Imagine answering a call from a candidate who never dialled, or watching a breaking video of a scandal that never happened. Picture receiving a personalised message that speaks directly to your deepest political fears, crafted not by human hands but by algorithms that know your voting history better than your family does. This isn't science fiction—it's the 2025 election cycle, where synthetic media reshapes political narratives faster than fact-checkers can respond. As artificial intelligence tools become increasingly sophisticated and accessible, the line between authentic political discourse and manufactured reality grows ever thinner.

We're witnessing the emergence of a new electoral landscape where deepfakes, AI-generated text, and synthetic audio can influence voter perceptions at unprecedented scale. This technological revolution arrives at a moment when democratic institutions already face mounting pressure from disinformation campaigns and eroding public trust. The question is no longer whether AI will impact elections, but whether truth itself remains a prerequisite for electoral victory.

The Architecture of Digital Deception

The infrastructure for AI-generated political content has evolved rapidly from experimental technology to readily available tools. Modern generative AI systems can produce convincing video content, synthesise speech patterns, and craft persuasive text that mirrors human writing styles with remarkable accuracy. These capabilities have democratised the creation of sophisticated propaganda, placing powerful deception tools in the hands of anyone with internet access and basic technical knowledge.

The sophistication of current AI systems means that detecting synthetic content requires increasingly specialised expertise and computational resources. While tech companies have developed detection systems, these tools often lag behind the generative technologies they're designed to identify. This creates a persistent gap where malicious actors can exploit new techniques faster than defensive measures can adapt. The result is an ongoing arms race between content creators and content detectors, with electoral integrity hanging in the balance.

Political campaigns have begun experimenting with AI-generated content for legitimate purposes, from creating personalised voter outreach materials to generating social media content at scale. However, the same technologies that enable efficient campaign communication also provide cover for more nefarious uses. When authentic AI-generated campaign materials become commonplace, distinguishing between legitimate political messaging and malicious deepfakes becomes exponentially more difficult for ordinary voters.

The technical barriers to creating convincing synthetic political content continue to diminish. Cloud-based AI services now offer sophisticated content generation capabilities without requiring users to possess advanced technical skills or expensive hardware. This accessibility means that state actors, political operatives, and even individual bad actors can deploy AI-generated content campaigns with relatively modest resources. The democratisation of these tools fundamentally alters the threat landscape for electoral security.

The speed at which synthetic content can be produced and distributed creates new temporal vulnerabilities in democratic processes. Traditional fact-checking and verification systems operate on timescales measured in hours or days, while AI-generated content can be created and disseminated in minutes. This temporal mismatch allows false narratives to gain traction and influence voter perceptions before authoritative debunking can occur. The viral nature of social media amplifies this problem, as synthetic content can reach millions of viewers before its artificial nature is discovered.

Structural Vulnerabilities in Modern Democracy

The American electoral system contains inherent structural elements that make it particularly susceptible to AI-driven manipulation campaigns. The Electoral College system, which allows candidates to win the presidency without securing the popular vote, creates incentives for highly targeted campaigns focused on narrow geographical areas. This concentration of electoral influence makes AI-generated content campaigns more cost-effective and strategically viable, as manipulating voter sentiment in specific swing states can yield disproportionate electoral returns.

Consider the razor-thin margins that decide modern American elections: in 2020, Joe Biden won Georgia by just 11,779 votes out of over 5 million cast. In Arizona, the margin was 10,457 votes. These numbers represent a fraction of the audience that a single viral deepfake video could reach organically through social media sharing. A synthetic clip viewed by 100,000 people in these states—requiring no advertising spend and achievable through organic social media distribution—would need to influence just 10% of viewers to swing the entire election. This mathematical reality transforms AI-generated content from a theoretical threat into a practical weapon of unprecedented efficiency.

The increasing frequency of Electoral College and popular vote splits—occurring twice in the last six elections—demonstrates how these narrow margins in key states can determine national outcomes. This mathematical reality creates powerful incentives for campaigns to deploy any available tools, including AI-generated content, to secure marginal advantages in contested areas. When elections can be decided by thousands of votes across a handful of states, even modest shifts in voter perception achieved through synthetic media can prove decisive.

Social media platforms have already demonstrated their capacity to disrupt established political norms and democratic processes. The 2016 election cycle showed how these platforms could be weaponised to hijack democracy through coordinated disinformation campaigns. AI-generated content represents a natural evolution of these tactics, offering unprecedented scale and sophistication for political manipulation. The normalisation of norm-breaking campaigns has created an environment where deploying cutting-edge deception technologies may be viewed as simply another campaign innovation rather than a fundamental threat to democratic integrity.

The focus on demographic micro-targeting in modern campaigns creates additional vulnerabilities for AI exploitation. Contemporary electoral strategy increasingly depends on making inroads with specific demographic groups, such as Latino and African American voters in key swing states. AI-generated content can be precisely tailored to resonate with particular communities, incorporating cultural references, linguistic patterns, and visual elements designed to maximise persuasive impact within targeted populations. This granular approach to voter manipulation represents a significant escalation from traditional broadcast-based propaganda techniques.

The fragmentation of media consumption patterns has created isolated information ecosystems where AI-generated content can circulate without encountering contradictory perspectives or fact-checking. Voters increasingly consume political information from sources that confirm their existing beliefs, making them more susceptible to synthetic content that reinforces their political preferences. This fragmentation makes it easier for AI-generated false narratives to take hold within specific communities without broader scrutiny, creating parallel realities that undermine shared democratic discourse.

The Economics of Synthetic Truth

The cost-benefit analysis of deploying AI-generated content in political campaigns reveals troubling economic incentives that fundamentally alter the landscape of electoral competition. Traditional political advertising requires substantial investments in production, talent, and media placement. A single television advertisement can cost hundreds of thousands of pounds to produce and millions more to broadcast across key markets. AI-generated content, by contrast, can be produced at scale with minimal marginal costs once initial systems are established. This economic efficiency makes synthetic content campaigns attractive to well-funded political operations and accessible to smaller actors with limited resources.

The return on investment for AI-generated political content can be extraordinary when measured against traditional campaign metrics. A single viral deepfake video can reach millions of viewers organically through social media sharing, delivering audience engagement that would cost hundreds of thousands of pounds through conventional advertising channels. This viral potential creates powerful financial incentives for campaigns to experiment with increasingly sophisticated synthetic content, regardless of ethical considerations or potential harm to democratic processes.

