Category: English

Source: The Conversation – UK – By Elizabeth Peatfield, Senior Lecturer, Criminal Justice, Liverpool John Moores University

In Britain, whether cannabis is treated as medicine or a crime may depend less on medical need than on the ability to pay. In 2018, the UK government changed drug policy, allowing specialist doctors to prescribe cannabis-based medicinal products.

The decision was presented as a move towards evidence-based healthcare, recognising cannabis may have therapeutic value for health conditions such as chemotherapy-induced nausea and vomiting, spasticity in adults with multiple sclerosis and treatment-resistant epilepsy. Nearly seven years later, though, access remains highly restricted. According to NHS guidance, medical cannabis is tightly controlled and usually considered only when other treatments have failed.

In practice, NHS prescriptions remain rare, with most patients accessing cannabis privately at considerable cost. For many others, legal access is simply unavailable.

Cannabis is recognised as medicine, yet people using it therapeutically without a prescription can still face criminal sanctions. The problem is not simply legal inconsistency but structural inequality.

While medical cannabis is legal in principle, the route to obtaining a prescription is narrow. Clinical guidance remains cautious, many doctors are reluctant to prescribe it, and patients find it difficult to navigate the system.

Those unable to afford private treatment are often left with limited options: go without treatment, rely on less effective alternatives, or obtain cannabis illegally.

The result is effectively a two-tier system in which legality is shaped not just by medical need, but by economic means. Those who can pay privately may use cannabis lawfully. Those who cannot may risk criminalisation for materially similar behaviour.

The financial barriers are significant: private patients must pay for consultations, clinic registration fees, ongoing review appointments and the medication itself. Prescriptions can run to hundreds of pounds a month, with some products priced at around £8.99 per gram.

By comparison, street cannabis typically costs £150-£200 per ounce, making it significantly cheaper – making the illicit market not just more accessible for some, but economically rational.

People who can’t access legal cannabis may be pushed towards unregulated supply chains linked to organised criminal networks. Unlike prescribed products, cannabis bought illegally has no quality checks, no guaranteed strength and no doctor overseeing how you use it. There is no guarantee it is free from harmful synthetic additives.

This creates another policy contradiction. A system intended to regulate cannabis use may, through its own restrictions, be sustaining the illicit markets it was meant to displace.

The inequality can exist even within the same household. Two people may use the same cannabis product for similar health reasons, yet only one is legally protected because they can afford the consultation fees and prescription charges required to access private treatment. The other, unable to absorb those costs, risks criminal sanctions for similar behaviour.

Legal dilemma

People using cannabis to manage chronic pain, anxiety, trauma or other long-term conditions may already be navigating significant health difficulties. Relying on illegal supply introduces additional stress, uncertainty and the fear of criminalisation on top of an already difficult health situation.

For some, the experience of breaking the law while attempting to manage their wellbeing may itself contribute to feelings of stigma, anxiety and social exclusion. This matters because research consistently shows that drug use is often functional – a way of managing pain, stress or trauma – rather than purely recreational.

In this context, self-medication with cannabis may represent a response to unmet medical need rather than deviance. Yet the law rarely accounts for this complexity. Unauthorised possession remains a criminal offence regardless of intent.

Cannabis occupies an increasingly ambiguous position in British society. According to the Office for National Statistics, it remains the most commonly used illicit drug in England and Wales. At the same time, alcohol retains a firmly established cultural and legal status despite its association with addiction, violence and long-term health harms.

One substance remains criminalised despite growing medical recognition and comparatively lower levels of harm. The other is culturally embedded despite well-documented links to addiction, violence and early death.

NHS England recorded over 1 million alcohol-related hospital admissions in 2023-24, alongside more than 22,000 alcohol-related deaths. Cannabis, by comparison, is not associated with mortality at that scale.

None of this means cannabis is without risk. Heavy use can produce dependency and mental health complications for some users, although others report relief from chronic pain, anxiety and related conditions. But the current policy framework appears difficult to justify on the basis of relative harm alone.

The continued policing of low-level cannabis possession also carries a cost to the criminal justice system, consuming police and court capacity at a time of serious backlogs.

A coherent cannabis policy would need to align law, medical evidence and lived reality. At present, UK policy sends contradictory messages. Cannabis is simultaneously framed as both a medicine and a criminal substance, depending less on how it is used than on how it is accessed.

