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White Paper

Samuel Hawksby-Robinson edited this page Apr 19, 2019 · 26 revisions

TiiQu : Quantified Trustworthiness

Samuel Hawksby-Robinson -

Xenia Bogomolec -


TiiQu is a blockchain based platform that uses the immutability and verifiable source of data qualities inherent to blockchains to create a digital "passport", which can be relied on as proof of an individual's professional trustworthiness, identity, qualifications, certifications, memberships, previous work experience, performance metrics and education. TiiQu proposes that the methods described in this paper will be refined and widely adopted, such that the TiiQu Passport becomes a globally ubiquitous avatar of an individual's achievements.

To facilitate collaboration between individuals TiiQu will provide the functionality to easily generate blockchain smart contracts to enforce any transactions that individuals agree to, without any programming experience needed. The fundamental purpose of TiiQu is to remove the guesswork and assumption from an individual's claims about themselves, conveniently and continuously providing proof that an individual is trustworthy. By securely collecting multiple and corroborating forms of proof about the individual, TiiQu can be used as a reliable confirmation of an individual's claims about themselves, while simultaneously and significantly reducing the costs traditionally associated with verification.

By reducing the economic barriers of confirming the veracity of an individual's claims TiiQu anticipates the revolutionising of international peer to peer collaboration, via the democratisation of data and by significantly increasing the resources required to successfully mislead others.

Trust Context

TiiQu confines the TQ to trust in a professional context only. Private behaviour should not have an impact on the trust quotient. We will examine the potential trust sources accordingly. Like this we ensure privacy of TiiQu members on this level too.


TiiQu Technologies

The Concept of Trustlessness

Although TiiQu perceives itself as a purveyor of trust, the ultimate aim is to create a state of trustlessness across the TiiQu platform. The term trustlessness or trustless is often misinterpreted as meaning a state in which trust is not possible or a system that can not be trusted, because most recognised dictionaries define it as such. However, the term in the context of blockchain technology is defined as a state in which trust is not needed, or a system that requires no trust when used. In this context trust is understood as an approximate quality of deviation from the certainty of an expected result. Therefore trusting someone a lot is akin to a small deviation from this certainty, where not trusting someone is a large (possibly even a incredibly large) deviation from the certainty.

If a system or state is trustless there is zero deviation from the certainty, meaning the involved parties do not require trust because the state or system guarantees the certainty of an outcome.

Legacy Methods of Establishing Trust

The current method of verifying an individual's claims about themselves is time consuming and costly, creating an environment conducive to exaggerating or giving misleading information when making claims about oneself. To illustrate consider a hypothetical example:

An individual applies for a position within an organisation, and makes a number of claims, the key ones being the following:

Claim Reality Match Status
Personal details Personal details True
Studied at University of Newcastle Studied at University of Newcastle True
Achieved a degree in bio-medicine Achieved a degree in bio-medicine True
Achieved a 1st class degree Achieved a 2:2 degree False
A member of the International Institute of Phlebotomy A member of the International Institute of Phlebotomy True
Part of the team that invented the "needleless blood draw" Knew someone who read about the "needleless blood draw" False

In order to verify the above key claims the organisation would need to contact at least four separate institutions to prove the individual's identity, place of education, study subject, study level attained, membership and research credentials. Getting the verification from each institution will incur costs and take a considerable amount of time. In many cases, although desirable, it simply would not be economically viable to verify all, if any, of the claims.

Because most individuals do not claim outrageously untrue things about themselves [1] some organisations may make cursory verification of an individual's claims and will rarely discover the false statements. However there are individuals that impersonate others, commit degree fraud [2] and make any number of false claims about themselves. In some contexts undiscovered false claims will have no negative consequences, in other contexts they can lead to significant financial loss, physical harm or even the death of another individual [3] [4].

Compounding this problem is another issue of economic barriers preventing those that may most benefit from confirming the veracity of individuals' claims, namely the average person in need of collaboration with or assistance from a specialist. Consider the example of an individual wishing to undergo cosmetic surgery and attempting to choose between specialists all claiming glowing impressive credentials. All the specialists may be telling the truth, however in the case where an individual is unfortunate enough to be duped by a charlatan the potential for personal damage is significant. Without easy access to a means of verifying the claims of purported experts there is always a risk, a risk that TiiQu makes unnecessary.