The production costs for synthetic media continue to plummet as AI technologies mature and become more accessible. What once required expensive studios, professional actors, and sophisticated post-production facilities can now be accomplished with consumer-grade hardware and freely available software. This democratisation of production capabilities means that even modestly funded political operations can deploy synthetic content campaigns that rival the sophistication of major network productions.

Political consulting firms have begun incorporating AI content generation into their service offerings, treating synthetic media production as a natural extension of traditional campaign communications. This professionalisation of AI-generated political content legitimises its use within mainstream campaign operations and accelerates adoption across the political spectrum. As these services become standard offerings in the political consulting marketplace, the pressure on campaigns to deploy AI-generated content or risk competitive disadvantage will intensify.

The international dimension of AI-generated political content creates additional economic complications that challenge traditional concepts of campaign finance and foreign interference. Foreign actors can deploy synthetic media campaigns targeting domestic elections at relatively low cost, potentially achieving significant influence over democratic processes without substantial financial investment. This asymmetric capability allows hostile nations or non-state actors to interfere in electoral processes with minimal risk and maximum potential impact, fundamentally altering the economics of international political interference.

The scalability of AI-generated content production enables unprecedented efficiency in political messaging. Traditional campaign communications require human labour for each piece of content created, limiting the volume and variety of messages that can be produced within budget constraints. AI systems can generate thousands of variations of political messages, each tailored to specific demographic groups or individual voters, without proportional increases in production costs. This scalability advantage creates powerful incentives for campaigns to adopt AI-generated content strategies.

Regulatory Frameworks and Their Limitations

Current regulatory approaches to AI-generated content focus primarily on commercial applications rather than political uses, creating significant gaps in oversight of synthetic media in electoral contexts. The Federal Trade Commission's guidance on endorsements and advertising emphasises transparency and disclosure requirements for paid promotions, but these frameworks don't adequately address the unique challenges posed by synthetic political content. The emphasis on commercial speech regulation leaves substantial vulnerabilities in the oversight of AI-generated political communications.

Existing election law frameworks struggle to accommodate the realities of AI-generated content production and distribution. Traditional campaign finance regulations focus on expenditure reporting and source disclosure, but these requirements become meaningless when synthetic content can be produced and distributed without traditional production costs or clear attribution chains. The decentralised nature of AI content generation makes it difficult to apply conventional regulatory approaches that assume identifiable actors and traceable financial flows.

The speed of technological development consistently outpaces regulatory responses, creating persistent vulnerabilities that malicious actors can exploit. By the time legislative bodies identify emerging threats and develop appropriate regulatory frameworks, the underlying technologies have often evolved beyond the scope of proposed regulations. This perpetual lag between technological capability and regulatory oversight creates opportunities for electoral manipulation that operate in legal grey areas or outright regulatory vacuums.

International coordination on AI content regulation remains fragmented and inconsistent, despite the global nature of digital platforms and cross-border information flows. While some jurisdictions have begun developing specific regulations for synthetic media, the global nature of digital platforms means that content banned in one country can easily reach voters through platforms based elsewhere. This regulatory arbitrage creates opportunities for malicious actors to exploit jurisdictional gaps and deploy synthetic content campaigns with minimal legal consequences.

The enforcement challenges associated with AI-generated content regulation are particularly acute in the political context. Unlike commercial advertising, which involves clear financial transactions and identifiable business entities, political synthetic content can be created and distributed by anonymous actors using untraceable methods. This anonymity makes it difficult to identify violators, gather evidence, and impose meaningful penalties for regulatory violations.

The First Amendment protections for political speech in the United States create additional complications for regulating AI-generated political content. Courts have traditionally applied the highest level of scrutiny to restrictions on political expression, making it difficult to implement regulations that might be acceptable for commercial speech. This constitutional framework limits the regulatory tools available for addressing synthetic political content while preserving fundamental democratic rights.

The Psychology of Synthetic Persuasion

AI-generated political content exploits fundamental aspects of human psychology and information processing that make voters particularly vulnerable to manipulation. The human brain's tendency to accept information that confirms existing beliefs—confirmation bias—makes synthetic content especially effective when it reinforces pre-existing political preferences. AI systems can be trained to identify and exploit these cognitive vulnerabilities with unprecedented precision and scale, creating content that feels intuitively true to target audiences regardless of its factual accuracy.

The phenomenon of the “illusory truth effect,” where repeated exposure to false information increases the likelihood of believing it, becomes particularly dangerous in the context of AI-generated content. A deepfake clip shared three times in a week doesn't need to be believed the first time; by the third exposure, it feels familiar, and familiarity masquerades as truth. Synthetic media can be produced in virtually unlimited quantities, allowing for sustained repetition of false narratives across multiple platforms and formats. This repetition can gradually shift public perception even when individual pieces of content are eventually debunked or removed.

Emotional manipulation represents another powerful vector for AI-generated political influence. Synthetic content can be precisely calibrated to trigger specific emotional responses—fear, anger, hope, or disgust—that motivate political behaviour. AI systems can analyse vast datasets of emotional responses to optimise content for maximum psychological impact, creating synthetic media that pushes emotional buttons more effectively than human-created content. This emotional targeting can bypass rational evaluation processes, leading voters to make decisions based on manufactured feelings rather than factual considerations.

The personalisation capabilities of AI systems enable unprecedented levels of targeted psychological manipulation. By analysing individual social media behaviour, demographic information, and interaction patterns, AI systems can generate content specifically designed to influence particular voters. This micro-targeting approach allows campaigns to deploy different synthetic narratives to different audiences, maximising persuasive impact while minimising the risk of detection through contradictory messaging.

Emerging research suggests even subtle unease may not inoculate viewers, but can instead blur their critical faculties. When viewers experience a vague sense that something is “off” about synthetic content without being able to identify the source of their discomfort, this ambiguous response can create cognitive dissonance that makes them more susceptible to the content's message as they struggle to reconcile their intuitive unease with the apparent authenticity of the material.

Social proof mechanisms, where individuals look to others' behaviour to guide their own actions, become particularly problematic in the context of AI-generated content. Synthetic social media posts, comments, and engagement metrics can create false impressions of widespread support for particular political positions. When voters see apparent evidence that many others share certain views, they become more likely to adopt those positions themselves, even when the supporting evidence is entirely artificial.

