Data from Dealroom was used for the State of European Tech report. Dealroom defines deep tech as 16 fields: Artificial Intelligence, Machine Learning, Big Data, Augmented Reality, Virtual Reality, Drones, Autonomous Driving, Blockchain, Nanotech, Robotics, Internet of Things, 3D Technology, Computer Vision, Connected Devices, Sensors Technology, and Recognition Technology (NLP, image, video, text, speech recognition).

In 2019, there was $10.2 billion capital invested in European deep tech. In 2020, that dropped to $8.9 billion:

I think it’s fair to say that 2020 has been a tough year for most people and businesses. Economic uncertainty – not just from COVID-19 but also trade wars, Brexit, and a rather tumultuous US presidential election – has naturally led to fewer investments and people tightening their wallets.

For just one example, innovative satellite firm OneWeb was forced to declare bankruptcy earlier this year after crucial funding it was close to securing was pulled during the peak of the pandemic. Fortunately, OneWeb was saved following an acquisition by the UK government and Bharti Global—but not all companies have been so fortunate.

Many European businesses will now be watching the close-to-collapse Brexit talks with hope that a deal can yet be salvaged to limit the shock to supply lines, prevent disruption to Europe’s leading financial hub, and help to build a friendly relationship going forward with a continued exchange of ideas and talent rather than years of bitterness and resentment.

The report shows the UK has retained its significant lead in European tech investment and startups this year:

Despite the uncertainties, the UK looks unlikely to lose its position as the hub of European technology anytime soon.

Investments in European tech as a whole should bounce back – along with the rest of the world – in 2021, with promising COVID-19 vaccines rolling out and hopefully some calm in geopolitics.

94 percent of survey respondents for the report stated they have either increased or maintained their appetite to invest in the European venture asset class. Furthermore, a record number of US institutions have participated in more than one investment round in Europe this year—up 36% since 2016.

You can find a full copy of the State of European Tech report here.

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The analysis was commissioned by the UK government in October 2018 and will receive a formal response.

Algorithms bring substantial benefits to businesses and individuals able to use them effectively. However, increasing evidence suggests biases are – often unconsciously – making their way into algorithms and creating an uneven playing field.

The CDEI is the UK government’s advisory body on the responsible use of AI and data-driven technology. CDEI has spent the past two years examining the issue of algorithmic bias and how it can be tackled.

Adrian Weller, Board Member for the Centre for Data Ethics and Innovation, said:

“It is vital that we work hard now to get this right as adoption of algorithmic decision-making increases. Government, regulators, and industry need to work together with interdisciplinary experts, stakeholders, and the public to ensure that algorithms are used to promote fairness, not undermine it.

The Centre for Data Ethics and Innovation has today set out a range of measures to help the UK to achieve this, with a focus on enhancing transparency and accountability in decision-making processes that have a significant impact on individuals.

Not only does the report propose a roadmap to tackle the risks, but it highlights the opportunity that good use of data presents to address historical unfairness and avoid new biases in key areas of life.”

The report focuses on four key sectors where algorithmic bias poses the biggest risk: policing, recruitment, financial services, and local government.

Today’s facial recognition algorithms are relatively effective when used on white males, but research has consistently shown how ineffective they are with darker skin colours and females. The error rate is, therefore, higher when facial recognition algorithms are used on some parts of society over others.

In June, Detroit Police chief Editor Craig said facial recognition would misidentify someone around 96 percent of the time—not particularly comforting when they’re being used to perform mass surveillance of protests.

Craig’s comments were made just days after the ACLU (American Civil Liberties Union) lodged a complaint against Detroit Police following the harrowing wrongful arrest of black male Robert Williams due to a facial recognition error.

And that’s just one example of where AI can unfairly impact some parts of society over another.

“Fairness is a highly prized human value,” the report’s preface reads. “Societies in which individuals can flourish need to be held together by practices and institutions that are regarded as fair.”

Ensuring fairness in algorithmic decision-making

Transparency is required for algorithms. In financial services, a business loan or mortgage could be rejected without transparency simply because a person was born in a poor neighbourhood. Job applications could be rejected not on a person’s actual skill but dependent on where they were educated.

Such biases exist in humans and our institutions today, but automating them at scale is a recipe for disaster. Removing bias from algorithms is not an easy task but if achieved would lead to increased fairness by taking human biases out of the equation.

