Emerging Labour Market Data Sources towards Digital Technical and Vocational Education and Training (TVET)


Paper by Nikos Askitas, Rafik Mahjoubi, Pedro S. Martins, Koffi Zougbede for Paris21/OECD: “Experience from both technology and policy making shows that solutions for labour market improvements are simply choices of new, more tolerable problems. All data solutions supporting digital Technical and Vocational Education and Training (TVET) will have to incorporate a roadmap of changes rather than an unrealistic super-solution. The ideal situation is a world in which labour market participants engage in intelligent strategic behavior in an informed, fair and sophisticated manner.

Labour market data captures transactions within labour market processes. In order to successfully capture such data, we need to understand the specifics of these market processes. Designing an ecosystem of labour market matching facilitators and rules of engagement for contributing to a lean and streamlined Logistics Management and Information System (LMIS) is the best way to create Big Data with context relevance. This is in contrast with pre-existing Big Data captured by global job boards or social media for which relevance is limited by the technology access gap and its variations across the developing world.

Network effects occur in technology and job facilitation, as seen in the developed world. Managing and instigating the right network effects might be crucial to avoid fragmented stagnation and inefficiency. This is key to avoid throwing money behind wrong choices that do not gain traction.

A mixed mode approach is possibly the ideal approach for developing countries. Mixing offline and online elements correctly will be crucial in bridging the technology access gap and reaping the benefits of digitisation at the same time.

Properly incentivising the various entities is critical for progression, and more specifically the private sector, which is significantly more agile and inventive, has “skin in the game” and a long-term commitment to the conditions in the field, has intimate knowledge of how to solve the the technology gap and brings a better understanding of the particular ambient context they are operating in. To summarise: Big Data starts small.

Managing expectations and creating incentives for the various stakeholders will be crucial in establishing digitally supported TVET. Developing the right business models will be crucial in the short term and beyond, and it will be the result of creating the right mix of technological and policy expertise with good knowledge of the situation on the ground….(More)”.

Don’t forget people in the use of big data for development


Joshua Blumenstock at Nature: “Today, 95% of the global population has mobile-phone coverage, and the number of people who own a phone is rising fast (see ‘Dialling up’)1. Phones generate troves of personal data on billions of people, including those who live on a few dollars a day. So aid organizations, researchers and private companies are looking at ways in which this ‘data revolution’ could transform international development.

Some businesses are starting to make their data and tools available to those trying to solve humanitarian problems. The Earth-imaging company Planet in San Francisco, California, for example, makes its high-resolution satellite pictures freely available after natural disasters so that researchers and aid organizations can coordinate relief efforts. Meanwhile, organizations such as the World Bank and the United Nations are recruiting teams of data scientists to apply their skills in statistics and machine learning to challenges in international development.

But in the rush to find technological solutions to complex global problems there’s a danger of researchers and others being distracted by the technology and losing track of the key hardships and constraints that are unique to each local context. Designing data-enabled applications that work in the real world will require a slower approach that pays much more attention to the people behind the numbers…(More)”.

Is the Government More Entrepreneurial Than You Think?


 Freakonomics Radio (Podcast): We all know the standard story: our economy would be more dynamic if only the government would get out of the way. The economist Mariana Mazzucato says we’ve got that story backward. She argues that the government, by funding so much early-stage research, is hugely responsible for big successes in tech, pharma, energy, and more. But the government also does a terrible job in claiming credit — and, more important, getting a return on its investment….

Quote:

MAZZUCATO: “…And I’ve been thinking about this especially around the big data and the kind of new questions around privacy with Facebook, etc. Instead of having a situation where all the data basically gets captured, which is citizens’ data, by companies which then, in some way, we have to pay into in terms of accessing these great new services — whether they’re free or not, we’re still indirectly paying. We should have the data in some sort of public repository because it’s citizens’ data. The technology itself was funded by the citizens. What would Uber be without GPS, publicly financed? What would Google be without the Internet, publicly financed? So, the tech was financed from the state, the citizens; it’s their data. Why not completely reverse the current relationship and have that data in a public repository which companies actually have to pay into to get access to it under certain strict conditions which could be set by an independent advisory council?… (More)”

What if technologies had their own ethical standards?