Case Studies in Synthetic Influence

Recent electoral cycles have provided early examples of AI-generated content's political impact, though comprehensive analysis remains limited due to the novelty of these technologies. The 2024 New Hampshire primary featured a particularly striking example when voters received robocalls featuring what appeared to be President Biden's voice urging them not to vote in the primary days before the election. The synthetic audio was sophisticated enough to fool many recipients initially, though it was eventually identified as a deepfake and traced to a political operative. This incident demonstrated both the potential effectiveness of AI-generated content and the detection challenges it poses for electoral authorities.

The 2023 Slovak parliamentary elections featured sophisticated voice cloning technology used to create fake audio recordings of a liberal party leader discussing vote-buying and media manipulation. The synthetic audio was released just days before the election, too late for effective debunking but early enough to influence voter perceptions. This case demonstrated how foreign actors could deploy AI-generated content to interfere in domestic elections with minimal resources and maximum impact.

The use of AI-generated text in political communications has become increasingly sophisticated and difficult to detect. Large language models can produce political content that mimics the writing styles of specific politicians, journalists, or demographic groups with remarkable accuracy. This capability has been exploited to create fake news articles, social media posts, and even entire websites designed to appear as legitimate news sources while promoting specific political narratives. The volume of such content has grown exponentially, making comprehensive monitoring and fact-checking increasingly difficult.

Audio deepfakes present particular challenges for political verification and fact-checking due to their relative ease of creation and difficulty of detection. Synthetic audio content can be created more easily than video deepfakes and is often harder for ordinary listeners to identify as artificial. Phone calls, radio advertisements, and podcast content featuring AI-generated speech have begun appearing in political contexts, creating new vectors for voter manipulation that are difficult to detect and counter through traditional means.

Video deepfakes targeting political candidates have demonstrated both the potential effectiveness and the detection challenges associated with synthetic media. While most documented cases have involved relatively crude manipulations that were eventually identified, the rapid improvement in generation quality suggests that future examples may be far more convincing. The psychological impact of seeing apparently authentic video evidence of political misconduct can be profound, even when the content is later debunked.

The proliferation of AI-generated content has created new challenges for traditional fact-checking organisations. The volume of synthetic content being produced exceeds human verification capabilities, while the sophistication of generation techniques makes detection increasingly difficult. This has led to the development of automated detection systems, but these tools often lag behind the generation technologies they're designed to identify, creating persistent gaps in verification coverage.

The Information Ecosystem Under Siege

Traditional gatekeeping institutions—newspapers, television networks, and established media organisations—find themselves increasingly challenged by the volume and sophistication of AI-generated content. The speed at which synthetic media can be produced and distributed often outpaces the fact-checking and verification processes that these institutions rely upon to maintain editorial standards. This creates opportunities for false narratives to gain traction before authoritative debunking can occur, undermining the traditional role of professional journalism in maintaining information quality.

Social media platforms face unprecedented challenges in moderating AI-generated political content at scale. The volume of synthetic content being produced exceeds human moderation capabilities, while automated detection systems struggle to keep pace with rapidly evolving generation techniques. This moderation gap creates spaces where malicious synthetic content can flourish and influence political discourse before being identified and removed. The global nature of these platforms further complicates moderation efforts, as content policies must navigate different legal frameworks and cultural norms across jurisdictions.

The fragmentation of information sources has created echo chambers where AI-generated content can circulate without encountering contradictory information or fact-checking. Voters increasingly consume political information from sources that confirm their existing beliefs, making them more susceptible to synthetic content that reinforces their political preferences. This fragmentation makes it easier for AI-generated false narratives to take hold within specific communities without broader scrutiny, creating parallel information realities that undermine shared democratic discourse.

The erosion of shared epistemological foundations—common standards for determining truth and falsehood—has been accelerated by the proliferation of AI-generated content. When voters can no longer distinguish between authentic and synthetic media, the concept of objective truth in political discourse becomes increasingly problematic. This epistemic crisis undermines the foundation of democratic deliberation, which depends on citizens' ability to evaluate competing claims based on factual evidence rather than manufactured narratives.

The economic pressures facing traditional media organisations have reduced their capacity to invest in sophisticated verification technologies and processes needed to combat AI-generated content. Newsroom budgets have been cut dramatically over the past decade, limiting resources available for fact-checking and investigative reporting. This resource constraint occurs precisely when the verification challenges posed by synthetic content are becoming more complex and resource-intensive, creating a dangerous mismatch between threat sophistication and defensive capabilities.

The attention economy that drives social media engagement rewards sensational and emotionally provocative content, creating natural advantages for AI-generated material designed to maximise psychological impact. Synthetic content can be optimised for viral spread in ways that authentic content cannot, as it can be precisely calibrated to trigger emotional responses without being constrained by factual accuracy. This creates a systematic bias in favour of synthetic content within information ecosystems that prioritise engagement over truth.

Technological Arms Race

The competition between AI content generation and detection technologies represents a high-stakes arms race with significant implications for electoral integrity. Detection systems must constantly evolve to identify new generation techniques, while content creators work to develop methods that can evade existing detection systems. This dynamic creates a perpetual cycle of technological escalation that favours those with the most advanced capabilities and resources, potentially giving well-funded actors significant advantages in political manipulation campaigns.

Machine learning systems used for content detection face fundamental limitations that advantage content generators. Detection systems require training data based on known synthetic content, creating an inherent lag between the development of new generation techniques and the ability to detect them. This temporal advantage allows malicious actors to deploy new forms of synthetic content before effective countermeasures can be developed and deployed, creating windows of vulnerability that can be exploited for political gain.

The democratisation of AI tools has accelerated the pace of this technological arms race by enabling more actors to participate in both content generation and detection efforts. Open-source AI models and cloud-based services have lowered barriers to entry for both legitimate researchers and malicious actors, creating a more complex and dynamic threat landscape. This accessibility ensures that the arms race will continue to intensify as more sophisticated tools become available to broader audiences, making it increasingly difficult to maintain technological advantages.

