I’m thrilled to announce that our book “Trust in Artificial Intelligence — A Multi-Perspective Analysis” will be published in a revised English edition by Springer Nature. I want to express my gratitude to the wonderful editor and esteemed colleague, Prof. Dr. Sabrina Schork, for the opportunity to contribute to this fascinating work. Trust in AI is a crucial yet multifaceted and complex topic. This book represents an initial step toward comprehensively exploring the field of ‘Trust in AI’ from diverse perspectives. My contribution supports this goal by deriving concrete design principles from a systems theory perspective.

The AI Trust Paradox: Why We Need a Systems Approach

Picture this: 39% of users describe AI data analysis as disruptive, while nearly as many find it helpful or comforting. Yet only 19% trust AI with their data without hesitation. This contradiction, the “privacy paradox”, sits at the heart of AI’s most significant challenge today.

My latest research, published in our upcoming Springer Nature book, reveals why trust isn’t just a nice-to-have in AI development — it’s the engine that powers the entire AI value creation cycle. Without it, even the most sophisticated algorithms fail to reach their potential.

The Trust-Data Feedback Loop: A Critical System Design Challenge

AI systems rely on a self-reinforcing cycle: high-quality data feeds better algorithms, which in turn create more compelling services, attracting more users who provide additional data. But here’s the catch — this entire cycle depends on user trust as its foundational enabler.

My systems theory analysis identifies five interconnected trust relationships that make or break this cycle:

• Trust between people and organizations
• Trust in AI service providers
• Trust in AI components and agents
• Trust in AI interfaces
• Trust shaped by individual personality factors

Four Emerging Complexity Drivers Threatening AI Adoption

Through an extensive literature review, I’ve identified four critical factors that are making trust in AI increasingly complex:

1. New Vulnerabilities: AI poses unprecedented risks to privacy, bias, and the loss of human agency, which users struggle to comprehend and assess.
2. Cognitive Biases: Users often rely on mental shortcuts when evaluating AI trustworthiness, which can lead to either dangerous over-trust or paralyzing under-trust.
3. Missing Digital Identity Infrastructure: The internet was built without a native way to verify who is real online, making AI-generated deception increasingly sophisticated and widespread.
4. Context-Sensitive Data Complexity: The value and sensitivity of data depend heavily on context, making it nearly impossible for users to make informed sharing decisions.

Five Design Principles for Trustworthy AI Systems

Based on this systems analysis, I’ve developed five actionable design principles that AI developers can implement immediately:

P1: Adaptive Context Awareness — AI systems should automatically consider the complexity of content and context-bound data use, providing binding rules that are transparent and understandable to users wherever intuitive understanding falls short.

P2: Human-Centered Anthropomorphism — By introducing proportionate human-like characteristics and adapting to user personality profiles, AI systems can enhance trust, resilience, and user engagement without creating false expectations.

P3: Transparent Reciprocity — User groups with limited digital experience benefit significantly from clear risk/return profiles, making transparent value exchange crucial for building trust, especially among less tech-savvy demographics.

P4: Cooperative Norm Consensus — Trust within human-machine collectives thrives when there’s perceived agreement on cooperative norms, supported by binding control architectures and voluntary observance of broader social boundaries.

P5: Native Digital Identity — As AI systems evolve, digital trust increasingly depends on solving the internet’s fundamental identity crisis. Without credible proof-of-personhood, even sophisticated trust-enhancing tools risk failure as the line between human and machine-generated input disappears.

The Path Forward: Building AI That Humans Can Trust

The research makes one thing crystal clear: trustworthy AI isn’t about making systems more transparent or adding more security features. It’s about understanding trust as a complex, interconnected system that spans technology, psychology, and social dynamics.

The stakes couldn’t be higher. As we face the potential of a “data winter,” where legal uncertainty and privacy concerns restrict access to the high-quality datasets AI needs to improve, getting trust right becomes existential for AI’s continued advancement.

The good news? These design principles provide a roadmap. They’re not theoretical concepts but practical guidelines that development teams can implement today to build AI systems that users will actually want to engage with.

Ready to dive deeper? Learn everything you need to know to understand and engender digital trust here: https://iceberg.digital/.

You can purchase the currently available German version of our book here, or contact me directly via private message for a copy of the pre-published English version. Let’s continue this critical conversation about building AI systems that truly serve humanity.

AI Disclaimer: This article was created with assistance from Claude (Anthropic) for content development and Recraft.ai for illustration. All content has been reviewed and approved by the author prior to publication.

Over the past days, a highly motivated community of digital experts gathered in sweltering Geneva, Switzerland, to discuss requirements and forge frameworks for future global digital collaboration (GC 2025).

Among the most compelling solutions emerging from these discussions is the First Person Project, which offers a groundbreaking approach to solving the “proof-of-personhood” challenge in our increasingly AI-dominated digital landscape.

In the context of first-person identity, the term “first-person” refers to identity as experienced, owned, and controlled by the individual — the “I” in “I am.” This shift is foundational for building trusted interactions in decentralized, AI-driven ecosystems where control, verifiability, and privacy must originate with the person themselves. Unlike centralized biometric systems such as World (formerly Worldcoin), which rely on iris scanning and global databases, First Person Credentials are built on Decentralized Identifiers (DIDs) and Verifiable Relationship Credentials (VRCs) — providing cryptographic proof of genuine personal trust relationships.

The Escalating Crisis of Digital Trust

As artificial intelligence becomes increasingly indistinguishable from human behavior online, the ability to distinguish between real people and bots presents critical threats to digital democracy and authentic human discourse. Recent advances in large language models, deepfake technology, and autonomous AI agents have created an environment where traditional verification methods are rapidly becoming obsolete. The sophistication of AI-generated content now extends beyond simple text to include convincing audio, video, and even real-time interactive behavior that can fool even experienced users.