Approach to the Suitability Question

The Suitability aspect of the TiiQu algorithm addresses the problem of matching experts with the right employers. Matching is a bilateral process and is fundamentally different and more subtle than filtering or searching. Hence, the answer to the suitability question involves other considerations in addition to simple measures of trustworthiness. Some of these considerations include

  • Expert-Job fit - referring to the fit between experts' skills (and preferences) and job supplies.
  • Expert-Organization fit - referring to how well experts will fit within the organization they are expected to perform.

The TiiQu-Match aims to address these questions using state-of-the-art Artificial Intelligence (AI) methods

TiiQu Aims

The primary aims of TiiQu are as follows:

  • Quantify an individual's trustworthiness based on the above proofs (ie. the TQ rating).
  • Create a decentralised collated source of proof for individual's professional claims
  • Facilitate smart contract governed collaboration
  • Reduce economic barriers of entry for verifying professional claims
  • Give individuals direct control over their data
  • Facilitate distributed stakeholdership of the platform via work based token distribution and share purchase rights

Quantifying Trust

In order to recommend an individual based on their professional trustworthiness TiiQu has established a metric of trustworthiness enabling direct comparison between individuals and even organisations.

Long Term Approach

To find fair trust ratings for experts from various areas and organisations we categorise potential member types and trust sources. With those categories a unified scheme for trust quotient computations is built. The scheme will build the base for all future member types and trust sources, already known or not. Therefore it has to be abstract enough to catch every future occuring case. All imaginable scenarios will be analysed by a trust quotient test system.

Future scenarios challenging the existing scheme will have to be integrated in a way that does not put existing trust quotients into question. For a good comparison between known and new scenarios' trust quotients, our TQ tests will be continuously documented.

Mathematical methods are chosen according evaluated test results with regard to fairness as well as a good digital performance within all other processes.

The TQ

The Trust Quotient (TQ) is the algorithmically derived value representing an individual's objective trustworthiness relative to their peers and based on proofs provided by the individual. The TQ quantifies trust based on proofs relating to an individual's identity, reputation, veracity and performance. By establishing multiple sources for each of the above criteria the TQ is able to robustly quantify the base components of trust.

Source weighting allows for a TQ rating to favour sources over others and applies to all criteria with the exception of veracity. With respect to veracity, all proven claims are equal and proving a claim impacts on an individual's TQ rating, but the nature of the claim doesn't impact on an individual's TQ rating.

Trustworthiness is handled separately from suitability questions. Only verification of expertise can have an impact on the TQ. But it this context, only the fact of the proven claim counts and not the quality of the claim itself. Furthermore, an expert's TQ will neither depend on the number of achieved certifications or experienced workfields nor on the range of his expertise.


The TiiQu algorithm will evolve over time and with the gathered experience from integrating new kinds of experts. Because of this, the fairness in our system will base on dynamically computed trust quotients from present values, ranges and weights. Every time we adapt the algorithm, each TiiQu member will have the same conditions.

Varying Rating Systems of Trust Sources

Each trust source determines its own rating system, which is independent from TiiQu. Experts from similar work fields might share trust sources, but experts from various work fields might also be members of various trust sources with differing rating systems. So the preconditions might be similar for experts from a specific area but it could be difficult to compare trust quotients of experts from various professional backgrounds. TiiQu is aiming to get close to fairness in this regard too. But we cannot guarantee to achieve a 100% satisfactory solution for everyone.


Building TQ Trees

A TQ tree is a tree of nodes which deliver values for the trust quotient. The first level consists of 4 nodes representing the following criteria

  1. Identity
  2. Verification (Veracity)
  3. Reputation
  4. Performance

These nodes have child nodes representing trust sources which can be added dynamically. Weights will be assigned to the trust sources according to their categories. Child nodes can be dynamically added to a criteria node. More verified values from trust sources will have a positive impact on the TQ rating.

Identity Node

Values of the identity child nodes will either be 0 for not verified or 1 for a verified identity. Possible trust sources are

  • credit agency (high weight due to identity verification by showing passport)
  • university (medium weight according to varying immatriculation processes)
  • online community (low weight because of identity verifaction by email/phone number only)
  • ...
Verification Node

Verification child nodes represent proven claims. A verified claim generates a child node with points in the verification node. Possible child nodes are thereby all kinds of child nodes which are present in the other three nodes.