International competition in AI development adds geopolitical dimensions to this technological arms race that extend far beyond electoral applications. Nations view AI capabilities as strategic assets that provide advantages in both economic and security domains. This competition incentivises rapid advancement in AI technologies, including those applicable to synthetic content generation, potentially at the expense of safety considerations or democratic safeguards. The military and intelligence applications of synthetic media technologies create additional incentives for continued development regardless of electoral implications.

The adversarial nature of machine learning systems creates inherent vulnerabilities that favour content generators over detectors. Generative AI systems can be trained specifically to evade detection by incorporating knowledge of detection techniques into their training processes. This adversarial dynamic means that each improvement in detection capabilities can be countered by corresponding improvements in generation techniques, creating a potentially endless cycle of technological escalation.

The resource requirements for maintaining competitive detection capabilities continue to grow as generation techniques become more sophisticated. State-of-the-art detection systems require substantial computational resources, specialised expertise, and continuous updates to remain effective. These requirements may exceed the capabilities of many organisations responsible for electoral security, creating gaps in defensive coverage that malicious actors can exploit.

The Future of Electoral Truth

The trajectory of AI development suggests that synthetic content will become increasingly sophisticated and difficult to detect over the coming years. Advances in multimodal AI systems that can generate coordinated text, audio, and video content will create new possibilities for comprehensive synthetic media campaigns. These developments will further blur the lines between authentic and artificial political communications, making voter verification increasingly challenging and potentially impossible for ordinary citizens without specialised tools and expertise.

The potential for real-time AI content generation during live political events represents a particularly concerning development for electoral integrity. As AI systems become capable of producing synthetic responses to breaking news or debate performances in real-time, the window for fact-checking and verification will continue to shrink. This capability could enable the rapid deployment of synthetic counter-narratives that undermine authentic political communications before they can be properly evaluated, fundamentally altering the dynamics of political discourse.

The integration of AI-generated content with emerging technologies like virtual and augmented reality will create new immersive forms of political manipulation that may prove even more psychologically powerful than current formats. These technologies could enable the creation of synthetic political experiences that feel more real and emotionally impactful than traditional media formats. The psychological impact of immersive synthetic political content may prove even more powerful than current text, audio, and video formats, creating new vectors for voter manipulation that are difficult to counter through traditional fact-checking approaches.

The normalisation of AI-generated content in legitimate political communications will make detecting malicious uses increasingly difficult. As campaigns routinely use AI tools for content creation, the presence of synthetic elements will no longer serve as a reliable indicator of deceptive intent. This normalisation will require the development of new frameworks for evaluating the authenticity and truthfulness of political communications that go beyond simple synthetic content detection to focus on intent and accuracy.

The potential emergence of AI systems capable of generating content that is indistinguishable from human-created material represents a fundamental challenge to current verification approaches. When synthetic content becomes perfect or near-perfect in its mimicry of authentic material, detection may become impossible using current technological approaches. This development would require entirely new frameworks for establishing truth and authenticity in political communications, potentially based on cryptographic verification or other technical solutions.

The long-term implications of widespread AI-generated political content extend beyond individual elections to threaten the fundamental nature of democratic discourse. If voters lose confidence in their ability to distinguish truth from falsehood in political communications, they may withdraw from democratic participation altogether or become susceptible to authoritarian alternatives that promise certainty in an uncertain information environment.

Implications for Democratic Governance

The proliferation of AI-generated political content raises fundamental questions about the nature of democratic deliberation and consent that strike at the heart of democratic theory. If voters cannot reliably distinguish between authentic and synthetic political communications, the informed consent that legitimises democratic governance becomes problematic. This epistemic crisis threatens the philosophical foundations of democratic theory, which assumes that citizens can make rational choices based on accurate information rather than manufactured narratives designed to manipulate their perceptions.

The potential for AI-generated content to create entirely fabricated political realities poses unprecedented challenges for democratic accountability mechanisms. When synthetic evidence can be created to support any political narrative, the traditional mechanisms for holding politicians accountable for their actions and statements may become ineffective. This could lead to a post-truth political environment where factual accuracy becomes irrelevant to electoral success, fundamentally altering the relationship between truth and political power.

The international implications of AI-generated political content extend beyond individual elections to threaten the sovereignty of democratic processes in ways that challenge traditional concepts of national self-determination. Foreign actors' ability to deploy sophisticated synthetic media campaigns represents a new form of interference that challenges traditional concepts of electoral independence. This capability could enable hostile nations to influence domestic political outcomes with minimal risk of detection or retaliation, potentially subjugating democratic processes to foreign manipulation.

The long-term effects of widespread AI-generated political content on public trust in democratic institutions remain uncertain but potentially catastrophic for the stability of democratic governance. If voters lose confidence in their ability to distinguish truth from falsehood in political communications, they may withdraw from democratic participation altogether. This disengagement could undermine the legitimacy of democratic governance and create opportunities for authoritarian alternatives to gain support by promising certainty and order in an uncertain information environment.

The potential for AI-generated content to exacerbate existing political polarisation represents another significant threat to democratic stability. Synthetic content can be precisely tailored to reinforce existing beliefs and prejudices, creating increasingly isolated information ecosystems where different groups operate with entirely different sets of “facts.” This fragmentation could make democratic compromise and consensus-building increasingly difficult, potentially leading to political gridlock or conflict.

The implications for electoral legitimacy are particularly concerning, as AI-generated content could be used to cast doubt on election results regardless of their accuracy. Synthetic evidence of electoral fraud or manipulation could be created to support claims of illegitimate outcomes, potentially undermining public confidence in democratic processes even when elections are conducted fairly and accurately.

Towards Adaptive Solutions

Addressing the challenges posed by AI-generated political content will require innovative approaches that go beyond traditional regulatory frameworks to encompass technological, educational, and institutional responses. Technical solutions alone are insufficient given the rapid pace of AI development and the fundamental detection challenges involved. Instead, comprehensive strategies must combine multiple approaches to create resilient defences against synthetic media manipulation while preserving fundamental democratic rights and freedoms.

Educational initiatives that improve media literacy and critical thinking skills represent essential components of any comprehensive response to AI-generated political content. Voters need to develop the cognitive tools necessary to evaluate political information critically, regardless of its source or format. This educational approach must be continuously updated to address new forms of synthetic content as they emerge, requiring ongoing investment in curriculum development and teacher training. However, education alone cannot solve the problem, as the sophistication of AI-generated content may eventually exceed human detection capabilities.