When the difference between a prescription and a criminal record is money, that tells you everything about who a drug law is really for.

Elizabeth Peatfield does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Cannabis: it’s medicine if you’re rich enough – a crime if you’re not – https://theconversation.com/cannabis-its-medicine-if-youre-rich-enough-a-crime-if-youre-not-282427

Source: The Conversation – UK – By Glyn White, Senior Lecturer in 20th-Century Literature and Culture, University of Salford

For many people over the age of 50, the first few bars of an old 1960s song will always prompt a smile. Bring Me Sunshine, adopted by comedians Morecambe and Wise as their theme tune in 1969, came to define the pair who sang it at the end of every show.

May 14 2026 marks the centenary of the birth of Eric Morecambe, “the funny one” to Ernie Wise’s straight man, together regarded as Britain’s pre-eminent comedy double act of the 1960s and 1970s. Recognition of that milestone is boosted by the rediscovery of a lost episode from the first BBC series of The Morecambe and Wise Show, from September 16 1968 – the corporation plans to broadcast it on his century birthday.

Morecambe was then 42 and at the beginning of a BBC run lasting until 1977 that would produce a series of Christmas specials on which the duo’s fame largely rests. Half a century later, these specials are still a feature of the BBC’s Christmas line-up. But it is difficult to convey how much of an institution Morecambe and Wise were in that three-channel 1970s world of television.

In 1999 Queen Elizabeth dedicated the statue of Morecambe on the promenade of his home town, whose name the young Eric Bartholemew adopted. There are more than ten books about the comedian, and the late Victoria Wood wrote and performed in Eric and Ernie (2011) about their early years. The hunt for missing shows that led to the recent discovery has found episodes as far afield as Sierra Leone.

An enduring double act

But why does the affection for Morecambe and Wise endure? As an academic with a specialism in TV comedy, I’m keen to explore Morecambe’s contribution to the continuing fascination with this double act.

The pair were brought together through talent shows and music-hall impresario Jack Hylton in the early 1940s. Initially Wise had the bigger name and the edge in song and dance. The double act had to be paused when they were old enough to be conscripted – Wise into the merchant navy, Morecambe as a miner in Accrington where he experienced a heart murmur, a shadow of what was ahead.

Post-war BBC Radio success led to an unhappy television debut, Running Wild (1954), but the duo were too good to be off screen for long. They became regulars on other series, until hired as the stars of ATV’s Two of a Kind (1961-68). The appearance of the Beatles on the show in 1963, right on the cusp of international stardom, illustrates these two sparky live performers’ fast, funny, irreverent signature style.

When John Lennon tries to make a point about his dad telling him about Morecambe and Wise when he was younger, indicating his height at the time, Eric responds “You’ve only got a little dad, have you?” and everyone dissolves into laughter, including Ernie.

It’s clear that while there was a script, delivered more or less successfully, there is also improvisation catching fellow performers on the hop. When Ernie sets up a joint rendition of Moonlight Bay, Eric storms in wearing a Beatles wig singing lines from their hits. He’s an aggressive, anarchic, decidedly northern spirit.

There was always a slapstick element to their comedy that takes real professionalism to make work, and to roll with it when things goes wrong. For this they drew on their music-hall roots and learned from film performers, too.

Writer Eddie Braben’s Morecambe and Wise Show sketches have them co-habiting like Laurel and Hardy and, like Oliver Hardy, Eric breaks the fourth wall, speaking to the audience directly, usually to highlight his awareness of how badly things are going. Their humour is quintessentially British.

Despite a real desire to break America, and numerous appearances on the hugely popular Ed Sullivan Show, they were not to be part of the “British invasion” of the 1960s. Instead they were regarded with real affection in a drab mid-century Britain seeking a bit of sunshine.

The great and the good flocked to appear on their Christmas specials in musical numbers and plays “wot Ern wrote”. It worked best when the guest brought some gravitas that could be undermined, as with stern classical actress Glenda Jackson, who revealed a real talent for comedy, and newsreader Angela Rippon, who revealed she had legs and an unsuspected ability to dance that is remembered today.