“It is well established that there is a risk that algorithmic systems can lead to biased decisions, with perhaps the largest underlying cause being the encoding of existing human biases into algorithmic systems. But the evidence is far less clear on whether algorithmic decision-making tools carry more or less risk of bias than previous human decision-making processes. Indeed, there are reasons to think that better use of data can have a role in making decisions fairer, if done with appropriate care.

When changing processes that make life-affecting decisions about individuals we should always proceed with caution. It is important to recognise that algorithms cannot do everything. There are some aspects of decision-making where human judgement, including the ability to be sensitive and flexible to the unique circumstances of an individual, will remain crucial.”

The report’s authors examined the aforementioned four key sectors to determine their current “maturity levels” in algorithmic decision-making.

In recruitment, the authors found rapid growth in the use of algorithms to make decisions at all stages. They note that adequate data is being collected to monitor outcomes but found that understanding of how to avoid human biases creeping in is lacking.

“More guidance is needed on how to ensure that these tools do not unintentionally discriminate against groups of people, particularly when trained on historic or current employment data.”

The financial services industry has relied on data to make decisions for longer than arguably any other to determine things like how likely it is an individual can repay a debt.

“Specific groups are historically underrepresented in the financial system, and there is a risk that these historic biases could be entrenched further through algorithmic systems.”

CDEI found limited use of algorithmic decision-making in UK policing but found variance across forces with regards to both usage and managing ethical risks.

“The use of data analytics tools in policing carries significant risk. Without sufficient care, processes can lead to Review into bias in algorithmic decision-making: Executive summary Centre for Data Ethics and Innovation 8 outcomes that are biased against particular groups, or systematically unfair.

In many scenarios where these tools are helpful, there is still an important balance to be struck between automated decision-making and the application of professional judgement and discretion.”

Finally, in local government, CDEI noted an increased use of algorithms to inform decision-making but most are in their early stages of deployment. Such tools can be powerful assets for societal good – like helping to plan where resources should be allocated to maintain vital services – but can also carry significant risks.

“Evidence has shown that certain people are more likely to be overrepresented in data held by local authorities and this can then lead to biases in predictions and interventions.”

The CDEI makes a number of recommendations in its report but among them is:

• Clear and mandatory transparency over how algorithms are used for public decision-making and steps taken to ensure the fair treatment of individuals.
• Full accountability for organisations implementing such technologies.
• Improving the diversity of roles involved with developing and deploying decision-making tools.
• Updating model contracts and framework agreements for public sector procurement to incorporate minimum standards around the ethical use of AI.
• The government working with regulators to provide clear guidance on the collection and use of protected characteristic data in outcome monitoring and decision-making processes. They should then encourage the use of that guidance and data to address current and historic bias in key sectors.
• Ensuring that the Equality and Human Rights Commission has sufficient resources to investigate cases of alleged algorithmic discrimination.

CDEI is overseen by an independent board which is made up of experts from across industry, civil society, academia, and government; it is an advisory body and does not directly set policies. The Department for Digital, Culture, Media & Sport is consulting on whether a statutory status would help the CDEI to deliver its remit as part of the National Data Strategy.

You can find a full copy of the CDEI’s report into tackling algorithmic bias here (PDF)

(Photo by Matt Duncan on Unsplash)

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Cambridge Analytica was accused in March 2018 of using AI tools and big data to influence the results of the Brexit referendum and the US presidential election. Most objective observers probably felt the case was overblown, but it’s taken until now to be confirmed.

“From my review of the materials recovered by the investigation I have found no further evidence to change my earlier view that CA [Cambridge Analytica] was not involved in the EU referendum campaign in the UK,” wrote Information Commissioner Elizabeth Denham.

Cambridge Analytica did obtain a ton of user data—but through predominantly commercial means, and of mostly US voters. Such data is available to, and has also been purchased by, other electoral campaigns for targeted advertising purposes (the Remain campaigns in the UK actually outspent their Leave counterparts by £6 million.)

“CA were purchasing significant volumes of commercially available personal data (at one estimate over 130 billion data points), in the main about millions of US voters, to combine it with the Facebook derived insight information they had obtained from an academic at Cambridge University, Dr Aleksandr Kogan, and elsewhere,” wrote Denham.

The only real scandal was Facebook’s poor protection of users which allowed third-party apps to scrape their data—for which it was fined £500,000 by the UK’s data protection watchdog.

It seems the claims Cambridge Analytica used powerful AI tools were also rather overblown, with the information commissioner saying all they found were models “built from ‘off the shelf’ analytical tools”.

The information commissioner even found evidence that Cambridge Analytica’s own staff “were concerned about some of the public statements the leadership of the company were making about their impact and influence.”