No weights are applied to verification child nodes. All proven claims are equal. This node also is where we handle lies, a discovered untrue claim is punished by a very large penalty.

Reputation Node

This node stands for opinions of other people or communities about the TiiQu member. Reputation child nodes can have values of a wide range. Some trust sources even allow limitless reputation ratings. Possible trust sources are

  • reputable online communities
  • reputable private societies
  • public reviews from Google, yelp or similar instances
  • publications
  • ...

TiiQu will have a list of accepted online communities. Online communities of organizations which are not on this list can contact TiiQu for an evaluation of suitability for the TQ reputation node. The weights of the trust sources will balance the differences between delivered reputation values.

Performance Node

Stands for the performance quality of the TiiQu member in completing a mission. This is the node that is closest to the suitability consideration of an expert. But is still represents a trust aspect. It is an answer to the question of self-validation and honesty.

Basically there are a number of things that can be tracked by TiiQu about a mission:

  • the time it took to complete
  • reached time milestones
  • how close the mission was to the proposal

The trust sources of this node and the way the performance node operates will be defined by work in progress. We may need to monitor this one more closely because although matching performance against proposal is interesting it may unfairly penalise people if done incorrectly.

A possible mitigation of unfair outcomes could be to define a set of questions which concern both employer and expert and give them the possibility to agree upon the given answers before delivering values to the TQ performance node. For example:

  1. Question for expert: How close was the proposal to the actual work that had to be done to complete the mission?
  2. Question for employer: How close was the completed mission to the proposal?
  3. Question for expert: Where there unexpected events that slowed down the originally planned process? If yes, how many?
  4. Question for employer: Did the expert communicate unexpected events such that the planning of completing the mission could be adjusted?

One of the goals of the TiiQu platform is to stimulate a good cooperation of employers and independent experts. Companies will be rated as well. The quality of a completed mission depends on a clearly and realisticly defined proposal as well as on the mode of operation and the skills of the expert. A good cooperation of employer and independent worker in coping unexpected challenges is crucial to a satisfying result for both sides.

Trustworthiness Vs Suitability

TiiQu makes a distinction between attributes that impart trustworthiness and attributes that impart suitability. For example consider an individual with a degree in mathematics, a verification of holding a degree proves the trustworthy attributes of identity and veracity. The same verification also proves their suitability for mathematics related collaboration, but as already stated doesn't increase the individual's TQ quotient more than if they had verified a membership with say a carpentry guild. A verified membership with a carpentry guild improves an individual's suitability for carpentry related collaboration, and increases the TQ quotient in the same way as a verified degree in mathematics would. Note, the degree to how suitable an individual is for collaboration is subjectively determined by the individual seeking collaboration.

One important factor to reiterate is that many sources such as confirmation of a degree or PhD would affect multiple criteria, ie confirming a degree would give evidence of someone's identity and would also give evidence of the same individual's ability to make and prove claims about themselves (veracity). Note, however, possessing and confirming a degree does not confer any more trustworthiness than another individual that does not possess a degree but is able to supply and prove other claims about themselves. The nature of a claim only impacts on the subjective quality of suitability, determined by matching search criteria, human judgement and expectation.

For a more comprehensive breakdown of the Trust Quotient see the dedicated The Trust Quotient (TQ) and The Trust Quotient Analysis.

Blockchain Based Claim Verification

A cornerstone of the TiiQu platform relies on two important qualities of the blockchain, immutability and verifiable source of data. Immutability, you can't change* the data once published. Verifiable source of data, we can know who published the data. These two qualities make a verification system significantly more robust than any precedents.

Because one can reliably confirm who is publishing data and can be certain that the data has not been tampered with once published, one can take it as true that a certain entity made a certain claim. For example, in the context of verifying an individual's membership of a prestigious organisation, the organisation can publish to the blockchain a series of statements detailing who has membership with them and what the public Ethereum address is for each. This means that if an individual made a claim that they were a member of the prestigious organisation they could prove it by proving they own the Ethereum address, via a simple cryptographic signature.

This principle is the foundation for TiiQu's approach to verifying individual's claims about themselves. TiiQu establishes a relationship with an institution, the institution publishes proofs to the blockchain of issues (certifications or memberships) they've made and TiiQu matches the proofs to individuals with valid claims.

