The new SkillsMatch platform tackles skills assessment and matches your skills with training


European Commission: “The European labour market requires new skills to meet the demands of the Digital Age. EU citizens should have the right training, skills and support to empower them to find quality jobs and improve their living standards.

‘Soft skills’ such as confidence, teamwork, self-motivation, networking, presentation skills, are considered important for the employability and adaptability of Europe’s citizens. Soft skills are essential for how we work together and influence the decisions we take every day and can be more important than hard skills in today’s workplaces. The lack of soft skills is often only discovered once a person is already working on the job.

The state-of-the-art SkillsMatch platform helps users to match and adapt their soft skills assets to the demands of the labour market. The project is the first to offer a fully comprehensive platform with style guide cataloguing 36 different soft skills and matching them with occupations, as well as training opportunities, offering a large number of courses to improve soft skills depending on the chosen occupation.

The platform proposes courses, such as organisation and personal development, entrepreneurship, business communication and conflict resolution. There is a choice of courses in Spanish and English. Moreover, the platform will also provide recognition of the new learning and skills (open badges)…(More)”.

Commission proposes measures to boost data sharing and support European data spaces


Press Release: “To better exploit the potential of ever-growing data in a trustworthy European framework, the Commission today proposes new rules on data governance. The Regulation will facilitate data sharing across the EU and between sectors to create wealth for society, increase control and trust of both citizens and companies regarding their data, and offer an alternative European model to data handling practice of major tech platforms.

The amount of data generated by public bodies, businesses and citizens is constantly growing. It is expected to multiply by five between 2018 and 2025. These new rules will allow this data to be harnessed and will pave the way for sectoral European data spaces to benefit society, citizens and companies. In the Commission’s data strategy of February this year, nine such data spaces have been proposed, ranging from industry to energy, and from health to the European Green Deal. They will, for example, contribute to the green transition by improving the management of energy consumption, make delivery of personalised medicine a reality, and facilitate access to public services.

The Regulation includes:

  • A number of measures to increase trust in data sharing, as the lack of trust is currently a major obstacle and results in high costs.
  • Create new EU rules on neutrality to allow novel data intermediaries to function as trustworthy organisers of data sharing.
  • Measures to facilitate the reuse of certain data held by the public sector. For example, the reuse of health data could advance research to find cures for rare or chronic diseases.
  • Means to give Europeans control on the use of the data they generate, by making it easier and safer for companies and individuals to voluntarily make their data available for the wider common good under clear conditions….(More)”.

Geospatial Data Market Study


Study by Frontier Economics: “Frontier Economics was commissioned by the Geospatial Commission to carry out a detailed economic study of the size, features and characteristics of the UK geospatial data market. The Geospatial Commission was established within the Cabinet Office in 2018, as an independent, expert committee responsible for setting the UK’s Geospatial Strategy and coordinating public sector geospatial activity. The Geospatial Commission’s aim is to unlock the significant economic, social and environmental opportunities offered by location data. The UK’s Geospatial Strategy (2020) sets out how the UK can unlock the full power of location data and take advantage of the significant economic, social and environmental opportunities offered by location data….

Like many other forms of data, the value of geospatial data is not limited to the data creator or data user. Value from using geospatial data can be subdivided into several different categories, based on who the value accrues to:

Direct use value: where value accrues to users of geospatial data. This could include government using geospatial data to better manage public assets like roadways.

Indirect use value: where value is also derived by indirect beneficiaries who interact with direct users. This could include users of the public assets who benefit from better public service provision.

Spillover use value: value that accrues to others who are not a direct data user or indirect beneficiary. This could, for example, include lower levels of emissions due to improvement management of the road network by government. The benefits of lower emissions are felt by all of society even those who do not use the road network.