Cambridge Analytica appears to have been a victim of those unable to accept democratic results combined with its own boasting of capabilities that weren’t actually that impressive.

You can read the full report here (PDF)

(Photo by Christian Lue on Unsplash)

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AI is helping with COVID-19 research around the world and much of it is being powered by NVIDIA GPUs. It’s a daunting task, new drugs often cost over $2.5 billion in research and development — doubling every nine years — and 90 percent of efforts fail.

Nvidia wants to help speed up discoveries of vital medicines while reducing costs

“COVID-19 hits home this urgency [for new tools],” Huang says.

Huang announced NVIDIA Clara Discovery—a suite of tools for assisting scientists in discovering lifesaving new drugs.

NVIDIA Clara combines imaging, radiology, and genomics to help develop healthcare AI applications. Pre-trained AI models and application-specific frameworks help researchers to find targets, build compounds, and develop responses.

Dr Hal Barron, Chief Scientific Officer and President of R&D at GSK, commented:

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise.

NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients.

Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the UK’s outstanding scientists.”

Researchers can now use biomedical-specific language models for their work, thanks to a breakthrough in natural language processing. This means researchers can organise and activate large datasets, research literature, and sort through papers or patents on existing treatments and other vital real-world data.

“Where there are popular industry tools, our computer scientists accelerate them,” Huang said. “Where no tools exist, we develop them—like NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT, NVIDIA RAPIDS.”

We’re all hoping COVID-19 research – using such powerful new tools available to scientists – can lead to a vaccine within a year or two, when they have often taken a decade or longer to create.

“The use of big data, supercomputing, and artificial intelligence has the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” commented Editor Weatherall, Ph.D., Head of Data Science and AI at AstraZeneca.

During his keynote, Huang provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer – which will be used to further healthcare research – the Cambridge-1.

NVIDIA has established partnerships with companies leading the fight against COVID-19 and other viruses including AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. These partners can harness Cambridge-1 for their vital research.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

And, for organisations wanting to set up their own AI supercomputers, NVIDIA has announced DGX SuperPODs as the world’s first turnkey AI infrastructure. The solution was developed from years of research for NVIDIA’s own work in healthcare, automotive, healthcare, conversational AI, recommender systems, data science and computer graphics.

While Huang has a nice kitchen, I’m sure he’d like to be back on the big stage for his GTC 2021 keynote. We’d certainly all love COVID-19 to be well and truly in the rear-view mirror.

(Photo by Elwin de Witte on Unsplash)

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NVIDIA is investing heavily in the UK’s accelerating AI sector. The company announced its acquisition of legendary semiconductor giant Arm for $40 billion back in September along with the promise to open a new AI centre in Cambridge.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named,” Huang said at the time. “We want to propel Arm – and the UK – to global AI leadership.”

NVIDIA promises to advance Arm’s platform in three major ways:

• NVIDIA will complement Arm partners with GPU, networking, storage and security technologies to create complete accelerated platforms.
• NVIDIA will work with Arm partners to create platforms for HPC, cloud, edge and PC — this requires chips, systems, and system software.
• NVIDIA will port the NVIDIA AI and NVIDIA RTX engines to Arm.

“Today, these capabilities are available only on x86,” Huang said, “With this initiative, Arm platforms will also be leading-edge at accelerated and AI computing.”

Huang also provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer, the Cambridge-1.

Cambridge-1 will boast 400 petaflops of AI performance and will be used by NVIDIA for its vast AI and healthcare collaborations in the UK across academia, industry, and startups.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

The company’s first partners are AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. A partnership with GSK will also see the world’s first AI drug discovery lab built in London.

“Because of the massive size of the datasets we use for drug discovery, we need to push the boundaries of hardware and develop new machine learning software,” commented Dr Kim Branson, senior vice president and global head of AI and ML at GSK.

“We’re building new algorithms and approaches in addition to bringing together the best minds at the intersection of medicine, genetics and artificial intelligence in the UK’s rich ecosystem. This new partnership with NVIDIA will also contribute additional computational power and state-of-the-art AI technology.”

While there were some natural concerns that Arm’s acquisition would see operations move from the UK to the US, NVIDIA clearly wants to build up its operations in what’s quickly becoming Europe’s AI epicentre.

(Photo by A Perry on Unsplash)

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British chip designer ARM’s technology is at the heart of most mobile devices. Meanwhile, Nvidia’s GPUs are increasingly being used for AI computation in servers, desktops, and even things like self-driving vehicles.

