Traditionally, welfare systems have been plagued by inefficiencies, a lack of transparency, and susceptibility to fraud. These systems, often burdened with bureaucratic red tape, struggle to ensure that aid reaches the right people in a timely manner. As the world moves towards digital, governments are also adopting digital solutions to automate their services and address inefficiencies in the public sector. Whilst technology presents incredible new opportunities to break down siloes and ensure an equitable world for all, a growing digital divide means that a significant portion of the population remains digitally excluded.
This creates a paradox: the digital tools designed to streamline and improve welfare distribution remain inaccessible to the very people that they’re intended to serve.
Covid-19 put a spotlight on the digital divide – the gap between people that have access to modern IT and those that don’t. The term is most commonly used in the context of the internet and online services. During lockdown, many people struggled to stay connected – in extreme cases, becoming completely isolated – as society pivoted towards digital platforms to deliver vital public services and remote schooling.
The pandemic also acted as a catalyst to the digital transformation of the labour market, which is increasingly dominated by AI, algorithms, and automation. All of this raises an important question around the government’s role in addressing hurdles to connectivity, affordability and accessibility that constitute the divide.
And the gap is evolving. From mobile phones and the internet to social media and AI, the rapid development and adoption of digital technologies means the concept of digital inclusion is a continuously moving target.
Digital welfare
An important concept is the notion of ‘digital welfare’, a term with multi-layered connotations depending on the context within which it is used.
One interpretation of the term, which we have already alluded to, is the digitalisation of welfare services. Automated decision-making systems based on AI/ML are being widely implemented in welfare systems around the world – spanning the US, UK, India, Australia, and parts of Europe – to improve operational efficiency while clamping down on fraud. Examples include Universal Credit in the UK, MiDAS in the US, and Aadhar in India. There have been numerous cases where these systems have gone wrong, resulting in severe consequences for vulnerable people reliant on welfare.
Choosing the right technology
A further key theme in the recent Government Cyber Security Strategy was the importance of Security Data, not only as a way to understand risk and identify vulnerabilities but, more importantly, to identify events before they become incidents. The private sector has successfully battled this issue for the last century, with the emergence of technologies like Endpoint Detection & Response (EDR) and Next-Generation Anti-Virus (NGAV) solutions becoming the core data solution. When the right solution is chosen, these tools allow organisations to focus on visibility and the ability to identify threats before they become a real issue to the enterprise. The combination of EDR and NGAV has allowed the private sector to automatically combine threat intelligence and modern technologies like AI and machine learning and other modern threat detection techniques such as behaviour to focus on the real threats to an organisation.
“As humankind moves toward the digital welfare future, it needs to alter course significantly and rapidly to avoid stumbling zombie-like into a digital welfare dystopia.”
Professor Philip G. Alston – UN report of the Special rapporteur on extreme poverty and human rights (2019)
Another interpretation of the term ‘digital welfare’ concerns the actual wellbeing of people in the digital world.
Governments are taking steps to protect people online from cyber related crimes like scams and bullying through regulations such as the UK’s Online Safeguarding Act.
There is increased awareness of fake news leading to misinformation across the internet and social media, although this is proving much harder to regulate.
There is also a growing imperative to ensure equal opportunities in the workplace to avoid people falling into an algorithmic inequality trap. The trap refers to the algorithmic discrimination and bias against people who may lack digital skills and literacy, stifling their opportunities for career advancement. An extreme example of this is employees who rarely interact with human co-workers, such as Uber drivers. The increase in popularity of tools like ChatGPT that are trained on large language models (historical datasets that are inextricably linked to unconscious biases) raises ethical concerns about the long-term implications of GenAI perpetuating existing social inequalities.
Let’s take a moment to unpick the origins of unconscious bias, and how societal trends are reinforced in the digital world.
Society, defined as a group of people who live in a definable community and share the same culture, is served by public institutions that are predicated on those cultural norms. A culture is a set of belief systems – the ideas, customs, and social behaviour of a particular people – and unconscious biases arise from the unique values and traditions inherent to that culture. These belief systems feed into the information disseminated by institutions that inform our policies and political preferences. This information then becomes data in the digital world, which drives the tools and platforms that we use in society. The result is that our social constructs are reinforced in the digital world, generating a self-fulfilling prophecy whereby existing social divisions will always manifest in digital forms.
In other words, without appropriate institutional change via policies to tackle these trends, we end up in a self-reinforcing cycle of social and digital division.
“The self-fulfilling prophecy, whereby fears are translated into reality, operates only in the absence of deliberate institutional controls.”