Identity serves as the foundation of trust: we can only establish meaningful relationships when we know who we’re engaging with. The digital realm, much like the physical world, cannot function as a lawless space. Just as we require identification in real-world contexts — from voting to financial transactions to professional credentialing — we need equivalent levels of assurance in digital environments. The stakes are particularly high when considering applications such as democratic participation, medical consultations, financial advice, and educational interactions.

This challenge is compounded by the scale at which AI can operate. While human actors are limited by time, energy, and resources, AI systems can potentially create thousands of convincing personas simultaneously, engage in coordinated influence campaigns, and manipulate public opinion at unprecedented scales. The economic incentives for such manipulation are significant, creating a pressing need for robust verification systems.

The Economics of Verified Identity

Stewart Brand’s prescient observation that “information wants to be free, but it also wants to be expensive” perfectly captures the fundamental tension at the heart of digital identity systems. Verified identity data possesses inherent value precisely because of its scarcity and authenticity in an environment increasingly saturated with synthetic content and automated interactions.

Having the right information at the right moment can be life-changing — understanding whether you’re communicating with a genuine person or an AI agent can determine how you evaluate medical advice, political perspectives, or participate in democratic processes. This information asymmetry creates what economists might term a “trust premium,” where verified human interaction becomes increasingly valuable as synthetic alternatives proliferate.

The economic implications extend beyond individual interactions to systemic market dynamics. Organizations conducting market research, governments seeking public input, and platforms trying to maintain community integrity all face the challenge of distinguishing authentic human participation from manufactured consensus. This has led to the emergence of what could be called “authentication markets,” where the ability to verify genuine human participation commands premium value.

Technical Architecture and Implementation

First Person Credentials operate through a sophisticated system of personal attestations, where individuals vouch for their relationships with others, creating a decentralized network of trust. This approach leverages the natural human tendency to form verifiable social connections while utilizing advanced cryptographic techniques to ensure privacy and security.

The system builds upon two key technological foundations: Verifiable Relationship Credentials (VRCs) create cryptographic proofs of authentic human connections. At the same time, Decentralized Identifiers (DIDs) provide the technical infrastructure for self-sovereign identity management. Together, these technologies enable individuals to prove their humanity and establish trust without relying on centralized authorities or surrendering personal data.

However, critics point out that “a web of trust can feel like a pile of disconnected threads” — establishing coherent and verifiable relationships across different contexts remains a significant technical challenge. The system must address questions of scalability, interoperability, and governance while maintaining the privacy and autonomy that initially make it attractive.

Any effective proof-of-personhood solution must satisfy several essential criteria: it must be tangible (grounded in real-world interactions that are difficult to fabricate), usable (with an intuitive interface design that doesn’t burden users), useful (addressing genuine problems that people actually face), portable (functioning across various platforms and services), contextual (providing appropriate verification levels for different use cases), and actionable (enabling concrete decisions and protective measures).

Global Policy Implications and Regulatory Landscape

The development of proof-of-personhood systems is occurring within a complex global regulatory environment where different jurisdictions are taking varying approaches to digital identity. Switzerland’s e-ID initiative exemplifies this complexity — after voters rejected a 2021 proposal involving private companies over privacy concerns, the government pivoted to a self-sovereign identity (SSI) model called SWIYU. This new approach emphasizes decentralized data storage, user control, and privacy-by-design principles; however, it still faces challenges, including a Referendum vote scheduled for September 2025, which highlights ongoing tensions between digital innovation and privacy protection.

The European Union’s Digital Identity Wallet framework takes a different approach, emphasizing user control and interoperability while maintaining regulatory oversight. Meanwhile, emerging markets are experimenting with entirely new models that may leapfrog traditional identity systems altogether.

The First Person Project’s decentralized approach presents both opportunities and challenges within this regulatory landscape. Its emphasis on individual control and privacy aligns with growing concerns about surveillance capitalism and data exploitation. However, regulators may struggle with oversight and compliance in systems designed to operate without central authorities.

These policy implications extend to international cooperation and digital sovereignty. As digital identity systems become critical infrastructure for online participation, questions arise about interoperability across borders, mutual recognition of credentials, and the potential for fragmentation.

Comparative Analysis with Existing Solutions

While World’s biometric approach has drawn criticism regarding surveillance risks and loss of digital anonymity, First Person Credentials preserve privacy through relationship-based verification, aligning with Vitalik Buterin’s advocacy for “pluralistic identity” systems. Buterin’s critique of centralized biometric systems highlights fundamental concerns about power concentration, erosion of privacy, and the potential for authoritarian surveillance.

The fundamental distinction lies in data control architecture: centralized systems concentrate valuable identity information within single entities, creating attractive targets for surveillance, data breaches, and cyberattacks. These “honeypot” architectures also create single points of failure that can compromise millions of users simultaneously.

Decentralized trust networks distribute this value across communities, maintaining both security and democratic oversight while preserving the essential economic value of verified human participation. This distributed approach makes the system more resilient to attack, more resistant to censorship, and more aligned with democratic principles of distributed power.

Other alternatives, such as government-issued digital IDs, knowledge-based authentication, and hardware security keys, each offer different trade-offs between security, privacy, usability, and scalability. The First Person Project’s relationship-based approach represents a unique position in this landscape, potentially offering the security benefits of social verification with the privacy advantages of cryptographic protection.

Future Implications and Next Steps

The success of proof-of-personhood systems, such as the First Person Project, will ultimately depend on achieving widespread adoption while maintaining the trust and security properties that make them valuable. This requires addressing not only technical challenges but also user experience, economic incentives, and governance structures.

As AI capabilities continue to advance, the arms race between synthetic content generation and detection will likely intensify. Proof-of-personhood systems may become essential infrastructure for maintaining authentic human discourse in digital spaces. The question is not whether such systems will emerge, but which approaches will prove most effective, trustworthy, and aligned with human values.

The ongoing discussions in Geneva represent just the beginning of a broader transformation in how we think about identity, trust, and authenticity in digital spaces.