However, Nvidia was most interested in ARM’s presence in edge devices—which it estimates to be in the region of 180 billion.

Jensen Huang, CEO of Nvidia, said:

“ARM is an incredible company and it employs some of the greatest engineering minds in the world. But we believe we can make ARM even more incredible and take it to even higher levels.

We want to propel it — and the UK — to global AI leadership.”

There were concerns Nvidia’s acquisition would lead to job losses, but the company has promised to keep the business in the UK. The company says it’s planning to hire more staff and retain ARM’s iconic brand.

Nvidia is going further in its commitment to the UK by opening a new AI centre in Cambridge, which is home to an increasing number of exciting startups in the field such as FiveAI, Prowler.io, Fetch.ai, and Darktrace.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named.

Here, leading scientists, engineers and researchers from the UK and around the world will come to develop their ideas, collaborate and conduct their ground-breaking work in areas like healthcare, life sciences, self-driving cars, and other fields.”

The new centre will have five key features when it opens:

• ARM/Nvidia-based supercomputer – set to be one of the most powerful AI supercomputers in the world.
• Research Fellowships and Partnerships – Nvidia will use the centre to establish new UK-based research partnerships, expanding on successful relationships already established with King’s College and Oxford.
• AI Training – Nvidia will make its AI curriculum available across the UK to help create job opportunities and prepare “the next generation of UK developers for AI leadership”
• Startup Accelerator – With so many of the world’s most exciting AI companies launching in the UK, the Nvidia Inception accelerator will help startups succeed by providing access to the aforementioned supercomputer, connections to researchers from NVIDIA and partners, technical training, and marketing promotion.
• Industry Collaboration – AI is still in its infancy but will impact every industry to some extent. Nvidia says its new research facility will be an open hub for industry collaboration, building on the company’s existing relationships with the likes of GSK, Oxford Nanopore, and other leaders in their fields.

The UK is Europe’s leader in AI and the British government is investing heavily in ensuring it maintains its pole position. Beyond funding, the UK is also aiming to ensure it’s among the best places to run an AI company.

Current EU rules, especially around data, are often seen as limiting the development of European AI companies when compared to elsewhere in the world. While the UK will have to avoid being accused of doing a so-called “bonfire of regulations” post-Brexit, data collection regulations is likely an area which will be relaxed.

In the UK’s historic trade deal signed with Japan last week, several enhancements were made over the blanket EU-Japan deal signed earlier this year. Among the perceived improvements is the “free flow of data” by not enforcing localisation requirements, and that algorithms can remain private.

UK trade secretary Liz Truss said: “The agreement we have negotiated – in record time and in challenging circumstances – goes far beyond the existing EU deal, as it secures new wins for British businesses in our great manufacturing, food and drink, and tech industries.”

Japan and the UK, as two global tech giants, are expected to deepen their collaboration in the coming years—building on the trade deal signed last week.

Shigeki Ishizuka, Chairman of the Japan Electronics and Information Technology Industries Association, said: “We are confident that this mutual relationship will be further strengthened as an ambitious agreement that will contribute to the promotion of cooperation in research and development, the promotion of innovation, and the further expansion of inter-company collaboration.”

Nvidia’s investment shows that it has confidence in the UK’s strong AI foundations continuing to gain momentum in the coming years.

(Photo by A Perry on Unsplash)

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The research, titled AI Skills in the UK, shines a spotlight on some concerning issues.

For its UK report, Microsoft used data from a global AI skills study featuring more than 12,000 people in 20 countries to see how the UK is doing in comparison to the rest of the world.

Most notably, compared to the rest of the world, the UK is seeing a higher failure rate for AI projects. 29 percent of AI ventures launched by UK businesses have generated no commercial value compared to the 19 percent average elsewhere in the world.

35 percent of British business leaders foresee an AI skills gap within two years, while 28 percent believe there already is one (above the global average of 24%).

However, it seems UK businesses aren’t helping to prepare employees with the skills they need. Just 17 percent of British employees have been part of AI reskilling efforts (compared to the global figure of 38 percent.)

Agata Nowakowska, AVP EMEA at Skillsoft, said:

“UK employers will have to address the growing digital skills gap within the workforce to ensure their business is able to fully leverage every digital transformation investment that’s made. With technologies like AI and cloud becoming as commonplace as word processing or email in the workplace, firms will need to ensure employees can use such tools and aren’t apprehensive about using them.