Institutional reforms may be necessary to preserve electoral integrity in the age of AI-generated content, though such changes must be carefully designed to avoid undermining democratic principles. This could include new verification requirements for political communications, enhanced transparency obligations for campaign materials, or novel approaches to candidate authentication. These reforms must balance the need for electoral security with fundamental rights to free speech and political expression, avoiding solutions that could be exploited to suppress legitimate political discourse.

International cooperation will be essential for addressing the cross-border nature of AI-generated political content threats, though achieving such cooperation faces significant practical and political obstacles. Coordinated responses among democratic nations could help establish common standards for synthetic media detection and response, while diplomatic efforts could work to establish norms against the use of AI-generated content for electoral interference. However, such cooperation will require overcoming significant technical, legal, and political challenges, particularly given the different regulatory approaches and constitutional frameworks across jurisdictions.

The development of technological solutions must focus on creating robust verification systems that can adapt to evolving generation techniques while remaining accessible to ordinary users. This might include cryptographic approaches to content authentication, distributed verification networks, or AI-powered detection systems that can keep pace with generation technologies. However, the adversarial nature of the problem means that technological solutions alone are unlikely to provide complete protection against sophisticated actors.

The role of platform companies in moderating AI-generated political content remains contentious, with significant implications for both electoral integrity and free speech. While these companies have the technical capabilities and scale necessary to address synthetic content at the platform level, their role as private arbiters of political truth raises important questions about democratic accountability and corporate power. Regulatory frameworks must carefully balance the need for content moderation with concerns about censorship and market concentration.

The development of this technological landscape will ultimately determine whether democratic societies can adapt to preserve electoral integrity while embracing the benefits of AI innovation. The choices made today regarding AI governance, platform regulation, and institutional reform will shape the future of democratic participation for generations to come. The stakes could not be higher: the very notion of truth in political discourse hangs in the balance. The defence of democratic truth will not rest in technology alone, but in whether citizens demand truth as a condition of their politics.

References and Further Information

Baker Institute for Public Policy, University of Tennessee, Knoxville. “Is the Electoral College the best way to elect a president?” Available at: baker.utk.edu

The American Presidency Project, University of California, Santa Barbara. “2024 Democratic Party Platform.” Available at: www.presidency.ucsb.edu

National Center for Biotechnology Information. “Social Media Effects: Hijacking Democracy and Civility in Civic Engagement.” Available at: pmc.ncbi.nlm.nih.gov

Brookings Institution. “Why Donald Trump won and Kamala Harris lost: An early analysis.” Available at: www.brookings.edu

Brookings Institution. “How tech platforms fuel U.S. political polarization and what government can do about it.” Available at: www.brookings.edu

Federal Trade Commission. “FTC's Endorsement Guides: What People Are Asking.” Available at: www.ftc.gov

Federal Register. “Negative Option Rule.” Available at: www.federalregister.gov

Marine Corps University Press. “The Singleton Paradox.” Available at: www.usmcu.edu

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0000-0002-0156-9795 Email: tim@smarterarticles.co.uk

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Lily Tsai, Ford Professor of Political Science, and Alex Pentland, Toshiba Professor of Media Arts and Sciences, are investigating how generative AI could facilitate more inclusive and effective democratic deliberation.

Their “Experiments on Generative AI and the Future of Digital Democracy” project challenges the predominant narrative of AI as democracy's enemy. Instead of focusing on disinformation and manipulation, they explore how machine learning systems might help citizens engage more meaningfully with complex policy issues, facilitate structured deliberation amongst diverse groups, and synthesise public input whilst preserving nuance and identifying genuine consensus.

The technical approach combines natural language processing with deliberative polling methodologies. AI systems analyse citizens' policy preferences, identify areas of agreement and disagreement, and generate discussion prompts designed to bridge divides. The technology can help participants understand the implications of complex policy proposals, facilitate structured conversations between people with different backgrounds and perspectives, and create synthesis documents that capture collective wisdom whilst preserving minority viewpoints.

Early experiments have yielded encouraging results. AI-facilitated deliberation sessions produce more substantive policy discussions than traditional town halls or online forums. Participants report better understanding of complex issues and greater satisfaction with the deliberative process. Most intriguingly, AI-mediated discussions seem to reduce polarisation rather than amplifying it—a finding that contradicts much of the conventional wisdom about technology's role in democratic discourse.

The implications extend far beyond academic research. Governments worldwide are experimenting with digital participation platforms, from Estonia's e-Residency programme to Taiwan's vTaiwan platform for crowdsourced legislation. The SERC research provides crucial insights into how these tools might be designed to enhance rather than diminish democratic values.

Yet the work also raises uncomfortable questions. If AI systems can facilitate better democratic deliberation, what happens to traditional political institutions? Should algorithmic systems play a role in aggregating citizen preferences or synthesising policy positions? The research suggests that the answer isn't a simple yes or no, but rather a more nuanced exploration of how human judgement and algorithmic capability can be combined effectively.

The Zurich Affair: When Research Ethics Collide with AI Capabilities

The promise of AI-enhanced democracy took a darker turn in early 2024 when researchers at the University of Zurich conducted a covert experiment that exposed the ethical fault lines in AI research. The incident, which SERC researchers have since studied as a cautionary tale, illustrates how rapidly advancing AI capabilities can outpace existing ethical frameworks.

The Zurich team deployed dozens of AI chatbots on Reddit's r/changemyview forum—a community dedicated to civil debate and perspective-sharing. The bots, powered by large language models, adopted personas including rape survivors, Black activists opposed to Black Lives Matter, and trauma counsellors. They engaged in thousands of conversations with real users who believed they were debating with fellow humans. The researchers used additional AI systems to analyse users' posting histories, extracting personal information to make their bot responses more persuasive.

The ethical violations were manifold. The researchers conducted human subjects research without informed consent, violated Reddit's terms of service, and potentially caused psychological harm to users who later discovered they had shared intimate details with artificial systems. Perhaps most troubling, they demonstrated how AI systems could be weaponised for large-scale social manipulation under the guise of legitimate research.

The incident sparked international outrage and forced a reckoning within the AI research community. Reddit's chief legal officer called the experiment “improper and highly unethical.” The researchers, who remain anonymous, withdrew their planned publication and faced formal warnings from their institution. The university subsequently announced stricter review processes for AI research involving human subjects.