The First Person Project (website placeholder) offers one compelling vision for this future — one where individuals retain control over their identity while contributing to a more trustworthy and democratic digital ecosystem.

We are all connected. Ever since the American social psychologist Stanley Milgram proved with his small-world experiments in the late Sixties that every human being is inevitably linked to each other by surprisingly short chains of connections, we know about the importance of social networks. We also know that networks not only play a significant role in social interaction, but rather all over our physical and mental world. They allow a virus or gossips to spread. They can be used to display protein-protein interaction networks as mathematical representations of the physical contacts between proteins in the cell. It is in the nature of things to position itself or allocate resources in networks. The typical network characteristics and effects can also be made accountable for the emergence and spread of the internet. Being connected is one of the most important qualities in today’s world. In our understanding of digital business, network effects are key to success. But there is a second side to this coin. Network effects can also encourage unfortunate developments. Alike the unstoppable spreading a bad virus or cancer, networks such as the internet can also evolve along wrong paths.

Today, the leading revenue models of digital businesses are based on advertising. It is evident that the economic engine of the world wide web is privatized and monopolized to a great extent. Monopolistic companies have emerged as giants in their space. Facebook, Google, Tencent, Airbnb and numerous other leaders of the pack have created own, proprietary platforms in the form of digital ecosystems in their markets. They collect and analyze the wealth of data their users produce when accessing their websites, communicating on their platforms and in many more occasions. All these data points together create the user’s digital identity.

Most of the success stories in digital business have one thing in common; they are all enabled by data. The mechanism behind this phenomenon can be explained in only a few sentences. Constantly improving algorithms lead to digital products and services. When done right, these data products add value to the customer. This perceived value added will convince customers to provide even more data. The use of data products itself leads in this perfectly closed loop to an ever-increasing wealth of transactional and behavioral data. All that data can be monetarized which causes profits to surge. Unfortunately, there is a worrying imbalance between giving and taking. Customers more and more understand this issue and demand for more reciprocity. They recognize the value of their personal data. Empowered with new technological means and knowledge, they start to better protect this data. An even bigger effect on existing data-driven business models is to be expected by interventions from regulators. The latter are also increasingly concerned at what they see as a growing imbalance between data-dependent companies and individuals (Nguyen et al., 2013). This is why the European Commission will finally introduce its “regulation on the protection of individuals with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation)” in May 2018 (European Commission, 2012).

All this leads to the hypothesis that the area of living in a land of milk and honey is coming to an end for the large players in the digital economy. The newly emerging web is characterized by fundamentally different mechanisms, it’s about multiple nodes sharing value across an open network. Companies like Facebook are already anticipating this change. They are restlessly on the search for other revenue models apart from data monetization through advertising. But this is another story. Before painting a picture of the future and outlining solutions, we need to ask ourselves; How did it come to this?

Absence of an identity layer

The answer to this question can be found in a simple fact: “The Internet was created without an identity layer”.

This increasingly popular quote from Kim Cameron, Chief Architect of Identity for Microsoft, leads to the root cause that can make network effects a counterproductive force. Although the triumphant progress of the internet brought the information age to a new level, it led digital business models towards the described impasse. Whereas the Hypertext Transfer Protocol (HTTP) as the underlying protocol used by the World Wide Web lead to unpanelled, successful growth of the internet, the web community missed to establish an adequate system to assign and verify identity. The way the internet works is fundamentally different than identity on the web works. The latter is a network of connected devise. Each of these devices connected to a network are centrally assigned a numerical label, the Internet Protocol address. Instead of identifying human beings as endpoints on the network, the system connects physical devices. That’s why it is nearly impossible today to uniquely identify people and authenticate their messages. In addition, we currently define and authenticate accounts that are not necessarily tied to real people or organizations. We do this often separately for each service. For the sake of convenience, we even allow the giants like Facebook or Google to authenticate our identities on third party platforms. This all leads to the unfavorable side effect that online identities can dramatically differ from real world identities and that information spread in digital communication might not be true. To better describe this phenomenon, politics has established all new expressions such as “alternative facts” or “fake news”.

A certain degree of anonymity in digital communication is not harmful by nature. Anonymity may lower barriers to engage in discussions and therefore to participate in a network. It can support the rapid growth of networks and with that it’s positive externalities. This effect describes the fact that an additional node in a network has a positive effect on the value of this network to others. Anonymity is also what drives the success of many use cases on the blockchain. The Bitcoin is one of them. Due to inherent anonymity, the most prominent among the broad range of new cryptocurrencies is also used for shady transactions. Without anonymity, the rapid adoption of the Bitcoin would not have happened and its value would be much lower.

Another aspect that sheds a bad light on anonymity is the popular assumption that online anonymity is one of the principle factors that promotes aggression. This must not necessarily be the case. Anonymity can produce the “stranger on a train” phenomenon, wherein people share intimate self-disclosures with strangers as they do not expect a reunion and hence do not fear any risks and constraints (Bargh et al., 2002). Recent studies in social norm theory show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals (Rost et al., 2016). When introducing an identity layer for the web the major focus should therefore not lay on making anonymity a thing of the past. It should rather lay on enabling and supporting authenticity and eventually data veracity. In this context authentication can be defined as the act of confirming the truth of an attribute. If we were able to more easily and reliably authenticate data on the web, the degree to which data we use to make decisions is accurate, precise and trusted will be much higher.