With male celebrities there is often an edge of rivalry. The best-known sketch of the Christmas Specials, from 1971, sees globally renowned conductor André Previn drawn in under false pretences by Ernie who has promised him the chance to work with violinist Yehudi Menuhin, setting up confusions over names. In the end Previn becomes Andrew Preview as Eric destroys Grieg’s Piano Concerto with “all the right notes just not necessarily in the right order”.

Previn managed to hold his own (and keep an admirably straight face) despite being unable to attend scheduled rehearsals, and it is noticeable how much Morecambe claps the other performers at the end. He feeds on live interaction that teeters breathlessly on the brink of collapse. This sketch is lightning in a bottle with Morecambe’s role as conductor of chaos.

But it took its toll. That early heart murmur would turn into near-fatal heart attacks in 1968 and 1979, and a fatal one in 1984, backstage after half a dozen curtain calls at a solo event. Morecambe was just 58, undoubtedly with more to give on many fronts, from comedy to writing and acting.

Watching the famous Breakfast Sketch (1976) used to irritate me, because to my younger self those pancakes weren’t going to be any good with all that eggshell in them, and the oranges weren’t cut into even halves.

But for me now, at 59, Eric and Ernie’s playfulness is clearly about the joy of being alive in the moment. The legacy of Eric Morecambe in entertainment terms is about delivering sunshine: having the ability to make such moments, and to produce them to order. There can never be enough of them.

Glyn White does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Eric Morecambe at 100: the beloved British comedian with an anarchic northern spirit – https://theconversation.com/eric-morecambe-at-100-the-beloved-british-comedian-with-an-anarchic-northern-spirit-282547

Source: The Conversation – UK – By Jason R.C. Nurse, Reader in Cyber Security, University of Kent

It’s hard to overstate the impact that artificial intelligence has had since the release of generative AI platforms such as ChatGPT just three years ago. While they have led to countless advances in how we live and work, they have also been at the centre of controversies around domestic and sexual abuse.

The use of the AI tool Grok to remove women’s clothing in images brought the issue of so-called technology-facilitated abuse to the fore. But it’s a problem that predates AI – with Bluetooth trackers, wearable devices, smart speakers, smart glasses and apps all used by abusers to control, harass or stalk their victims.

This abuse has worsened as tech has become more embedded in people’s lives, and as AI advances rapidly. But governments have struggled to make tech companies design systems that minimise misuse, and to hold them accountable when things go wrong.

Our own research has confirmed that technology misuse has increased and that its harms are significant. But governments and the tech sector are doing little to combat it – despite numerous examples of how tech can enable abuse.

Case 1: Smart glasses

The growing availability of smart glasses – which look like normal eyewear but can do many things a smartphone does – has led to reports of secret filming. In some cases, videos were posted online, often attracting degrading and sexually explicit comments.

Meta has said its smart glasses have a light to show when they are recording and anti-tamper tech to make sure the light cannot be covered. But there appear to be workarounds.

In England and Wales, voyeurism legislation focuses on private spaces, and harassment laws do not specifically apply to targeted recording and online distribution. However, the UK Information Commissioner’s Office is investigating Meta after subcontractors were allegedly able to access intimate footage from customers’ glasses. This is in addition to a lawsuit in the US, which alleges Meta violated privacy laws and engaged in false advertising. Meta has said that it takes the protection of data very seriously and that faces are usually blurred out. It also discloses in its UK terms of service the potential for content to be reviewed either by a human or by automation.

Case 2: Bluetooth trackers

Apple’s AirTags, and other devices built for tracking personal items, can be misused to stalk and harass people, particularly women. Apple released updates to AirTags and other trackable tech so that potential victims would be alerted if an unknown device was travelling with them. But for many, this feature should have existed from the outset.

The law in England and Wales is clear that attaching tracker devices to someone without their knowledge is a criminal offence. But despite convictions, the ease of covertly monitoring people using these devices means people continue to be at risk.

Case 3: AI deepfake and ‘nudification’ apps

Apps can now “nudify” people, while AI is increasingly used to make non-consensual deepfake pornography. In January, several instances of xAI’s assistant Grok being used to create sexualised photos of women and minors came to light. All it took to create the images were some simple prompts.

After criticism, xAI decided to limit this feature. But the safeguards appear to apply only to certain jurisdictions and certain users.

In February, the UK government announced legal changes similar to the Take It Down Act in the US, which will require tech platforms in the UK to remove non-consensual intimate images within 48 hours. Failure to do so will result in fines and services being blocked, and the law is likely to be implemented from summer.