The Zurich affair illustrates a broader challenge: existing research ethics frameworks, developed for earlier technologies, may be inadequate for AI systems that can convincingly impersonate humans at scale. Institutional review boards trained to evaluate survey research or laboratory experiments may lack the expertise to assess the ethical implications of deploying sophisticated AI systems in naturalistic settings.

SERC researchers have used the incident as a teaching moment, incorporating it into their ethics curriculum and policy discussions. The case highlights the urgent need for new ethical frameworks that can keep pace with rapidly advancing AI capabilities whilst preserving the values that make democratic discourse possible.

The Corporate Conscience: Industry Grapples with AI Ethics

The private sector's response to ethical AI challenges reflects the same tensions visible in academic and policy contexts, but with the added complexity of market pressures and competitive dynamics. Major technology companies have established AI ethics teams, published responsible AI principles, and invested heavily in bias detection and mitigation tools. Yet these efforts often feel like corporate virtue signalling rather than substantive change.

Google's 2024 update to its AI Principles exemplifies both the promise and limitations of industry self-regulation. The company's new framework emphasises “Bold Innovation” alongside “Responsible Development and Deployment”—a formulation that attempts to balance ethical considerations with competitive imperatives. The principles include commitments to avoid harmful bias, ensure privacy protection, and maintain human oversight of AI systems.

However, implementing these principles in practice proves challenging. Google's own research has documented significant biases in its image recognition systems, language models, and search algorithms. The company has invested millions in bias mitigation research, yet continues to face criticism for discriminatory outcomes in its AI products. The gap between principles and practice illustrates the difficulty of translating ethical commitments into operational reality.

More promising are efforts to integrate ethical considerations directly into technical development processes. IBM's AI Ethics Board reviews high-risk AI projects before deployment. Microsoft's Responsible AI programme includes mandatory training for engineers and product managers. Anthropic has built safety considerations into its language model architecture from the ground up.

These approaches recognise that ethical considerations cannot be addressed through post-hoc auditing or review processes. They must be embedded in design and development from the outset. This requires not just new policies and procedures, but cultural changes within technology companies that have historically prioritised speed and scale over careful consideration of societal impact.

The emergence of third-party AI auditing services represents another significant development. Companies like Anthropic, Hugging Face, and numerous startups are developing tools and services for evaluating AI system fairness, transparency, and reliability. This growing ecosystem suggests the potential for market-based solutions to ethical challenges—though questions remain about the effectiveness and consistency of different auditing approaches.

Measuring the Unmeasurable: The Fairness Paradox

One of SERC's most technically sophisticated research streams grapples with a fundamental challenge: how do you measure whether an AI system is behaving ethically? Traditional software testing focuses on functional correctness—does the system produce the expected output for given inputs? Ethical evaluation requires assessing whether systems behave fairly across different groups, respect human autonomy, and produce socially beneficial outcomes.

The challenge begins with defining fairness itself. Computer scientists have identified at least twenty different mathematical definitions of algorithmic fairness, many of which conflict with each other. A system might achieve demographic parity (equal positive outcomes across groups) whilst failing to satisfy equalised odds (equal true positive and false positive rates across groups). Alternatively, it might treat individuals fairly based on their personal characteristics whilst producing unequal group outcomes.

These aren't merely technical distinctions—they reflect fundamental philosophical disagreements about the nature of justice and equality. Should an AI system aim to correct for historical discrimination by producing equal outcomes across groups? Or should it ignore group membership entirely and focus on individual merit? Different fairness criteria embody different theories of justice, and these theories sometimes prove mathematically incompatible.

SERC researchers have developed sophisticated approaches to navigating these trade-offs. Rather than declaring one fairness criterion universally correct, they've created frameworks for stakeholders to make explicit choices about which values to prioritise. The kidney allocation research, for instance, allows medical professionals to adjust the relative weights of efficiency and equity based on their professional judgement and community values.

The technical implementation requires advanced methods from constrained optimisation and multi-objective machine learning. The researchers use techniques like Pareto optimisation to identify the set of solutions that represent optimal trade-offs between competing objectives. They've developed algorithms that can maintain fairness constraints whilst maximising predictive accuracy, though this often requires accepting some reduction in overall system performance.

Recent advances in interpretable machine learning offer additional tools for ethical evaluation. Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) can identify which factors drive algorithmic decisions, making it easier to detect bias and ensure systems rely on appropriate information. However, interpretability comes with trade-offs—more interpretable models may be less accurate, and some forms of explanation may not align with how humans actually understand complex decisions.

The measurement challenge extends beyond bias to encompass broader questions of AI system behaviour. How do you evaluate whether a recommendation system respects user autonomy? How do you measure whether an AI assistant is providing helpful rather than manipulative advice? These questions require not just technical metrics but normative frameworks for defining desirable AI behaviour.

The Green Code: Climate Justice and Computing Ethics

An emerging area of SERC research examines the environmental and climate justice implications of computing technologies—a connection that might seem tangential but reveals profound ethical dimensions of our digital infrastructure. The environmental costs of artificial intelligence, particularly the energy consumption associated with training large language models, have received increasing attention as AI systems have grown in scale and complexity.

Training GPT-3, for instance, consumed approximately 1,287 MWh of electricity—enough to power an average American home for over a century. The carbon footprint of training a single large language model can exceed that of five cars over their entire lifetimes. As AI systems become more powerful and pervasive, their environmental impact scales accordingly.

However, SERC researchers are exploring questions beyond mere energy consumption. Who bears the environmental costs of AI development and deployment? What are the implications of concentrating AI computing infrastructure in particular geographic regions? How might AI systems be designed to promote rather than undermine environmental justice?

The research reveals disturbing patterns of environmental inequality. Data centres and AI computing facilities are often located in communities with limited political power and economic resources. These communities bear the environmental costs—increased energy consumption, heat generation, and infrastructure burden—whilst receiving fewer of the benefits that AI systems provide to users elsewhere.

The climate justice analysis also extends to the global supply chains that enable AI development. The rare earth minerals required for AI hardware are often extracted in environmentally destructive ways that disproportionately affect indigenous communities and developing nations. The environmental costs of AI aren't just local—they're distributed across global networks of extraction, manufacturing, and consumption.