What’s the matter with Personal Data

Another side effect of the missing identity layer on the world wide web is the fact that personal data is easily accessible for service provider and that this data can be monetized without a clear consent and without remuneration of the owner. Dealing with personal data is complicated and gets more and more toxic for companies of all industries. Here is why:

• The amount of data is growing at an astonishing rate. Users leave traces with every activity and generate countless data points along the customer journey
• Although cost of data storage is shrinking, the cost to acquire, manage, analyse and protect huge volumes of user data is increasing
• Digital identities bear the risk of correlation. If a user is to use one identifier in multiple places, those places might collude to correlate that identifier and amass significant data about the individual without its consent.
• Central data stores are honeypots for hackers. With new data regulations coming into force this year, storing personal data can become illegal. Together with the high risk and impact of data breaches, capturing data becomes toxic.
• Generally, the responsibility and complexity of the management of personal data cannot be outsourced to the user. They prefer easy to remember passwords compared to excessively safe passwords. Usability of authentication systems remains key.
• Low data quality and veracity can lead to wrong decision and damaged trust

There is a common understanding of the strategic thrust mandatory to further develop digital business models: “A new approach to personal data is needed that is flexible and adaptive to encourage innovation, but also protects the rights of individuals. Notice and consent need to be reconsidered to be equipped for this changing world.” (WEF, 2013). Extracting insight from consumer data requires respectful and farsighted handling of personal data. A first step on this approach is to establish a new paradigm to manage digital identities. Alike the handling of personal data, the control about identity needs to be brought back to the individual. Individual identity shall have administrative autonomy regardless of its location in digital space.

The path towards the web of trust

The missing identity layer of the internet is a well known issue. That’s why there have been countless attempts to close this gap. The task has been left to applications and services. While these apps do their job quite well for a clearly defined area, they can’t hardly be applied across silos. Furthermore, they all rely on a central authority. These are all facts that make current identity systems imperfect and also vulnerable to abuse.

The first step towards a better solution is to establish a solid, mental layer that takes up the challenges described. Such a layer requires a common understanding of the problem, a common language (ontology) and a clear commitment of participants to support this idea and to obey to certain rules of the game (codex).

Many interesting attempts in this direction have been made in the last years. Among them are trust networks such as the Secure Access For Everyone (Safe) network (https://safenetwork.org) or the respect network. The latter globally launched its platform in 2014 with a line-up of around 50 founding partners, including Neustar, Swisscom, and NEC. In a nutshell, these trust frameworks provide a set of guidelines, rules and tools together with an assessment and enforcement infrastructure that operationalizes them. In addition, trust networks usually rely on decentralized concepts for data storage. The individual itself shall own its data using Personal Data Services or Personal Data Stores (PDS). These are services that let the individual store, manage and deploy their key personal data in a highly secure and structured way.

However, despite all efforts and well-intentioned ideas, it is a cumbersome endeavor to establish such a new type of contract that may legally bind the members of the trust community to the policies. It is therefore, not a big surprise, that many attempts have failed so far to get traction and eventually to establish a well anticipated industry standard.

Why the timing is right now

Great ideas often fail because they are ahead of their time. But the wind is about to change. Two forces can set the timing to stablish a reliable identity layer just right: The implementation of the General Data Protection Regulation (GDPR) in Europe and the increasing importance and anticipation of the blockchain technology.

With the upcoming introduction of new data regulation standards in Europe the discussion about the necessity of a resilient identity layer for the web and the demanded empowerment of individuals gains momentum.

The regulation demands that the control about personal data is given back to the individual. This implies that the identity should again belong to the individual. It must never be possible for a centralized authority to alter an identity or to take it away. Such a self-sovereign identity can only exist in a decentralized system.

A stringent requirement to establish self-sovereign identity is a web of trust with its decentralized trust model — a valid alternative to the centralized trust model of a public key infrastructure, which relies exclusively on a certificate authority.

The impressive global popularity of cryptocurrencies brings a much better understanding of the principles of decentralized systems. The Blockchain technology could be the missing link for a successful implementation of a decentralized trust network. Countless projects demonstrate that the Blockchain technology is tremendously powerful in overcoming the trust barrier. It’s trust-less systems might be the answer. The Blockchain with its distributed ledger is the ideal backbone of a resilient web of trust. It reliably connects the described prerequisites such as policies trough smart contacts and with personal data stores in the form of decentralized applications (dApps). Now that the timing seems to be right, it’s no surprise that a high number of projects enter the game. They have learned from the previous failures and often anticipate the culture of open source and open data. They know that they can only succeed, if their solution is open if they seamlessly integrate into the bigger picture that draws the self-sovereign identity.

The question arises if the Blockchain technology is the ideal vehicle to handle personal data? Given its decentralized mechanisms and with that its robustness against manipulation, it is an adequate solution for an identity layer. But there are also drawbacks of the technology that need to be considered:

• The distributed ledger is forged by consensus. Therefore, it misses by design a strong governance. In order to improve the codebase or just fix an issue, the community around the Blockchain may decide to change the protocol. Such a hard fork would dramatically impair identity schemes.
• Personal data could be stored on the ledger. This would result quickly in a breach of the new data protection regulations.
• Transferring information across Blockchains can be difficult. Portability and interoperability may be impaired.
• The fact that a unique identifier would have to be defined and stored on the ledger would again trigger an immediate correlation risk
• Identity information on the Blockchain cannot easily be revoked. This is a critical requirement in order to manage claims and entitlements.

Following this argumentation, it stands to reason that the complexity of an identity layer can only be solved by drawing on multiple concepts and technologies. While a robust trust framework in terms of binding commitments to rules of the game remains important, the mechanisms of the blockchain can be leveraged to access personal data stores and handle value transfers in particular. There will not be a single, centrally owned solution or architecture, but rather a consortium of different, autonomous solution providers with their interoperable components.

