In a new analysis, IMF economists examined AI’s potential impact on the global labour market. While many studies foresee jobs being automated by AI, the technology will often complement human work instead. The IMF analysis weighs up both scenarios.  

The findings are striking: almost 40 percent of jobs globally are susceptible to automation or augmentation by AI.

Historically, new technologies have tended to affect routine tasks—but AI can also impact high-skilled roles. As a result, advanced economies face greater risks from AI but also stand to gain more of its benefits versus emerging markets.

Per the IMF’s research, about 60 percent of jobs in advanced economies may be impacted by AI. Around half of those jobs could benefit from AI integration, enhancing productivity. For the remainder, AI may execute key human tasks, lowering labour demand, wages, and hiring. In some cases, human jobs could disappear entirely.

In emerging and developing economies, IMF economists predict AI exposure of 40 percent and 26 percent respectively. This suggests fewer immediate AI disruptions than advanced economies. However, many emerging markets lack the infrastructure and skills to harness AI’s benefits. Over time, this could worsen inequality between countries. 

The IMF warns AI may also drive inequality within countries. Workers able to exploit AI may become more productive and boost wages, while those who cannot fall behind.

Research shows that AI can accelerate the productivity of less experienced staff. Younger workers could therefore benefit more from AI opportunities whereas older workers may struggle adapting.  

Advanced economies are better prepared for AI adoption but must still prioritise innovation, integration, and regulation to cultivate its safe and responsible use. For emerging markets, the priority is developing digital infrastructure and skills.

To assist countries in crafting effective policies, the IMF has introduced an AI Preparedness Index—evaluating readiness in areas such as digital infrastructure, human capital, innovation, and regulation. Wealthier economies – including Singapore, the US, and Denmark – have shown higher preparedness for AI adoption.

The AI era has arrived, and proactive measures are crucial to ensuring its benefits are shared prosperity for all.

(Photo by Levi Meir Clancy on Unsplash)

See also: McAfee unveils AI-powered deepfake audio detection

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Racial inequality continues to be rampant in healthcare. Examining the fallout of COVID-19 serves as yet another example of the disparity between ethnicities.

In England and Wales, males of Black African ethnic background had the highest rate of death involving COVID-19, 2.7 times higher than males of a White ethnic background. Females of Black Caribbean ethnic background had the highest rate, 2.0 times higher than females of White ethnic background. All ethnic minority groups other than Chinese had a higher rate than the White ethnic population for both males and females.

Such disparities are sadly common across many conditions that can reduce life enjoyment, limit opportunities, and even lead to premature death. AI could be a powerful aid in tackling the problem, if thoroughly tested and implemented responsibly.

“As the first health and social care secretary from an ethnic minority background, I care deeply about tackling the disparities which exist within the healthcare system,” explained Javid, speaking to The Guardian.

Among the projects given the green light by Javid include the creation of new standards for health inclusivity to improve the representation of ethnic minorities in datasets used by the NHS.

“If we only train our AI using mostly data from white patients it cannot help our population as a whole,” added Javid. “We need to make sure the data we collect is representative of our nation.”

A recent analysis found a significant disparity in performance when using computer screening to detect diabetic retinopathy in patients from ethnic minority communities due to different levels of retinal pigmentation. One project will attempt to address this disparity.

Among the devastating statistics affecting minority communities is that black women are five times more likely to die from complications during pregnancy than white women. One project will use algorithms to investigate the factors and recommend changes – including potentially new training for nurses and midwives – that will hopefully ensure that everyone has the best possible chance to live a healthy life with their child.

The development of an AI-powered chatbot also hopes to raise the uptake of screening for STIs/HIV among minority ethnic communities.

The drive will be led by NHSX. A report in 2017 by PwC found that just 39 percent of the UK public would be willing to engage with AI for healthcare. However, research (PDF) by KPMG found that – despite an overall unwillingness from the British public to share their data with the country’s biggest organisations even if it improved service – the NHS came out on top with 56 percent willing to do so.

If the UK Government wants to use AI as part of its “level up” plans, it will need to tread carefully with a sceptical public and prove its benefits while avoiding the kind of devastating missteps that have cost thousands of lives and defined Johnson’s premiership so far.