As the value from geospatial data does not always accrue to the direct user of the data, there is a risk of underinvestment in geospatial technology and services. Our £6 billion estimate of turnover for a subset of geospatial firms in 2018 does not take account of these wider economic benefits that “spill over” across the UK economy, and generate additional value. As such, the value that geospatial data delivers is likely to be significantly higher than we have estimated and is therefore an area for potential future investment….(More)”.

Covid-19 Data Is a Mess. We Need a Way to Make Sense of It.


Beth Blauer and Jennifer Nuzzo in the New York Times: “The United States is more than eight months into the pandemic and people are back waiting in long lines to be tested as coronavirus infections surge again. And yet there is still no federal standard to ensure testing results are being uniformly reported. Without uniform results, it is impossible to track cases accurately or respond effectively.

We test to identify coronavirus infections in communities. We can tell if we are casting a wide enough net by looking at test positivity — the percentage of people whose results are positive for the virus. The metric tells us whether we are testing enough or if the transmission of the virus is outpacing our efforts to slow it.

If the percentage of tests coming back positive is low, it gives us more confidence that we are not missing a lot of infections. It can also tell us whether a recent surge in cases may be a result of increased testing, as President Trump has asserted, or that cases are rising faster than the rate at which communities are able to test.

But to interpret these results properly, we need a national standard for how these results are reported publicly by each state. And although the Centers for Disease Control and Prevention issue protocols for how to report new cases and deaths, there is no uniform guideline for states to report testing results, which would tell us about the universe of people tested so we know we are doing enough testing to track the disease. (Even the C.D.C. was found in May to be reporting states’ results in a way that presented a misleading picture of the pandemic.)

Without a standard, states are deciding how to calculate positivity rates on their own — and their approaches are very different.

Some states include results from positive antigen-based tests, some states don’t. Some report the number of people tested, while others report only the number of tests administered, which can skew the overall results when people are tested repeatedly (as, say, at colleges and nursing homes)….(More)”

Malicious Uses and Abuses of Artificial Intelligence


Report by Europol, the United Nations Interregional Crime and Justice Research Institute (UNICRI) and Trend Micro: “… looking into current and predicted criminal uses of artificial intelligence (AI)… The report provides law enforcers, policy makers and other organizations with information on existing and potential attacks leveraging AI and recommendations on how to mitigate these risks.

“AI promises the world greater efficiency, automation and autonomy. At a time where the public is getting increasingly concerned about the possible misuse of AI, we have to be transparent about the threats, but also look into the potential benefits from AI technology.” said Edvardas Šileris, Head of Europol’s Cybercrime Centre. “This report will help us not only to anticipate possible malicious uses and abuses of AI, but also to prevent and mitigate those threats proactively. This is how we can unlock the potential AI holds and benefit from the positive use of AI systems.”

The report concludes that cybercriminals will leverage AI both as an attack vector and an attack surface. Deepfakes are currently the best-known use of AI as an attack vector. However, the report warns that new screening technology will be needed in the future to mitigate the risk of disinformation campaigns and extortion, as well as threats that target AI data sets.

For example, AI could be used to support:

  • Convincing social engineering attacks at scale
  • Document-scraping malware to make attacks more efficient
  • Evasion of image recognition and voice biometrics
  • Ransomware attacks, through intelligent targeting and evasion
  • Data pollution, by identifying blind spots in detection rules..

The three organizations make several recommendations to conclude the report:

The Next Generation Humanitarian Distributed Platform


Report by Mercy Corps, the Danish Red Cross and hiveonline: “… call for the development of a shared, sector-wide “blockchain for good” to allow the aid sector to better automate and track processes in real-time, and maintain secure records. This would help modernize and coordinate the sector to reach more people as increasing threats such as pandemics, climate change and natural disasters require aid to be disbursed faster, more widely and efficiently.

A cross-sector blockchain platform – a digital database that can be simultaneously used and shared within a large decentralized, publicly accessible network – could support applications ranging from cash and voucher distribution to identity services, natural capital and carbon tracking, and donor engagement.