Organisations will need to think holistically about managing reskilling, upskilling and job transitioning. As the war for talent intensifies, employee development and talent pooling will become increasingly vital to building a modern workforce that’s adaptable and flexible. Addressing and easing workplace role transitions will require new training models and approaches that include on-the-job training and opportunities that support and signpost workers to opportunities to upgrade their skills.” 

Currently, a mere 32 percent of British employees feel their workplace is doing enough to prepare them for an AI-enabled future (compared to the global average of 42%)

“The most successful organisations will be the ones that transform both technically and culturally, equipping their people with the skills and knowledge to become the best competitive asset they have,” comments Simon Lambert, Chief Learning Officer for Microsoft UK.

“Human ingenuity is what will make the difference – AI technology alone will not be enough.”

AI brain drain

It’s well-documented that the UK suffers from a “brain drain” problem. The country’s renowned universities – like Oxford and Cambridge – produce globally desirable AI talent, but they’re often swooped up by Silicon Valley giants who are willing to pay much higher salaries than many British firms.

In one example, a senior professor from Imperial College London couldn’t understand why one of her students was not turning up to any classes. Most people wouldn’t pay £9,250 per year in tuition fees and not turn up. The professor called her student to find out why he’d completed three years but wasn’t turning up for his final year. She found that he was offered a six-figure salary at Apple. 

This problem also applies to teachers who are needed to pass their knowledge onto the future generations. Many are lured away from academia to work on groundbreaking projects with almost endless resources, less administrative duties, and be paid handsomely for it too.

Some companies, Microsoft included, have taken measures to address the brain drain problem. After all, a lack of AI talent harms the entire industry.

Dr Chris Bishop, Director of Microsoft’s Research Lab in Cambridge, said:

“One thing we’ve seen over the past few years is: because there are so many opportunities for people with skills in machine learning, particularly in industry, we’ve seen a lot of outflux of top academic talent to industry.

This concerns us because it’s those top academic professors and researchers who are responsible not just for doing research, but also for nurturing the next generation of talent in this field.”

Since 2018, Microsoft has funded a program for training the next generation of data scientists and machine-learning engineers called the Microsoft Research-Cambridge University Machine Learning Initiative.

Microsoft partners with universities to ensure it doesn’t steal talent, allows employees to continue roles in teaching, funds some related PhD scholarships, sends researchers to co-supervise students in universities, and offers paid internships to work alongside teams at Microsoft on projects.

You can find the full AI Skills in the UK report here.

(Photo by William Warby on Unsplash)

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Bristol-based Graphcore ranked number one on Fast Company’s top 10 most innovative AI companies of 2020 list. Nvidia, for comparison, ranked fifth.

Fast Company’s confidence in Graphcore clearly isn’t misplaced. Announcing its GC200 processor, Graphcore says its new chip is the world’s most complex.

The GC200 processor boasts 59.4 billion transistors and takes the crown from Nvidia’s A100 as the world’s largest. The A100 was announced by Nvidia earlier this year and features 54 billion transistors.

Each GC200 chip has 1,472 independent processor cores and 8,832 separate parallel threads, all supported by 900MB of in-processor RAM.

Graphcore says that up to 64,000 of the 7nm GC200 chips can be linked to create a massive parallel processor with around 16 exaflops of computational power and petabytes of power. Such a system would be able to support AI models with trillions of parameters.

“We are impressed with Graphcore’s technology for energy-efficient construction and execution of large, next-generation ML models, and we expect significant performance gains for several of our AI-oriented research projects in medical imaging and cardiac simulations,” comments Are Magnus Bruaset, Research Director at Simula Research Laboratory.

“We are also pursuing other avenues of research that can push the envelope for Graphcore’s multi-IPU systems, such as how to efficiently conduct large-scale, sparse linear algebra operations commonly found in physics-based HPC workloads.”

The GC200 is just the second chip to be launched by Graphcore. Compared to the first generation, the GC200 delivers an up to 9.3x performance increase.

Graphcore’s founders believe the IPU approach that the company is taking is more efficient than Nvidia’s GPU route. The ability to scale up to thousands of IPU processors in existing compute infrastructures could mean that the cost could be 10-20x lower than using GPUs.

Back in February, Graphcore announced that it had raised $150 million in funding for its R&D. The company’s total valuation is $1.95 billion.

Graphcore was fortunate to have secured its cash before the COVID-19 pandemic really hit – with many startups reporting difficulties obtaining vital funding where there was previous interest. Undoubtedly, the GC200 will help to power research to get us through this pandemic and all the other challenges the world faces now and in the future.