(Image Credit: UK Parliament under CC BY 3.0 license)

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The research was published by the Royal Statistical Society but conducted by Glass AI, a startup which uses artificial intelligence to analyse every UK website.

In a blog post, the company explained its unique approach:

“Previous related studies created for economists, policy-makers, or business analysts have tended to underuse or even ignore the web as a data source, typically only looking in any detail at a limited number of sectors of the economy, examining a small slice of geography or conducting manual (and expensive) surveys.

Worse, given a small data set, data scientists have no choice but to extrapolate and rely on small sample statistics.“

Across the entire ‘.uk’ domain, Glass AI read the genders of 2.3 million people and the positions they held in 150,000 organisations spanning 108 industry sectors.

Gender gaps were found across industries with men far more likely to be in leadership positions.

82 percent of all CEOs, 92 percent of chairpersons, and 73 percent of directors are male.

Meanwhile, support roles are dominated by women. 95 percent of receptionists, legal secretaries, and care assistants are female.

Both of the core genders are relatively equal when it comes to participating in the workforce, but the gap presents itself in the roles they attain.

Glass AI’s findings matched those of the Office for National Statistics which shows promise for the use of artificial intelligence to conduct research far quicker and with fewer resources that matches or exceeds traditional means.

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Amnesty International and Access Now prepared the ‘Toronto Declaration’ (PDF) that’s also been signed by Human Rights Watch and the Wikimedia Foundation. As an open declaration; other companies, governments, and organisations are being called on to add their endorsement.

In a post, Access Now wrote:

“As machine learning systems advance in capability and increase in use, we must examine the positive and negative implications of these technologies.

We acknowledge the potential for these technologies to be used for good and to promote human rights, but also the potential to intentionally or inadvertently discriminate against individuals or groups of people.

We must keep our focus on how these technologies will affect individual human beings and human rights. In a world of machine learning systems, who will bear accountability for harming human rights?”

Ethics have become a major talking point in the AI industry. However, much of the conversation so far has focused on drawing red lines when it comes to surveillance and military applications.

There’s a big debate over AIs potential impact to jobs. Some believe automation will cause a work shortage, while others argue that most will simply be enhanced by AI.

If jobs are being replaced, ideas like a universal income will have to be re-examined. If jobs are being enhanced, ensuring AI is indiscriminate will be even more important.

AI has already shown discrimination

Technologies developed and used in the West are typically developed by white males.

Research has been performed into the gender and race gap of executives in Silicon Valley. This data at least provides some indication of the representation problem:

What this means is that, unintentionally, products often perform better for this particular group. Today, that could just mean something relatively trivial like Siri recognising an American male voice with greater accuracy (even as a British male, Silicon Valley-developed products often struggle with my accent!)

A 2010 study by researchers at NIST and the University of Texas in Dallas found that algorithms designed and tested in East Asia are better at recognising East Asians, while those designed in western countries are more accurate at detecting Caucasians.

However, if jobs are becoming more reliant on AI, they need to work as well for everyone who uses them. Failing to do so will put certain groups at a greater advantage than others.

“From policing, to welfare systems, online discourse, and healthcare – to name a few examples – systems employing machine learning technologies can vastly and rapidly change or reinforce power structures or inequalities on an unprecedented scale and with significant harm to human rights,” wrote Access Now.

Policing is one area of particular concern. An investigative report by ProPublica revealed that computer-generated ‘risk assessment scores’ used to determine eligibility for parole are almost twice as likely to label black defendants as potential repeat offenders, despite evidence to the contrary.

Similarly, a 2012 study (paywall) by the IEEE  found that police surveillance cameras using facial recognition to identify suspected criminals are five to 10 percent less accurate when identifying African Americans – which could lead to more innocent black people being arrested.

Machine learning models for AI are often trained on public data and therefore we must be careful about what sources are used. Microsoft’s attempt to create a chatbot which learns from the public, Tay, infamously ended up becoming a rather unsavoury character spouting racist and sexist remarks.

The declaration signed today is a great start to keep these issues in mind as AI technologies are being developed, but it will require tackling inequalities across the whole of society to make developments truly representative of those it serves.

What are your thoughts on the AI discrimination issue? Let us know in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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