For a more comprehensive breakdown of TiiQu's approach to verifying claims see the dedicated Verification Paper.

* At least without a full transaction history of any data appendments including the initial value.

Smart Contract Enforced Collaboration

TiiQu aims to be an enabler of global and trustless collaboration. In order to guarantee the trustless quality of collaboration, TiiQu provides functionality that allows non-technical users to author and publish smart contracts to the Ethereum blockchain, with as much ease as ordering a takeaway meal online.

There is a significant amount of work involved with producing a system that allows users to easily create smart contracts of an undefined scope, and so we intend to release the functionality in stages. The current plan for facilitating accessible smart contract authoring is as follows:

  • Simple conditional payments
  • Coarse dispute resolution
  • Complex conditional payments
  • Advanced conditional transactions
  • Dispute resolution
  • Legally enforceable contracts

Simple conditional payments

Basic contracts that function similarly to multi-party escrow and multi-signature transaction authorisation. Optionally milestone payments are added to the contract, allowing for multiple payments to be made via the same contract. Parties may choose the currency to transact in, allowing payments to be made via tokenised fiat or other network tokens. Described simply, funds are transferred to the contract by the payer, work is performed, both parties agree for all or part of the funds to be transferred to the payee. Transfers of funds are authorised manually via party confirmation.

Coarse dispute resolution

A flagging mechanism that notifies a TiiQu resolver to assess the details of a contract that's conditions are in dispute and attempt to arbitrate between the parties. Dispute flagging, contention logging and arbiter decisions are handled on chain, but arbitration will be predominantly be handled outside of the blockchain.

Complex conditional payments

Conditional payments that can be authorised and performed automatically dependent on measurable criteria being met. Definitions of a full range of measurable criteria is to be established. An example of measurable criteria is the amount of time spent performing an activity, or number of confirmed sales secured by an individual.

A full range of methods for proving measurable criteria is to be established, with all methods being as blockchain-centric as possible. An example of these methods would be querying a smart contract dedicate to recording sales.

Advanced conditional transactions

Contracts that allow for trading of more abstract things, such as, information and assets not representable by network tokens, dependent on potentially elaborate and esoteric conditions.

Dispute resolution

Advanced arbitration of contract disputes including the following functionality; arbiter nomination, arbiter dismissal, arbiter decision appeals, evidence submission, arbiter scope restrictions

Legally enforceable contracts

An admittedly aspirational goal requiring the input of many experts, rather than a purely technical problem requiring primarily the talents of a developer. The intention is that we can establish some legal precedent for making smart contracts, perhaps initially of a limited scope, enforceable by the power of a state's legal system. Subsequently the real challenge will be to establish the same precedent across multiple jurisdictions [5].

Crowd-sourced Arbitrage

The concept of crowd-sourced arbitrage or crowd-arbitrage allows parties of a contract to select a crowd sourced arbiter entity to mediate between parties in the event of a dispute and ultimately give the final word in the event the parties are unable to agree on the dispute.

The TiiQu platform will give functionality that allows users to register as an arbiter and allow users authoring smart contracts to assign the TiiQu crowd-arbitrage. Note that when using the TiiQu crowd-arbitrage service the parties to the contract have no ability to select individual members to be part of their crowd-arbiter, as these are chosen at random from the platform's pool of arbiter members.

In the event of a dispute the arbiters discuss and vote on the merits and details of the dispute. Initially crowd-arbiters facilitate discussion between contract parties but, in the event the parties cannot come to an agreement, ultimately decided the outcome of the dispute.

Members with arbiter status will receive payment for their arbitrage services and will also optionally receive arbitrage experience proofs on their TiiQu passport.

Humanistic Future of Work

TiiQu is establishing ethical guidelines with a board of advisors from various scientific and society related domains such as humanistic management, social science, law and data science for a sensible integration of powerful technologies like Blockchain and AI. It is our goal to practically realize those guidelines by implementing rules for development, management, business relations as well as a code of conduct for the TiiQu members.


[1] : Detecting lies and deceit: the psychology of lying and implications for professional practice. / Vrij, Aldert. Chichester : Wiley, 2000. 254 p. (Wiley series in psychology of crime, policing and law). Research output: Book/Report › Book

[2] : Advice and Guidance on Degree Fraud for HE Providers. HEDD, 2017 source

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