How a Swiss project may lead the way

Companies like to leverage Switzerland as a brand that stands for quality and security. Conversely, authorities and local businesses are strongly motivated to life up to high standards. It is therefore not a surprise that, Switzerland plays a leading role in shaping new data protection regulations and developing new standards in digital identity. The Swiss government is keen to provide an electronical identity (E-ID) in the near future. How a final solution will look like remains open. It will however rely on private innovators as identity solutions provides. Over the last year, a few forward-looking projects have been implemented. Among them is an Ethereum based digital identity leveraging the Consensuses web-based wallet and identity management system called uPort. The solution has been implemented in Swiss City of Zug. It ties personal information to an Ethereum address and allows citizen to establish a self-sovereign identity, collect badges and credentials, login to decentralized apps, and digitally sign transactions. Another groundbreaking identity solution has been rolled-out for Canton of Schaffhausen by e-government as a service provider Procivis. The creators of this eID+ solution are aware of the fact, that such a project can only succeed if it is built based on the principle of an open architecture. The pilot project is only a first step towards an integrated e-Government solution. The system is inherently open to adjustments and to integrate seamlessly additional identity solutions such as the planned SwissID.

The creators of the self-sovereign eID are about to go one step further. By end of February 2018 they are going to launch their blockchain based VALID ecosystem with an initial token offering. The team that is based in Zurich intends to expand the digital identity sphere beyond personal attributes by encompassing the individual user’s entire personal data space. To achieve this, they will provide two key components.

1. The VALID wallet for personal data management. The wallet is the central user interface to access the VALID ecosystem. It features a personal data store that stores most sensitive data locally on the device. The encrypted data is therefore not stored on the ledger itself and the user is in full control of all his data at any time.
2. The VALID marketplace for data monetization. Users can decide to share their data by granting interested parties access to clearly defined data points. As a reward, users will be remunerated in VALID tokens (ERC20) based on the desirability of the shared data.

Obviously, the idea to establish a marketplace for personal data that is provided from personal data stores with the informed consent of the owner is not new. Many attempts in this direction have been made and all of the ambitious project have failed so far to get traction. VALID however, seems not only to have the right blueprint at hand. Also, the timing to roll-out the solution seems to be just right. With the new European privacy regulation coming into force in May 2018, users will pay much more attention to personal data management. Companies that used to tab into their user’s data often without clear consent and any intention for remuneration are now forced to ensure transparency and data portability. The latter will allow users to conveniently collect personal data and store is in their vaults.

Another success factor will remain the openness of the solution architecture. VALID is aware of the fact, that it will coexist with numerous other solutions for personal data stores and marketplaces. The seamless integration with other platforms will be critical for the acceptance as well as for the usability of this product. An important step in this direction is already made. Procivis has established a partnership with the Lucerne University of Applied Sciences and Arts and the Crypto Valley Association with the intention to implement the Sovrin Blockchain ID protocol. The Sovrin Foundation incorporated the principles of the respect network and governs the world’s first open public self-sovereign identity network.

Disclaimer:

The author is a member of VALID’s ambassador program and owns VALID tokens. The writer’s opinion is his own. This article is for educational purposes only and does not provide financial or investment advice. Please conduct your own thorough research before investing in any cryptocurrency.

Learn more about the importance and mechanism of trust in the digital space. Visit: www.iceberg.digital

About the author:

Daniel Glinz is an experienced management consultant and digital strategist. With broad academic background from leading management and design universities and a strong link to investors and start-ups, Daniel combines groundbreaking conceptual work with the ability to shape and eventually transform businesses. Find out more about digital strategy consulting on www.glinz.co.

Literature:

Nguyen, C. M.-H., Haynes, P., Maguire, S., Friedberg, J., 2013. A User-Centred Approach to the Data Dilemma: Context, Architecture, and Policy, in: Digital Enlightenment Yearbook 2013, Hildebandt M. et al. (Eds.), Amsterdam, IOS Press BV, 227–242

WEF, 2013. Unlocking the Value of Personal Data: From Collection to Usage, World Economic Forum, Industry Agenda

European Commission, 2012. General Data Protection Regulation (GDPR), Brussels

Bargh J.A., McKenna K.Y.A., Fitzsimons G.M., 2002. Can you see the real me? Activation and expression of the “true self” on the internet. Journal of Social Issues. 58(1), 33–48

Rost, K., Stahel, L., Frey, B. S., 2016. Digital Social Norm Enforcement: Online Firestorms in Social Media. PLOS ONE 11(6)

Advances in information and communications technology, combined with exponential growth in data, have given rise to a more empowered consumer. Online users have learned how to compare services and their prices. They have found ways to make their voice heard, by providing feedback and therefore stepping into the territory of traditional brand communications. The downside of this is that the customers themselves, together with their preferences, needs and identity, have become more transparent. Preventing personal data from being processed is no longer a realistic, feasible approach in an Internet enabled society. It is rather increasingly important to understand how businesses should use disclosed data and how control over this data can be brought back to the individual. Today, the majority of digital businesses rely on an ad-centric, data driven business model. However, they have missed and often intentionally neglected the opportunity to establish a trust-based relationship with their customers. Users are forced to blindly trust in fair and lawful conduct. The steady erosion of privacy results in pervasive concerns regarding the use of personal data. In a world where privacy is withering away like ice in summer, understanding and building online trust are essential business capabilities. Digital trust is moving further away from the traditional concept of trust in people and becoming more similar to institution-based trust. This article introduces a comprehensive, strategic approach to understanding digital trust. The iceberg model is used to pay attention to different component of trust and provides a framework that allows marketing professionals to understand how trust is built.

As the data volumes grow, business analytics capabilities become more and more powerful. This underlines the hypothesis that true privacy on the Internet does not exist — any more. Even if a service provider guarantees privacy, users bear a certain risk that their personal data are mined and brought in context with their identity at a later stage. Modern analytics may inadvertently make it possible to re-identify individuals over large data sets. The numbers of data points that can be used to build a rich profile of users are countless. An insurance company can for example monitor the characteristics of a prospect’s keyboard usage (speed of typing and moments of hesitations, typing pressure and usage of small caps versus uppercase), which can reveal relevant information. Hesitation, for example, shows limited decisiveness and therefore may give hints regarding a customer’s willingness to pay. Individuals can often be re-identified from anonymised data because their personal data often narrows possible combinations down and finally leads to the individual. Thus, online privacy is at risk. As a natural reaction to the described developments, consumer concerns regarding the disclosure of personal data are increasing. However, the motivation of online businesses to protect their users privacy and to act as advocates for the customers is often surprisingly low. It is usually limited to pursue objectives in marketing, such as avoiding reputation risks. Businesses have to change their attitude in order to be successful in the future.