Using automated technology known as “hash matching”, victims will only need to report an image once to have it removed from multiple platforms simultaneously. The same images would then be automatically deleted every time anyone attempted to reupload them. Nudification apps and using AI chatbots to create deepfake pornography will also become illegal in the UK.

But there is more to be done. Mitigating risks must be embedded at the design stage to prevent these images being created in the first place. The rise of romantic and sexual chatbots means this has become more urgent.

And beyond deepfakes and nudification, AI can also enable harassment at scale. This includes directly targeting someone with abusive content, or fake images or profiles that impersonate victims for so-called “sextortion” scams.

Challenges ahead

These issues must be prevented with robust guardrails built into these technologies. This is what prioritising user safety should look like, after all. But often, these guardrails have failed. Safety tools are only usually added after public pressure, not built into platforms from the start.

Governments have allowed regulation to fall behind fast-paced developments. Tech companies have grown quickly, but laws and enforcement have not kept up. At the same time, police and legal systems are often under-trained or unclear on how to handle digital harm.

Even where there is regulation, such as the UK’s Online Safety Act, penalties for platforms that allow abuse are often weak or unenforceable. The regulator Ofcom has issued only voluntary guidance to tech companies on how to better protect women and girls on their platforms. Campaigners have called for this to be made mandatory, with clear penalties for companies that do not comply, placing it on a level legal footing with child sexual abuse and terrorism content.

As AI advances, tech companies must prioritise system design that puts user safety first. But until governments enforce real consequences, the tech sector will be able to profit from harm while those using the platforms bear the cost.

Jason R.C. Nurse receives/received funding from The Engineering and Physical Sciences Research Council (EPSRC), The Research Institute for Sociotechnical Cyber Security, The National Cyber Security Centre (NCSC), and the UK Home Office. He is affiliated with Wolfson College, University of Oxford as a Research Member, CybSafe as the Director of Science and Research, and The Royal United Services Institute (RUSI) as an Associate Fellow.

Lisa Sugiura receives funding from Home Office Domestic Abuse Perpetrators Intervention Fund

– ref. From AirTags to AI nudification: the growing toolkit of technology-facilitated abuse – https://theconversation.com/from-airtags-to-ai-nudification-the-growing-toolkit-of-technology-facilitated-abuse-274468

Source: The Conversation – UK – By Richard Youngs, Professor of International and European Politics, University of Warwick

Compounding the alarm triggered by Russia’s 2022 full-scale invasion of Ukraine, the erratic unpredictability of the second Trump administration has made the need for European security autonomy obvious. On a number of occasions over the past year, Donald Trump has loosely intimated that he might leave the Nato defence alliance.

Washington’s recent move to withdraw 5,000 troops from Germany, plus unease over the US’s actions in Iran, have reinforced the imperative of European strategic independence. The US administration announced its planned withdrawal after the German chancellor, Friedrich Merz, criticised Trump’s Middle Eastern adventurism.

European rearmament is well underway. Governments still need to follow through on their promises to increase defence budgets to Nato’s new 5% of GDP target. But in 2025, European Nato members and Canada spent US$574 billion (£422 billion) on defence – an increase of nearly 20% on the previous year. This was the sharpest annual rise for 70 years.

The security debate should now move into a new phase in which European governments grasp the complex political implications of rearmament. These are gradually becoming apparent. Examples include a sharper trade-off between spending on defence and social programmes, and the prospect of Germany gaining military superiority as well as economic dominance.

There is also the danger of rightwing populist parties taking power with hugely increased military arsenals. Such parties are currently leading polls in France, Germany, the UK and several other countries, on agendas that sit uneasily with longstanding European security cooperation.

European militarisation adds to the eye-watering military build-up globally, which is increasing the risk of major conflict. There is also the harmful environmental impact of rearmament, and the threat of over-militarisation crowding out Europe’s focus on non-military security – an approach rooted in social development and conflict prevention.

These challenges show that rearmament represents a foundational shift for the European order. Simply grafting this defence build-up on to unreformed EU and Nato structures is likely to create new imbalances.

The EU risks losing its value as a peace project if it morphs into a security union without a more balanced and comprehensive political settlement.