Professor Robert K. Merton
The Self-Fulfilling Prophecy. The Antioch Review 8.2 (1948): 193-210
Evidently, this interpretation of digital welfare is a loaded concept and it is easy to get lost in its connotations. Taking a step back, let’s capture the key problem statements we need to address:
How can public institutions ensure that they and the people they serve do not get left behind in an increasingly digital world? How can governments leverage emerging technologies in a responsible and inclusive way? How can technology be used to bridge the digital divide that it created and ensure equitable digital welfare for all?
Introducing Blockchain for Digital Welfare
At first glance, digital inclusivity and blockchain technology might seem incompatible. Blockchain as a digital innovation appears ill-suited to address the needs of those who are digitally marginalised. So, how can blockchain support digital welfare?
The answer lies in the fundamental features of the technology. Blockchain emphasises security, transparency, and decentralisation. It increases accountability, auditability, and provides secure platforms to exchange data. All these things could make for a more inclusive and effective welfare state.
To understand blockchain, imagine a public digital ledger, similar to a shared document. Everyone can see changes to the document, but no one can alter the past entries. Now add to this the monetary aspect of blockchain, i.e., digital currencies, and we can incentivise participation in the network. For example, a blockchain-based welfare system could trigger a local community project aimed at improving digital literacy, where blockchain technology would be used to incentivise and track participation, distribute rewards, and ensure transparent reporting of the project’s outcomes.
However, it is worth pointing out that technology alone cannot tackle the digital divide.
The demographics of digitally excluded people include the elderly, people living in remote/rural regions, low-income households, or people with disabilities. Digitally excluded people therefore need personalised support from their local communities that is suitable for their unique needs. As an open network, blockchain can enable community-level participation and even incentivise interventions by rewarding positive contributions towards digital welfare, encouraging a more active and supportive welfare environment.
Macro versus Micro-welfare systems
My paper describing a blockchain-based welfare management system distinguishes between ‘macro-welfare’ (the traditional policy-driven welfare system) and ‘micro-welfare’ (community actions within the incentive-driven micro-welfare system). This distinction allows for a more nuanced approach to welfare distribution, catering to both broad policy goals and local community needs.
In the macro-welfare system, the model integrates blockchain technology with existing welfare policies. This integration enhances the efficiency and reach of welfare services, ensuring that they align with broader policy goals. The blockchain’s role here is to provide a transparent and reliable platform for managing welfare records at a regional level. Some might worry that increasing transparency and auditability can imply increased surveillance, but the cryptographic functions inherent to blockchain technology can ensure that private welfare records are never stored on the public ledger. Instead, a uniquely identifiable fingerprint would be stored of digital welfare data at a regional level, i.e., never at the individual level and never storing data on the blockchain in its raw form.
The micro-welfare system focuses on community-level initiatives within the welfare model. It emphasises local actions and support, facilitated by the blockchain’s incentive-driven approach. This system plays a crucial role in bridging the digital divide and ensuring that welfare support is accessible at the community level.
Processes within the micro-welfare system can be automated through smart contracts. These contracts specify conditions and states for welfare processes, ensuring a transparent and auditable trail of events. The blockchain’s immutable nature ensures that historical records of welfare distribution are reliable and tamper-proof, enhancing accountability. The incentivization framework borrows from Bitcoin’s model, creating an ecosystem where both welfare recipients and providers are encouraged to participate positively. This framework introduces gamification and reward mechanisms, offering financial or non-financial incentives. It aims to stimulate local community action, ensuring active participation and fostering a supportive environment for effective welfare distribution.
Challenges and Future Outlook
Despite its potential, integrating blockchain into existing welfare systems is not without challenges. These include the need for technological infrastructure, the energy efficiency of the network, ensuring scalability to handle vast numbers of transactions, and fostering digital literacy among all stakeholders.
However, the future of blockchain in welfare distribution is promising. As we address these challenges, blockchain’s role in welfare systems is poised to grow, paving the way for a more inclusive, efficient, and transparent distribution of aid.
Conclusion
Blockchain technology, typically associated with digital currencies, has the potential to revolutionise welfare systems worldwide. Its application in welfare distribution could lead to a more transparent, efficient, and equitable system, ensuring that aid reaches those who need it most.
Embracing blockchain in welfare distribution is not just about technological advancement; it’s about fostering a more inclusive society where technology plays a crucial role in supporting the most vulnerable.
Chloe Tartan leads tech advisory and delivery for sustainability use cases at Accenture UK&I.
Previously a researcher and head of sustainability at blockchain R&D company nChain, Chloe has a PhD in engineering science from the University of Oxford and a Master’s degree in photonics from the University of Cambridge.