Studies conducted by leading management consultancies reveal that the perceived confidence in the security of personal data is alarmingly low. Only about one third of the respondents in Europe trust in the security of their data (Accenture, 2014). The research also identifies significant differences in the acceptability of data use and confidence in the security of personal data between countries. While respondents in India show the highest confidence (72%), only one in four Japanese users think that their data is safe. Users consider certain data types, such as credit card data and financial data, as the most private. Data types that are not directly linked to the identity, but only to the profile of a user (such as age and gender) are perceived as less delicate. But despite consumers’ distrust, there is hope for data scientists and marketing specialists; Although today’s internet users are very much concerned about privacy and the integrity of their business partners, two third of consumers globally are willing to share additional personal data in exchange for additional services or discounts. European companies that successfully establish a trust-based relationship with their customers can increase access to consumer data by about ten times (BGC, 2013).

How digital trust reduces complexity

Ever since the World Wide Web became a global phenomenon, scientists from a wide range of disciplines have tried to make the trust construct comprehensible. These studies all build on the broad scientific work that originates from a more analogue world. Deutsch has developed one of the most fundamental definitions for trust (1962): He defines a framework in which an individual faces two aspects. First, the individual has a choice between multiple options that result in outcomes that are perceived as either positive or negative. Second, the individual acknowledges that the actual result depends on the behavior of another person. Deutsch also mentions that the trusting individual perceives the effect of a bad result as stronger than the effect of the positive outcome. Thus, trust is built, if a person assumes that the aspired beneficial result is more likely to occur than a bad outcome. Important factors in the trust equation are missing control, vulnerability and the existence of risk (Petermann, 1985). The existence of multiple options and diverse scenarios lead to ambiguity and risk. Faced with numerous possibilities, individuals must reduce complexity in order to eventually make a decision and not to fall into the trap of analysis paralysis. Trust is such a mechanism that reduces social complexity. It plays a central role in helping consumers overcome perceptions of risk and insecurity (McKnight, 2002). This context is best captured in the definition of trust developed by Mayer, Davis and Schoorman (1995: 712):

“Trust is the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party“.

A trust model that resembles the shape of an iceberg

In the context of trust, the iceberg metaphor works in many different ways: First of all, trust must be seen as a precious good. It is hard to build, but can be lost very quickly. This makes it both a key differentiator to win against the competition and a potential pitfall that can easily destroy organizations. The Volkswagen emissions scandal has demonstrated how quickly trust is lost. It showed that even strong brands from traditional brick-and-mortar businesses can become severely damaged. The Internet accelerates this process. Bad news or reviews are spread at light speed around the globe and once negligible pieces of information can cause harmful shitstorms to spiral out of control. Like an iceberg at open sea, trust issues must be recognized early and marketers need to sail elegantly around such perils. On the other hand, trust is vital. Ongoing melting of the polar ice caps will inevitably raise the sea level and put coastal areas in danger. Similarly, if the trust factor is neglected, organizations are missing out on potential business opportunities and putting themselves at risk to be sunk by the competition.

Second, the sheer size of an iceberg usually remains unknown to the observer. This is due to ice’s density being less than liquid water’s density. Hence, only one-tenth of the volume of an iceberg is typically above the water level. This resembles the fact that most of the determinants of trust are less known, not understood or simply invisible. Moreover, it is difficult to manipulate those constructs. Trust therefore is often built when no one is looking. For companies this means that there are limited but important options to influence the information asymmetry in digital markets.

Just like the processes of freezing water and forming an iceberg take a lot of time, the process of building trust usually takes time. The primary way of gaining trust is to earn it by developing and nurturing a relationship with customers and future prospects. Companies can and must have control over the quality and intensity of the customer experience if they want to influence the level of trust of the customer. Opportunities to shape the experience exist at any touchpoints in the customer decision lifecycle. This model however focuses on understanding and engendering initial trust.

The iceberg trust model differentiates between two major components that influence formation of initial trust: Trust constructs and cues/signals. Trust constructs refers to elements of the iceberg that are usually under water. They represent the schemata that unconsciously steer our behavior. Constructs represent mechanisms driven by our hot, affective system. Cues or signals refer to strategic areas and design patterns companies apply to engender trust. They are above the surface and allow for reducing information asymmetry.

The iceberg model suggests four clusters of trust cues that sit at the tip of the iceberg: Reciprocity, brand, social adaptor and social protector. Each cluster holds again a set of trust signals or trust design patterns (please refer to the online documentation on iceberg.digital® for details on trust design patterns) that can be considered by marketing professionals in order to engender trust.

• Reciprocity: A social construct that describes the act of rewarding kind actions by responding to a positive action with another positive action. A fair level of reciprocity is reached through transparent exchange of information for appropriate compensation.
• Brand: A company makes a certain commitment when investing in its brand, reputation and awareness. Capital invested into a brand can be considered a pledge that is at stake with every customer interaction and every transaction.
• Social adaptor: A strategic element that connects the cues with the base of the iceberg as an interface to the construct of institution-based trust. The social adaptor holds innovative strategic elements in the technological space that influence how consumers perceive structural assurance and situation normality. Online users that perceive high situation normality would belief that, in general agents in the environment have the required trust building attitudes; competence, benevolence and integrity.
• Social protector: The empowered customer has identified online review systems as a very intuitive, quite robust and therefore useful source of information for decision-making. Trust design patterns within this cluster can support users to avoid interactions that result in a loss. They can help to overcome ‘tragedies of the commons’ and create social benefits.