Addressing the consequences

Concerns are rising in several European countries about the need to embed and constrain future German military power within a more deeply integrated EU. Calls for a “European army” are resurfacing, most recently by the Spanish government – but still without political precision.

Defence spending is growing not just through national governments, but EU-level instruments that entail deeper collective security. Many European governments are pushing towards Nordic-style, whole-of-society security in which military and civilian resources mobilise in unison. The EU’s Preparedness Union Strategy, introduced in 2025, is aimed at this too.

Such considerations show that a securitised Europe must be underpinned by continent-wide political debate and channels of accountablity. As citizens are asked to mobilise around full-spectrum defence, they need a greater say in security policies. They need a voice in the trade-offs that higher defence spending will require, and how to manage issues such as Germany’s incipient military predominance.

However, the process of rearmament is currently being carried out in a way that reinforces the opaque, crisis-mode features of EU decision-making that have nourished illiberal populist parties. Europe will struggle to legitimise its security turn without rivitalising its collective political system in ways that provide stronger and more active societal input.

European powers are currently seeking to act more assertively in defence of their immediate geopolitical interests. They are doing so while not entirely jettisoning the liberal-order principles of rules-based cooperation and openness.

But they are struggling to inject this combination with clear, precise content. European governments have not, together, defined a common position on how far European rearmament should be used to project sharper-edged power externally, in addition to dissuading aggression against European territory.

European security deployments and conflict prevention elsewhere in the world have retrenched in recent years. The withdrawal of EU military forces from Africa’s Sahel region is perhaps the most notable example. It is unclear whether the current security turn aims to reverse this trend, or move further in the same direction.

Rearmament also raises questions about the organisational structure of the European order. Security dynamics are altering power balances and the relationship between different regional bodies. They are dragging the UK back into European affairs, for example, and prompting talk of new, flexible forms of alliance across the continent.

Upgrading European burden-sharing and coordination within Nato is overdue. But the alliance is unlikely to suffice as a structural, ordering principle for post-Trump security autonomy. Other formats will be needed to allow greater thematic and geographic adaptability.

Discussions took place on defence and security matters at the European Political Community summit in Armenia on May 4. It involved not only EU member states but the UK and other non-EU European powers. Recent European coalition efforts covering Ukrainian security and navigation in the Strait of Hormuz may herald a trend towards functional and shifting clusters of states.

Security debates do not neatly match the EU’s economic and regulatory space – and this invites reflection on innovative formats. Excluded from EU security plans, the British government especially needs to be ready with proactive ideas that contribute to structural reordering, well beyond negotiations of the current EU-UK reset.

As the EU finalises its new security strategy and the UK moves forward with implementing its strategic defence review, European governments need to address the political ramifications of rearmament. These present harder, more structural challenges than hiking defence budgets – but currently, governments are pushing them down the road.

Until these challenges are resolved, European rearmament will rest on shaky foundations, and generate many difficulties in its wake.

Richard Youngs receives funding from several EU research projects.

– ref. Europe is rearming itself without addressing the political consequences – https://theconversation.com/europe-is-rearming-itself-without-addressing-the-political-consequences-282516

Source: The Conversation – UK – By Michael Mayowa Farayola, PhD Graduate, School of Computing, Dublin City University

If artificial intelligence (AI) systems shape decisions that affect people’s lives, they should do so fairly. This should be a given considering that potential applications for AI include automated hiring systems, as well as tools used in education, finance and criminal justice.

But ensuring the fairness of AI systems is far more complex than it might sound. Despite years of research, there is still no consensus on what fairness means, how it should be measured, or whether it can ever be fully achieved.

Fairness inherently depends on context. What counts as fair in one domain may be inappropriate or even harmful in another. In criminal justice, fairness may prioritise avoiding disproportionate harm to particular communities. In education, it may focus on equal opportunity and long-term outcomes.

In finance, it often involves balancing access to credit with risk assessment. Because AI systems must be formalised mathematically, researchers translate fairness into technical definitions expressed through metrics that specify how outcomes should be distributed across groups.

These metrics are useful tools, but they are not neutral. Each encodes assumptions about which differences matter and which trade-offs are acceptable.

Problems with the data

A deeper issue lies in the data itself. AI systems learn from historical datasets that reflect past decisions, institutional practices, and social inequalities. When a model is trained to replicate observed outcomes, such as hiring decisions or loan and mortgage approvals, it may reproduce existing injustices under the appearance of objectivity.