The report authors call for the creation of a committee to develop cross-sector governance and coordinate the implementation of a shared “Humanitarian Distributed Platform.” The authors believe the technology can help organizations fulfill commitments made to transparency, collaboration and efficiency under the Humanitarian Grand Bargain.

The report is compiled from responses of 35 survey participants, representing stakeholders in the humanitarian sector, including NGO project implementers, consultants, blockchain developers, academics, and founders. A further 39 direct interviews took place over the course of the research between July and September 2020….(More)”.

For America’s New Mayors, a Chance to Lead with Data


Article by Zachary Markovits and Molly Daniell:”While the presidential race drew much of the nation’s attention this year, voters also chose leaders in 346 mayoral elections, as well as many more city and county commission and council races, reshaping the character of government leadership from coast to coast.

These newly elected and re-elected leaders will enter office facing an unprecedented set of challenges: a worsening pandemic, weakened local economies, budget shortfalls and a reckoning over how government policies have contributed to racial injustice. To help their communities “build back better”—in the words of the new President-elect—these leaders will need not just more federal support, but also a strategy that is data-driven in order to protect their residents and ensure that resources are invested where they are needed most.

For America’s new mayors, it’s a chance to show the public what effective leadership looks like after a chaotic federal response to Covid-19—and no response can be fully effective without putting data at the center of how leaders make decisions.

Throughout 2020, we’ve been documenting the key steps that local leaders can take to advance a culture of data-informed decision-making. Here are five lessons that can help guide these new leaders as they seek to meet this moment of national crisis:

1. Articulate a vision

The voice of the chief executive is galvanizing and unlike any other in city hall. That’s why the vision for data-driven government must be articulated from the top. From the moment they are sworn in, mayors have the opportunity to lean forward and use their authority to communicate to the whole administration, council members and city employees about the shift to using data to drive policymaking.

Consider Los Angeles Mayor Eric Garcetti who, upon coming into office, spearheaded an internal review process culminating in this memo to all general managers stressing the need for a culture of both continuous learning and performance. In this memo, he creates urgency, articulates precisely what will change and how it will affect the success of the organization as well as build a data-driven culture….(More)”.

Using artificial intelligence to make decisions: Addressing the problem of algorithmic bias (2020)


Foreword of a Report by the Australian Human Rights Commission: “Artificial intelligence (AI) promises better, smarter decision making.

Governments are starting to use AI to make decisions in welfare, policing and law enforcement, immigration, and many other areas. Meanwhile, the private sector is already using AI to make decisions about pricing and risk, to determine what sorts of people make the ‘best’ customers… In fact, the use cases for AI are limited only by our imagination.

However, using AI carries with it the risk of algorithmic bias. Unless we fully understand and address this risk, the promise of AI will be hollow.

Algorithmic bias is a kind of error associated with the use of AI in decision making, and often results in unfairness. Algorithmic bias can arise in many ways. Sometimes the problem is with the design of the AI-powered decision-making tool itself. Sometimes the problem lies with the data set that was used to train the AI tool, which could replicate or even make worse existing problems, including societal inequality.

Algorithmic bias can cause real harm. It can lead to a person being unfairly treated, or even suffering unlawful discrimination, on the basis of characteristics such as their race, age, sex or disability.

This project started by simulating a typical decision-making process. In this technical paper, we explore how algorithmic bias can ‘creep in’ to AI systems and, most importantly, how this problem can be addressed.

To ground our discussion, we chose a hypothetical scenario: an electricity retailer uses an AI-powered tool to decide how to offer its products to customers, and on what terms. The general principles and solutions for mitigating the problem, however, will be relevant far beyond this specific situation.

Because algorithmic bias can result in unlawful activity, there is a legal imperative to address this risk. However, good businesses go further than the bare minimum legal requirements, to ensure they always act ethically and do not jeopardise their good name.