Why digital minds do not act rationally

Giving away personal data in a situation of uncertainty and without adequate compensation seams to be an irrational behavior. This contrasts sharply with the traditional economic theory of a rationally acting individual, the Homo Economicus. Neuroeconomists and modern behavioral economists have demonstrated that consumers are, in fact, not rational in their decision-making. The same can be said for the consumer in the digital space and here are the reasons why.

An understanding of the basic structure and mechanisms of the human brain is key to understanding the digital customer’s mind. The most fundamental insight in this context is the fact that our thinking relies on two distinct systems. The first system operates automatically and quickly, with little or no effort and no sense of voluntary control. The second operates more slowly and allocates attention to the effortful mental activities that demand it. Popular science names these systems the cool and hot thinking (Mischel, 2014) or simply system 1 and system 2 (Kahneman, 2011).

When prompted to provide personal data online, a customer will always process information though the two systems. It is key to know that the cool and the hot system always have an influence on and compete with each other. If we face a stressful situation, the cognitive system powers down and resources are allocated to the hot system. Digital consumers need to be able to resist temptations and to cool allures down in order to be able to make rational, deliberate decisions. Professor Mischel identifies a set of powerful strategies that allow individuals to increase their level of self-control. Among these is the strategy to reflect on one’s own situation through the eyes of another. The cool system can more easily be activated when a hot decision is made for another person. This may explain the success of online recommendations and third party endorsements. Users may make wrong purchase decisions, but their online reviews (e.g. on Tripadvisor) are very specific and helpful for others to prevent the same mistakes. Reviewers are able to conceive meaningful recommendations, even if they have not bought or consumed the service or product themselves. An increased sensitivity of online customers regarding privacy and higher competence in dealing with personal data support the activation and therefore the weight of the cognitive, so the cold, system. The evolution of the digital mind will make it increasingly more difficult to extract an email address from a visitor with promises of a chance to win an iPhone. Already, users have become more willing to deliberate a truly strong password instead of a 4-digit number — which was probably the user’s birthday. Building initial trust relies heavily on the evaluation of existing schemata. Such schemata describe a pattern of thought or behavior that organizes categories of information and the relationships among them. Schemata can be seen as memory traces. Part of the journey towards a more sustainable approach to personal data is to benevolently support customers in building new competence in dealing with data and eventually to develop these schemata’s.

Another interesting perspective originates from Behavioral Economics. Economists provide a set of theories that contribute to an answer on how people act under uncertainty. This discipline has been dominated for years by the picture of the homo economicus — an individual that acts fully rationally without being influenced by emotions. The homo economicus draws on the cool system as described above to align his decisions fully on the maximization of benefit and minimization of cost. However, friends, feelings and preferences influence even the coolest character in his behavior. Our brain is “hard-wired” to socially interact with other human beings. And all these interactions lead to information and eventually schemata that influence our decisions made by the hot system even before the cool system has a chance to kick in. Empowered by technology, consumers have become networked minds, social decision-makers, more than ever before (Helbing, 2015) and so more vulnerable to ‘hot’ influencing factors.

The traditional economic concept of the rationally acting individual turns out to be incorrect. Decision-making can best be explained with the theory of bounded rationality (Simon, 1997). According to this, people tend to make decisions with particular biases. Understanding these biases gives insights into the mind of the consumers and eventually reveals strategies for marketing to influence costumer decisions. In particular, the prospect theory helps to explain several behaviours observed in economics (Kahneman/Tversky, 1979). The different perspectives on looming gains and losses may explain why the same person may buy both an insurance policy and a lottery ticket. Please refer to our online documentation on iceberg.digital® for more insights about cognitive biases.

Required capabilities to engender digital trust

The ability to establish a mutual, honest dialog and a long-term partnership with customers will be central to defining the winners of tomorrow. In order to engender trust and to build a true partnership with their customers, companies need to go a step further in the evolution of marketing. MIT Sloan Professor Glen Urban has conceived an interesting approach that is fully oriented to the characteristics and needs of the empowered customer. Urban describes an advanced form of market-orientation that responds to the new drivers of consumer choice, involvement and knowledge. Customer Advocacy aims to build deeper customer relationships by earning new levels of trust and commitment (Lawer et al., 2006). It is all about the development of mutual transparency, dialogue and partnership with customers. Customer Advocacy uses the mindset and tools of the CRM-approach, as well as orientation towards total quality and customer satisfaction as a foundation. Companies following this strategy act as honest and unselfish advocates for the customer. This includes providing the customer with the best offer, even if it comes from a competitor (Urban 2005). In return, the consumer offers trust to the company and is more willing to share information.

There is no standard process for identifying and solving trust issues. However, in order to leverage digital trust as a currency and as a key differentiator for future success, companies can build and practice three major business capabilities. Engendering the belief of competency, benevolence and integrity constitutes a poorly bounded problem. These are problems that are essentially unique. Solutions to these “wicked” problems are not true-or-false, but better or worse. It therefore requires a dynamic approach to problem solving. Furthermore, it requires an effective approach that eventually identifies often hidden problems. Last but not least, it requires an empathy-based, human centered process that focuses on actual user needs. All of these requirements are met with the Design Thinking methodology by the Stanford Design School.

• Understand: Businesses must be able to find their customers’ issues and therefore to correctly understand their situation. Although analytics can support this step, businesses have to rely rather on “thick” than on “big data”. Whereas Big Data can give insights into customer behavior on a massive scale, Thick Data aims to reveal underlying motivations, intentions and emotions. Specific techniques such as interviews, contextual research and usability studies can help collect data required for empathizing, ideating, prototyping, testing and learning.
• Enable: Answer the question about the “why?” first. Then conceive plans for the “what?” and the “how?”. It is critical to align all trust strategy measures to clearly articulated business objectives. Companies may build a value tree that clearly structures potential value drivers and outlines objectives. These objectives will be used to evaluate projects that result from ideation and prototyping. A set of trust metrics will help to build the business case and to control initiatives. Furthermore, guiding principles and a solid code of conduct need to be established.
• Engender: Trust is earned over time and further shaped on every touch point within the customer life cycle. Companies must actively manage their roadmap of trust initiatives. Feedbacks from implementation need to be considered and may require further actions and adaptations. Engendering trust is an iterative process.