SERC researchers are developing frameworks for assessing and addressing these environmental justice implications. They're exploring how AI systems might be designed to minimise environmental impact whilst maximising social benefit. This includes research on energy-efficient algorithms, distributed computing approaches that reduce infrastructure concentration, and AI applications that directly support environmental sustainability.

The work connects to broader conversations about technology's role in addressing climate change. AI systems could help optimise energy grids, reduce transportation emissions, and improve resource efficiency across multiple sectors. However, realising these benefits requires deliberate design choices that prioritise environmental outcomes over pure technical performance.

Pedagogical Revolution: Teaching Ethics to the Algorithm Generation

SERC's influence extends beyond research to educational innovation that could reshape how the next generation of technologists thinks about their work. The programme has developed pedagogical materials that integrate ethical reasoning into computer science education at all levels, moving beyond traditional approaches that treat ethics as an optional add-on to technical training.

The “Ethics of Computing” course, jointly offered by MIT's philosophy and computer science departments, exemplifies this integrated approach. Students don't just learn about algorithmic bias in abstract terms—they implement bias detection algorithms whilst engaging with competing philosophical theories of fairness and justice. They study machine learning optimisation techniques alongside utilitarian and deontological ethical frameworks. They grapple with real-world case studies that illustrate how technical and ethical considerations intertwine in practice.

The course structure reflects SERC's core insight: ethical reasoning and technical competence aren't separate skills that can be taught in isolation. Instead, they're complementary capabilities that must be developed together. Students learn to recognise that every technical decision embodies ethical assumptions, and that effective ethical reasoning requires understanding technical possibilities and constraints.

The pedagogical innovation extends to case study development. SERC commissions peer-reviewed case studies that examine real-world ethical challenges in computing, making these materials freely available through open-access publishing. These cases provide concrete examples of how ethical considerations arise in practice and how different approaches to addressing them might succeed or fail.

One particularly compelling case study examines the development of COVID-19 contact tracing applications during the pandemic. Students analyse the technical requirements for effective contact tracing, the privacy implications of different implementation approaches, and the social and political factors that influenced public adoption. They grapple with trade-offs between public health benefits and individual privacy rights, learning to navigate complex ethical terrain that has no clear answers.

The educational approach has attracted attention from universities worldwide. Computer science programmes at Stanford, Carnegie Mellon, and the University of Washington have adopted similar integrated approaches to ethics education. Industry partners including Google, Microsoft, and IBM have expressed interest in hiring graduates with this combined technical and ethical training.

Regulatory Roulette: The Global Governance Puzzle

The international landscape of AI governance resembles a complex game of regulatory roulette, with different regions pursuing divergent approaches that reflect varying cultural values, economic priorities, and political systems. The European Union's AI Act, which entered force in 2024, represents the most comprehensive attempt to regulate artificial intelligence through legal frameworks. The Act categorises AI applications by risk level and imposes transparency, bias auditing, and human oversight requirements for high-risk systems.

The EU approach reflects European values of precaution and rights-based governance. High-risk AI systems—those used in recruitment, credit scoring, law enforcement, and other sensitive domains—face stringent requirements including conformity assessments, risk management systems, and human oversight provisions. The Act bans certain AI applications entirely, including social scoring systems and subliminal manipulation techniques.

Meanwhile, the United States has pursued a more fragmentary approach, relying on executive orders, agency guidance, and sector-specific regulations rather than comprehensive legislation. President Biden's October 2023 executive order on AI established safety and security standards for AI development, but implementation depends on individual agencies developing their own rules within existing regulatory frameworks.

The contrast reflects deeper philosophical differences about innovation and regulation. European approaches emphasise precautionary principles and fundamental rights, whilst American approaches prioritise innovation whilst addressing specific harms as they emerge. Both face the challenge of regulating technologies that evolve faster than regulatory processes can accommodate.

China has developed its own distinctive approach, combining permissive policies for AI development with strict controls on applications that might threaten social stability or party authority. The country's AI governance framework emphasises algorithmic transparency for recommendation systems whilst maintaining tight control over AI applications in sensitive domains like content moderation and social monitoring.

These different approaches create complex compliance challenges for global technology companies. An AI system that complies with U.S. standards might violate EU requirements, whilst conforming to Chinese regulations might conflict with both Western frameworks. The result is a fragmented global regulatory landscape that could balkanise AI development and deployment.

SERC researchers have studied these international dynamics extensively, examining how different regulatory approaches might influence AI innovation and deployment. Their research suggests that regulatory fragmentation could slow beneficial AI development whilst failing to address the most serious risks. However, they also identify opportunities for convergence around shared principles and best practices.

The Algorithmic Accountability Imperative

As AI systems become more sophisticated and widespread, questions of accountability become increasingly urgent. When an AI system makes a mistake—denying a loan application, recommending inappropriate medical treatment, or failing to detect fraudulent activity—who bears responsibility? The challenge of algorithmic accountability requires new legal frameworks, technical systems, and social norms that can assign responsibility fairly whilst preserving incentives for beneficial AI development.

SERC researchers have developed novel approaches to algorithmic accountability that combine technical and legal innovations. Their framework includes requirements for algorithmic auditing, explainable AI systems, and liability allocation mechanisms that ensure appropriate parties bear responsibility for AI system failures.

The technical components include advanced interpretability techniques that can trace algorithmic decisions back to their underlying data and model parameters. These systems can identify which factors drove particular decisions, making it possible to evaluate whether AI systems are relying on appropriate information and following intended decision-making processes.

The legal framework addresses questions of liability and responsibility when AI systems cause harm. Rather than blanket immunity for AI developers or strict liability for all AI-related harms, the SERC approach creates nuanced liability rules that consider factors like the foreseeability of harm, the adequacy of testing and validation, and the appropriateness of deployment contexts.

The social components include new institutions and processes for AI governance. The researchers propose algorithmic impact assessments similar to environmental impact statements, requiring developers to evaluate potential social consequences before deploying AI systems in sensitive domains. They also advocate for algorithmic auditing requirements that would mandate regular evaluation of AI system performance across different groups and contexts.

Future Trajectories: The Road Ahead

Looking towards the future, several trends seem likely to shape the evolution of ethical computing. The increasing sophistication of AI systems, particularly large language models and multimodal AI, will create new categories of ethical challenges that current frameworks may be ill-equipped to address. As AI systems become more capable of autonomous action and creative output, questions about accountability, ownership, and human agency become more pressing.