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Clearview AI has repeatedly made headlines, and rarely for good reason. The company’s facial recognition technology is impressive but relies on scraping billions of people’s data from across the web.

“Common law has never recognised a right to privacy for your face,” Clearview AI lawyer Tor Ekeland argued recently.

Regulators in the UK and Australia seem to have a different perspective than Ekeland and have announced a joint probe into Clearview AI’s practices.

“The Office of the Australian Information Commissioner (OAIC) and the UK’s Information Commissioner’s Office (ICO) have opened a joint investigation into the personal information handling practices of Clearview Inc., focusing on the company’s use of ‘scraped’ data and biometrics of individuals,” the ICO wrote in a statement.

“The investigation highlights the importance of enforcement cooperation in protecting the personal information of Australian and UK citizens in a globalized data environment.”

A similar probe was launched by the EU’s privacy watchdog last month.

The European Data Protection Board ruled that any use of the service by law enforcement in Europe would “likely not be consistent with the EU data protection regime” and that it “has doubts as to whether any Union or Member State law provides a legal basis for using a service such as the one offered by Clearview AI.”

Clearview AI’s facial recognition system is used by over 2,200 law enforcement agencies around the world. A recent leak suggests it’s even being used by commercial businesses like Best Buy and Macy’s. In May, Clearview said it would stop working with non–law enforcement entities.

The American Civil Liberties Union (ACLU) launched a lawsuit against Clearview AI in May after calling it a “nightmare scenario” for privacy.

Aside from the company’s practices, concerns have been raised about Clearview AI’s extensive ties with the far-right. Ekeland himself has gained notoriety as “The Troll’s Lawyer” for defending clients such as neo-Nazi troll Andrew Auernheimer.

“Companies like Clearview will end privacy as we know it, and must be stopped,” said Nathan Freed Wessler, senior staff attorney with the ACLU’s Speech, Privacy, and Technology Project.

(Photo by The Creative Exchange on Unsplash)

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AI is proving itself to be an important tool in tackling some of the biggest issues the world faces today; including coronavirus and climate change. However, some public distrust remains.

“The current pandemic has shown us more needs to be done to speed up the adoption of trusted AI around the world,” said Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at the World Economic Forum.

“We moved from guidelines to practical tools, tested and iterated them – but this is still just a start. Now we will be working to scale them to countries around the world.”

The guidelines released today aim to “help society tackle big data problems faster” while also preparing them for future risks. The UK government adopted the guidelines across its various departments.

“The UK is a global leader in AI and I am pleased we are working with the World Economic Forum and international partners to develop guidelines to ensure its safe and ethical deployment,” said Caroline Dinenage, Digital Minister of the United Kingdom.

“By taking a dynamic approach we can boost innovation, create competitive markets and support public trust in artificial intelligence. I urge public sector organisations around the world to adopt these guidelines and consider carefully how they procure and deploy these technologies.”

For the past year, the WEF has worked alongside the UK’s Office for AI; companies such as Deloitte, Salesforce, and Splunk; 15 other countries; and more than 150 members of government, academia, civil society, and the private sector.

“As a trusted AI advisor to governments around the world, we were thrilled to collaborate with the World Economic Forum and the government of the UK in the development of procurement guidelines that help the public sector put AI at the service of its constituents in a manner that is both efficient and ethical,” said Shelby Austin, Managing Partner, Growth & Investments and Omnia AI, Deloitte, Canada.

“As our societies reorganize and make progress in our fight against COVID-19, the need for multi-stakeholder cooperation has never been more apparent. We believe in these joint efforts, and we believe in the power of data-driven decision-making to help our countries recover and thrive.”

The result of the joint effort was the “Procurement in a Box” toolkit which provides guidance from conducting drafting proposals and conducting risk assessments, all the way to purchasing AI solutions and deploying them in a trusted manner.

A proposal for a chatbot allowing executives for the Dubai Electricity and Water Authority (DEWA) to obtain answers to data-related questions was used to test the guidelines. DEWA’s chatbot was successful and serves as an early example of how rapid but safe AI deployments can be achieved using the guidelines.

“In an era that will continue to be dominated by the transformative technologies emerging from the Fourth Industrial Revolution, integrating AI into the public sector for everyday use will significantly elevate the performance of government departments,” said Khalfan Belhoul, CEO of the Dubai Future Foundation, the host entity of Centre for the Fourth Industrial Revolution UAE.

You can find a copy of the Procurement in a Box toolkit here (PDF)

(Photo by Franck V. on Unsplash)

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