Optimising for one notion of fairness often means violating another. This tension is evident in automated loan approval systems. An algorithm may be designed so that applicants with the same predicted probability of default are treated similarly across demographic groups.

Yet one group may still be more likely to be incorrectly denied credit, while another may be more likely to receive loans they later struggle to repay. Fairness in predictive accuracy can therefore conflict with fairness in how financial risk and opportunity are distributed.

These differences often reflect structural inequalities embedded in the data the model is trained on. Groups that have historically faced barriers to credit, due to factors such as discrimination or exclusion from financial systems, may have thinner credit histories or lower recorded incomes.

As a result, models can treat socioeconomic disadvantage as a signal of higher risk, even when it does not reflect an individual’s actual ability to repay.

The same pattern emerges in hiring. If a company historically promoted fewer women into senior roles, a system trained to predict “successful” candidates may learn patterns that favour characteristics more common among men, even if gender is not explicitly included as an input. In both cases, the model does not invent bias, it inherits it.

A fundamental question is whether AI systems mirror the world as it was, or attempt to correct for known injustices.

The idea of fairness is further complicated by how it is assessed. Many assessments examine a single protected attribute, such as gender or race, in isolation. While common, this approach can obscure how discrimination operates in practice.

An automated hiring system might appear fair when comparing men and women overall, and fair when comparing ethnic groups overall, yet it might also consistently disadvantage older women from minority backgrounds.

Complex evaluation

People are defined by several characteristics that intersect, including age, ethnicity, disability, and socioeconomic background. Because these intersectional subgroups are often small and underrepresented in data, the harms they face may remain invisible in standard evaluations.

This invisibility has a direct technical consequence. When a subgroup is small, the model encounters too few examples to learn reliable patterns for that group and instead applies generalisations drawn from the broader categories it has seen more of, which may not reflect that group’s actual characteristics or circumstances.

Errors and biases affecting small subgroups are also less likely to surface in standard performance metrics, which aggregate results across all users and can therefore mask poor outcomes for minorities within minorities. Which means that those most at risk are therefore often the least visible.

These challenges suggest that fairness in AI cannot be reduced to better metrics or more sophisticated algorithms. Fairness is shaped by institutional context, historical legacies, and power relations.

Decisions about what data to collect, which objectives to optimise, and how systems are deployed are influenced by social and organisational factors. Technical fixes are necessary but insufficient. Meaningful approaches must engage with the broader context in which AI systems operate.

This includes involving interested parties beyond engineers and data scientists. People affected by AI systems, often members of marginalised communities, possess contextual knowledge about risks and harms that may not be visible from a purely technical perspective.

Participatory approaches, in which affected groups contribute to the design and governance of AI systems, acknowledge that fairness cannot be defined without considering those who bear the consequences of automated decisions.

Even when interventions appear successful, they may not remain so. Societies change, demographics shift and language evolves. A system that performs acceptably today may produce unfair outcomes tomorrow. In particular, recent advances in large language models, the technology underlying many widely used AI tools, add further complexity.

Unlike traditional systems that make specific predictions, these models generate language based on vast collections of historical text. Such datasets inevitably contain stereotypes and imbalances.

Fairness is therefore not a one-time achievement but an ongoing responsibility requiring monitoring, accountability, and a willingness to revise or withdraw systems when harms emerge.

Together, these challenges suggest that fairness in AI is not a purely technical problem awaiting a finite solution. It is a moving target shaped by social values and historical context.

Rather than asking whether an AI system is fair in the abstract, a more productive question may be: fair according to whom, under what conditions, and with what forms of accountability? How we answer that question will shape not only the systems we build, but the kind of society they help to create.

Michael Mayowa Farayola receives funding from Taighde Éireann Research Ireland grants 13/RC/2094_P2 (Lero) and 13/RC/2106_P2 (ADAPT) and is co-funded under the European Regional Development Fund (ERDF).

– ref. AI doesn’t create bias, it inherits it – how do we ensure fairness when it comes to automated decisions? – https://theconversation.com/ai-doesnt-create-bias-it-inherits-it-how-do-we-ensure-fairness-when-it-comes-to-automated-decisions-280927