The development of artificial general intelligence—AI systems that match or exceed human cognitive abilities across multiple domains—could fundamentally alter the ethical landscape. Such systems might require entirely new approaches to safety, control, and alignment with human values. The timeline for AGI development remains uncertain, but the potential implications are profound enough to warrant serious preparation.

The global regulatory landscape will continue evolving, with the success or failure of different approaches influencing international norms and standards. The EU's AI Act will serve as a crucial test case for comprehensive AI regulation, whilst the U.S. approach will demonstrate whether more flexible, sector-specific governance can effectively address AI risks.

Technical developments in AI safety, interpretability, and alignment offer tools for addressing some ethical challenges whilst potentially creating others. Advances in privacy-preserving computation, federated learning, and differential privacy could enable beneficial AI applications whilst protecting individual privacy. However, these same techniques might also enable new forms of manipulation and control that are difficult to detect or prevent.

Perhaps most importantly, the integration of ethical reasoning into computing education and practice appears irreversible. The recognition that technical and ethical considerations cannot be separated has become widespread across industry, academia, and government. This represents a fundamental shift in how we think about technology development—one that could reshape the relationship between human values and technological capability.

The Decimal Point Denouement

Returning to that midnight phone call about decimal places, we can see how a seemingly technical question illuminated fundamental issues about power, fairness, and human dignity in an algorithmic age. The MIT researchers' decision to seek philosophical guidance on computational precision represents more than good practice—it exemplifies a new approach to technology development that refuses to treat technical and ethical considerations as separate concerns.

The decimal places question has since become a touchstone for discussions about algorithmic fairness and medical ethics. When precision becomes spurious—when computational accuracy exceeds meaningful distinction—continuing to use that precision for consequential decisions becomes not just pointless but actively harmful. The recognition that “the computers can calculate to sixteen decimal places” doesn't mean they should represents a crucial insight about the limits of quantification in ethical domains.

The solution implemented by the MIT team—stochastic tiebreaking for clinically equivalent cases—has been adopted by other organ allocation systems and is being studied for application in criminal justice, employment, and other domains where algorithmic decisions have profound human consequences. The approach embodies a form of algorithmic humility that acknowledges uncertainty rather than fabricating false precision.

The broader implications extend far beyond kidney allocation. In an age where algorithmic systems increasingly mediate human relationships, opportunities, and outcomes, the decimal places principle offers a crucial guideline: technical capability alone cannot justify consequential decisions. The fact that we can measure, compute, or optimise something doesn't mean we should base important choices on those measurements.

This principle challenges prevailing assumptions about data-driven decision-making and algorithmic efficiency. It suggests that sometimes the most ethical approach is admitting ignorance, embracing uncertainty, and preserving space for human judgement. In domains where stakes are high and differences are small, algorithmic humility may be more important than algorithmic precision.

The MIT SERC initiative has provided a model for how academic institutions can grapple seriously with technology's ethical implications. Through interdisciplinary collaboration, practical engagement with real-world problems, and integration of ethical reasoning into technical practice, SERC has demonstrated that ethical computing isn't just an abstract ideal but an achievable goal.

However, significant challenges remain. The pace of technological change continues to outstrip institutional adaptation. Market pressures often conflict with ethical considerations. Different stakeholders bring different values and priorities to these discussions, making consensus difficult to achieve. The global nature of technology development complicates efforts to establish consistent ethical standards.

Most fundamentally, the challenges of ethical computing reflect deeper questions about the kind of society we want to build and the role technology should play in human flourishing. These aren't questions that can be answered by technical experts alone—they require broad public engagement, democratic deliberation, and sustained commitment to values that transcend efficiency and optimisation.

In the end, the decimal places question that opened this exploration points toward a larger transformation in how we think about technology's role in society. We're moving from an era of “move fast and break things” to one of “move thoughtfully and build better.” This shift requires not just new algorithms and policies, but new ways of thinking about the relationship between human values and technological capability.

The stakes could not be higher. As computing systems become more powerful and pervasive, their ethical implications become more consequential. The choices we make about how to develop, deploy, and govern these systems will shape not just technological capabilities, but social structures, democratic institutions, and human flourishing for generations to come.

The MIT researchers who called in the middle of the night understood something profound: in an age of algorithmic decision-making, every technical choice is a moral choice. The question isn't whether we can build more powerful, more precise, more efficient systems—it's whether we have the wisdom to build systems that serve human flourishing rather than undermining it.

That wisdom begins with recognising that fourteen decimal places might be thirteen too many.

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References and Further Information

• MIT Social and Ethical Responsibilities of Computing: https://computing.mit.edu/cross-cutting/social-and-ethical-responsibilities-of-computing/
• MIT Ethics of Computing Research Symposium 2024: Complete proceedings and video presentations
• Bertsimas, D. et al. “Predictive Analytics for Fair and Efficient Kidney Transplant Allocation” (2024)
• Berinsky, A. & Péloquin-Skulski, G. “Effectiveness of AI Content Labelling on Democratic Discourse” (2024)
• Tsai, L. & Pentland, A. “Generative AI for Democratic Deliberation: Experimental Results” (2024)
• World Economic Forum AI Governance Alliance “Governance in the Age of Generative AI” (2024)
• European Union Artificial Intelligence Act (EU) 2024/1689
• Biden Administration Executive Order 14110 on Safe, Secure, and Trustworthy AI (2023)
• UNESCO Recommendation on the Ethics of Artificial Intelligence (2021)
• Brookings Institution “Algorithmic Bias Detection and Mitigation: Best Practices and Policies” (2024)
• Nature Communications “AI Governance in a Complex Regulatory Landscape” (2024)
• Science Magazine “Unethical AI Research on Reddit Under Fire” (2024)
• Harvard Gazette “Ethical Concerns Mount as AI Takes Bigger Decision-Making Role” (2024)
• MIT Technology Review “What's Next for AI Regulation in 2024” (2024)
• Colorado AI Act (2024) – First comprehensive U.S. state AI legislation
• California AI Transparency Act (2024) – Digital replica and deepfake regulations

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0000-0002-0156-9795 Email: tim@smarterarticles.co.uk

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