or considered appropriate by the person in question. For English, there is no universal agreement on a gender-neutral third-person pronoun which could be used for a person whose gender is unknown or who is a non-binary gender identity ; various alternatives are described in the following sections.

Since at least the 14th century, they including related forms such as them , their , theirs , themselves , and themself has been used with a plural verb form to refer to a singular antecedent.

Prescription against singular they has historically impacted more formal registers of writing. Conversely, to the present day, singular they continues to be attested in both speech and less formal registers of writing in British and American English.

While many speakers recognize the need for gender neutral pronouns, they nevertheless deem referential singular they, as in 13 , ungrammatical or unfit for the job due to the ambiguity it can create in certain contexts.

Generally speaking, there are three kinds of antecedents with which the singular they can be used. In examples , subscript i indicates coreference ; moreover, examples such as 15 and 16 are sometimes referred to as 'referential they'. In the twenty-first century, syntactic research differentiates three groups of English speakers can be identified, based on their judgments about pronoun usage for 14 , 15 and A recent study by Kirby Conrod found these speaker groups to be correlated with age and gender identity.

Relative to gender identity, non-binary and transgender participants rated referential they higher than did cisgender participants. Elsewhere, cisgender speakers with at least one trans or non-binary family member have also been found to rate all three cases as acceptable. Work by Keir Moulton and colleagues, published in , has also found that the presence of a linguistic antecedent — which is the case for examples 14 , 15 , and 16 — significantly improves the acceptability judgments of singular they.

In sentences with a linguistic antecedent, such as 17a , the use of singular they is judged to be equally acceptable whether or not the hearer knows the binary gender of the referent. In sentences where singular they is purely deictic and has no linguistic antecedent, such as 17b , the use of singular they is judged to be less acceptable than the use of a singular gendered pronoun such as he or she when the hearer knows the referent's binary gender.

The authors suggest that the use of a gender-neutral antecedent e. server or reporter may signal the irrelevance of gender in the discourse context, making singular they more acceptable. Additionally, having a linguististic antecedent clarified that the speaker was referring to a singular antecedent, rather than a plural one. In the deictic case, without a linguistic antecedent, these signals were not overt, and the speakers' judgment depended more on their experience with the pronoun itself.

Type of antecedent affects acceptability of singular their subscript i denotes coreference [40]. Another study found an effect of social distance on speaker judgments of singular they use. Forms of the pronoun he were used for both males and females during the Middle English and Modern English periods.

Susanne Wagner observes that: "There was rather an extended period of time in the history of the English language when the choice of a supposedly masculine personal pronoun him said nothing about the gender or sex of the referent. This may be compared to usage of the word man for humans in general although that was the original sense of the word "man" in the Germanic languages , much as the Latin word for "human in general", homo , came to mean "male human"—which was vir , in Latin—in most of the Romance languages.

The use, in formal English, of he , him or his as a gender-neutral pronoun has traditionally been considered grammatically correct. Such examples point to the fact indiscriminate use of generic he leads to non-sensical violations of semantic gender agreement. The use of generic he has increasingly been a source of controversy, as it can be perceived as reflecting a positive bias towards men and a male-centric society, and a negative bias against women.

The use of generic he has also been seen as prejudicial by some, as in the following cases:. Avoidance of the generic he is seen by proponents of non-sexist writing as indicating that the purportedly gender-neutral he is in fact not gender-neutral since it "brings a male image to mind". She has traditionally been used as a generic pronoun when making generalizations about people belonging to a group when most members of that group are assumed to be female: [48]. The 19th and 20th centuries saw an upsurge in consciousness and advocacy of gender equality , and this has led in particular to advocacy for gender-neutral language.

In this context, the usage of generic he has declined in favor of other alternatives. To disambiguate contexts where a referent encompasses both males and females, periphrasis is used. Though cumbersome, this solution is attested with the full range of English pronouns, include the subject pronouns he or she 23 , the object pronouns him or her 24 , the possessive pronoun his or hers 25 , and the reflective pronouns himself or herself However, usage indicates that the masculine pronouns is most often mentioned first.

Some observers, such as the linguist James McCawley, suggest that the use of periphrastic forms may promote stereotypes: " he and she [can foster] the standard sexual stereotypes [in that] if you say he or she , you imply that women aren't included unless they are specifically mentioned, and you make it easier to talk about cases where only one sex is included than where both are. Authors sometimes employ rubrics [ according to whom?

Old English had grammatical gender , and thus commonly used "it" for people , even where they were clearly male or female:. Over time, English gradually developed a system of natural gender gender based on semantic meaning which now holds sway in Modern English.

For more on usage of it , see It pronoun Semantics. In Modern English, pronouns referring to adult humans are typically gendered: feminine she , masculine he. However, in some contexts, children may be referred to with the gender-neutral pronoun it. When not referring specifically to children, it is not generally applied to people, even in cases where their gender is unknown.

The edition of the Quirk et al. grammar observes that whereas he and she are used for entities treated as people including anthropomorphized entities , the pronoun it is normally used for entities not regarded as persons. But the pronoun it can be used of children in some circumstances, for instance when the sex is indefinite or when the writer has no emotional connection to the child, as in a scientific context such as It may even be used when the child's sex is known: In the passage given in 27c , the characters refer to the boy-child at the center of the narrative as a he , but then the narrator refers to it as an it.

In this case, the child has yet to be developed into a character that can communicate with the reader. The Quirk et al. Another gender-neutral pronoun that can be used to refer to people is the impersonal pronoun " one ". This can be used in conjunction with the generic he according to the preference and style of the writer.

In everyday language, generic you is often used instead of one :. Historically, there were two gender-neutral pronouns native to English dialects, ou and h a. In , William H. Marshall records the existence of a dialectal English epicene pronoun, singular "ou": " 'Ou will' expresses either he will, she will, or it will.

Relics of these gender-neutral terms survive in some British dialects of Modern English — for example hoo for 'she', in Yorkshire — and sometimes a pronoun of one gender can be applied to a human or non-human animal of the opposite gender.

Farewell from Protocol. It is the intention of the XML Schema Working Group to allow '' as a lexical representation in the dateTime , date , gYear , and gYearMonth datatypes in a subsequent version of this Recommendation. It is strongly recommended that all API methods accept all possible aliases for keywords and types, but generate the canonical form. This format is used in the Unicode Common Locale Data Repository. They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance.

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