Rigorous design, testing and monitoring can avoid algorithmic bias. This technical paper offers some guidance for companies to ensure that when they use AI, their decisions are fair, accurate and comply with human rights….(More)”

Facial-recognition research needs an ethical reckoning


Editorial in Nature: “…As Nature reports in a series of Features on facial recognition this week, many in the field are rightly worried about how the technology is being used. They know that their work enables people to be easily identified, and therefore targeted, on an unprecedented scale. Some scientists are analysing the inaccuracies and biases inherent in facial-recognition technology, warning of discrimination, and joining the campaigners calling for stronger regulation, greater transparency, consultation with the communities that are being monitored by cameras — and for use of the technology to be suspended while lawmakers reconsider where and how it should be used. The technology might well have benefits, but these need to be assessed against the risks, which is why it needs to be properly and carefully regulated.Is facial recognition too biased to be let loose?

Responsible studies

Some scientists are urging a rethink of ethics in the field of facial-recognition research, too. They are arguing, for example, that scientists should not be doing certain types of research. Many are angry about academic studies that sought to study the faces of people from vulnerable groups, such as the Uyghur population in China, whom the government has subjected to surveillance and detained on a mass scale.

Others have condemned papers that sought to classify faces by scientifically and ethically dubious measures such as criminality….One problem is that AI guidance tends to consist of principles that aren’t easily translated into practice. Last year, the philosopher Brent Mittelstadt at the University of Oxford, UK, noted that at least 84 AI ethics initiatives had produced high-level principles on both the ethical development and deployment of AI (B. Mittelstadt Nature Mach. Intell. 1, 501–507; 2019). These tended to converge around classical medical-ethics concepts, such as respect for human autonomy, the prevention of harm, fairness and explicability (or transparency). But Mittelstadt pointed out that different cultures disagree fundamentally on what principles such as ‘fairness’ or ‘respect for autonomy’ actually mean in practice. Medicine has internationally agreed norms for preventing harm to patients, and robust accountability mechanisms. AI lacks these, Mittelstadt noted. Specific case studies and worked examples would be much more helpful to prevent ethics guidance becoming little more than window-dressing….(More)”.

Technologies of Speculation: The limits of knowledge in a data-driven society


Book by Sun-ha Hong: “What counts as knowledge in the age of big data and smart machines? In its pursuit of better knowledge, technology is reshaping what counts as knowledge in its own image – and demanding that the rest of us catch up to new machinic standards for what counts as suspicious, informed, employable. In the process, datafication often generates speculation as much as it does information. The push for algorithmic certainty sets loose an expansive array of incomplete archives, speculative judgments and simulated futures where technology meets enduring social and political problems.

Technologies of Speculation traces this technological manufacturing of speculation as knowledge. It shows how unprovable predictions, uncertain data and black-boxed systems are upgraded into the status of fact – with lasting consequences for criminal justice, public opinion, employability, and more. It tells the story of vast dragnet systems constructed to predict the next terrorist, and how familiar forms of prejudice seep into the data by the back door. In software placeholders like ‘Mohammed Badguy’, the fantasy of pure data collides with the old spectre of national purity. It tells the story of smart machines for ubiquitous and automated self-tracking, manufacturing knowledge that paradoxically lies beyond the human senses. Such data is increasingly being taken up by employers, insurers and courts of law, creating imperfect proxies through which my truth can be overruled.

The book situates ongoing controversies over AI and algorithms within a broader societal faith in objective truth and technological progress. It argues that even as datafication leverages this faith to establish its dominance, it is dismantling the longstanding link between knowledge and human reason, rational publics and free individuals. Technologies of Speculation thus emphasises the basic ethical problem underlying contemporary debates over privacy, surveillance and algorithmic bias: who, or what, has the right to the truth of who I am and what is good for me? If data promises objective knowledge, then we must ask in return: knowledge by and for whom, enabling what forms of life for the human subject?…(More)”.