The “balancing act” of competing business interests and privacy requirements will be of increasing importance in the near future. Marketing often takes advantage of the consumer’s bounded rationality and their well known cognitive biases. Such a rather opportunistic approach in digital markets is not sustainable. Electronic markets have the potential to substantially reduce information asymmetry and transaction costs. They offer effective opportunities to gain insights into needs and behaviours of customers. Extracting these insight requires respectful and farsighted handling of personal data. The digital revolution with its explosive growth of data and system complexity is about to transform industries again. In order to harness the potential of data, the digital economy and its data driven business models must get on a journey towards trust-based customer relationships. It all starts with a better understanding of digital trust.

Please visit www.iceberg.digital to learn more about digital trust.

About the author:

Daniel Glinz is an experienced management consultant and digital strategist. With broad academic background from leading management and design universities and a strong link to investors and start-ups, Daniel combines groundbreaking conceptual work with the ability to shape and actually transform businesses. Find out more about digital transformation consulting on www.glinz.co.

References:

Accenture, 2014, CMT Digital Consumer Survey.

BCG, 2013, Global Consumer Sentiment Survey. In: The trust advantage, How to Win with Big Data.

Deutsch, M., 1962, Cooperation and Trust: Some Theoretical Notes. In: Marshall, Jones R.: Nebraska Symposium on Motivation, Lincoln, University of Nebraska Press: 275–318.

Helbing, D., 2015, Thinking Ahead, Essays on Big Data, Digital Revolution, and Participatory Market Society, Springer

Kahneman, D., Tversky, A., 1979, Prospect Theory: An Analysis of Decision under Risk. Econometrica 47 (2): 263–292.

Kahneman, D., 2011, Thinking, fast and slow, Penguin.

Lawer, C., Knox, S., 2006, Customer Advocacy and Brand Management. Journal of Product & Brand Management 15 (2): 121–129.

Mayer, R. C., Davis, J. H., Schoorman, D. F., 1995, An integrative model of organisational trust. Academy of Management Review 20 (3): 709–734.

McKnight, D. H., Choudhury, V., Kacmar, C ., 2002, Developing and validating trust measures for e-commerce: An integrative typology. Information systems research 13 (3): 334–359.

Mischel, W., 2014, The Marshmallow Test: Why Self-Control Is the Engine of Success.

Petermann, F., 1985, Psychologie des Vertrauens. Salzburg: Müller.

Simon, H. A., 1997, An empirically-based microeconomics. Cambridge University Press.

Urban, G., 2005, Don’t Just Relate — Advocate!: A Blueprint for Profit in the Era of Customer Power. Wharton School Publishing.

Photo: Kuromon Ichiba Market, Ōsaka-shi, Japan, by Andy Kelly on unsplash.com

This article has initially been published in the Center for Digital Business Yea(h)rbook 2015,

ISBN: 978–3–03805–041–4 (Print), 978–3–03805–191–6 (PDF), 978–3–03805–192–3 (ePub), 978–3–03805–193–0 (mobi/Kindle)

Originally published at www.glinz.co on January 18, 2018.

Customers, together with their preferences, needs and identity, have become more transparent. Preventing personal data from being processed is no longer a realistic, feasible approach in an Internet enabled society. It is rather increasingly important to understand how businesses should use disclosed data and how control over this data can be brought back to the individual.

In a world where privacy is withering away like ice in summer, understanding and building online trust are essential business capabilities. The ability to establish a mutual, honest dialog and a longterm partnership with customers will be central to defining the winners of tomorrow.

As a management consultant I had the pleasure to conceive and implement numerous digital strategy projects that strongly influenced digital trust.

Independent of the exact topic — be it the design of a Big Data strategy or the implementation of a large scale, global social media monitoring solution — the design of a digital strategy can and should be always approached by following three steps.

Each step builds up and trains one of the fundamental business capabilities:

1. Businesses need to get a good understanding of the issues and potential outcomes at hand. Paying attention to the fact that maturity levels in dealing with customer data differs dramatically from company to company, organizations would be wise to first listen and learn.
2. Companies need to build the necessary foundations to successfully enable trust cues. This involves primarily identifying and effectively evaluating the options in form of use cases and testing these cases rapidly in prototypes.
3. Thirdly, the proposed project approach suggests implementing a governance and operating structure that allows managing and optimizing initiatives and ultimately engendering trust. In the last section of this article the three key capabilities will be mapped to single activities that together constitute an exemplary project. This project approach to engendering trust should help to make this abstract topic more tangible and actionable.

These three capabilities that are essential to effectively deal with digital trust can be built in a partly sequential approach. The recommended project approach to engendering digital trust first tries to answer the question “why” the organisation needs to act, then identifies “what” needs to be done and ultimately defines “how” the organisation needs to operate.

Please visit www.iceberg.digital to learn more about digital trust and to get a copy of the free whitepaper.

It is in the nature of things that the digital economy relies on consumer data. Access to data will become more and more critical, and consumer trust is the key that will unlock it. The digital revolution with its explosive growth of data and system complexity is about to transform industries again. In order to harness the potential of data, the digital economy and its data driven business models must embark on a journey towards trust-based customer relationships. It all starts with a better understanding of digital trust.

About the autor

Daniel Glinz is an experienced management consultant and digital strategist. With a broad academic background from leading management and design universities and a strong link to investors and start-ups, Daniel combines groundbreaking conceptual work with the ability to shape and effectively transform businesses.

Find out more about digital transformation consulting on www.glinz.co.

Parts of the whitpaper have been published in the HWZ Center for Digital Business Yea(h)rbook. Get a copy here.

Originally published at www.glinz.net.