European Parliament: “Technologies are often seen either as objects of ethical scrutiny or as challenging traditional ethical norms. The advent of autonomous machines, deep learning and big data techniques, blockchain applications and ‘smart’ technological products raises the need to introduce ethical norms into these devices. The very act of building new and emerging technologies has also become the act of creating specific moral systems within which human and artificial agents will interact through transactions with moral implications. But what if technologies introduced and defined their own ethical standards?…(More)”.

AI and Big Data: A Blueprint for a Human Rights, Social and Ethical Impact Assessment


Alessandro Mantelero in Computer Law & Security Review: “The use of algorithms in modern data processing techniques, as well as data-intensive technological trends, suggests the adoption of a broader view of the data protection impact assessment. This will force data controllers to go beyond the traditional focus on data quality and security, and consider the impact of data processing on fundamental rights and collective social and ethical values.

Building on studies of the collective dimension of data protection, this article sets out to embed this new perspective in an assessment model centred on human rights (Human Rights, Ethical and Social Impact Assessment-HRESIA). This self-assessment model intends to overcome the limitations of the existing assessment models, which are either too closely focused on data processing or have an extent and granularity that make them too complicated to evaluate the consequences of a given use of data. In terms of architecture, the HRESIA has two main elements: a self-assessment questionnaire and an ad hoc expert committee. As a blueprint, this contribution focuses mainly on the nature of the proposed model, its architecture and its challenges; a more detailed description of the model and the content of the questionnaire will be discussed in a future publication drawing on the ongoing research….(More)”.

Towards Digital Enlightenment: Essays on the Dark and Light Sides of the Digital Revolution


Book edited by Dirk Helbing: “This new collection of essays follows in the footsteps of the successful volume Thinking Ahead – Essays on Big Data, Digital Revolution, and Participatory Market Society, published at a time when our societies were on a path to technological totalitarianism, as exemplified by mass surveillance reported by Edward Snowden and others.

Meanwhile the threats have diversified and tech companies have gathered enough data to create detailed profiles about almost everyone living in the modern world – profiles that can predict our behavior better than our friends, families, or even partners. This is not only used to manipulate peoples’ opinions and voting behaviors, but more generally to influence consumer behavior at all levels. It is becoming increasingly clear that we are rapidly heading towards a cybernetic society, in which algorithms and social bots aim to control both the societal dynamics and individual behaviors….(More)”.

Origin Privacy: Protecting Privacy in the Big-Data Era


Paper by Helen Nissenbaum, Sebastian Benthall, Anupam Datta, Michael Carl Tschantz, and Piot Mardziel: “Machine learning over big data poses challenges for our conceptualization of privacy. Such techniques can discover surprising and counteractive associations that take innocent looking data and turns it into important inferences about a person. For example, the buying carbon monoxide monitors has been linked to paying credit card bills, while buying chrome-skull car accessories predicts not doing so. Also, Target may have used the buying of scent-free hand lotion and vitamins as a sign that the buyer is pregnant. If we take pregnancy status to be private and assume that we should prohibit the sharing information that can reveal that fact, then we have created an unworkable notion of privacy, one in which sharing any scrap of data may violate privacy.

Prior technical specifications of privacy depend on the classification of certain types of information as private or sensitive; privacy policies in these frameworks limit access to data that allow inference of this sensitive information. As the above examples show, today’s data rich world creates a new kind of problem: it is difficult if not impossible to guarantee that information does notallow inference of sensitive topics. This makes information flow rules based on information topic unstable.

We address the problem of providing a workable definition of private data that takes into account emerging threats to privacy from large-scale data collection systems. We build on Contextual Integrity and its claim that privacy is appropriate information flow, or flow according to socially or legally specified rules.

As in other adaptations of Contextual Integrity (CI) to computer science, the parameterization of social norms in CI is translated into a logical specification. In this work, we depart from CI by considering rules that restrict information flow based on its origin and provenance, instead of on it’s type, topic, or subject.

We call this concept of privacy as adherence to origin-based rules Origin Privacy. Origin Privacy rules can be found in some existing data protection laws. This motivates the computational implementation of origin-based rules for the simple purpose of compliance engineering. We also formally model origin privacy to determine what security properties it guarantees relative to the concerns that motivate it….(More)”.

Biometric Mirror


University of Melbourne: “Biometric Mirror exposes the possibilities of artificial intelligence and facial analysis in public space. The aim is to investigate the attitudes that emerge as people are presented with different perspectives on their own, anonymised biometric data distinguished from a single photograph of their face. It sheds light on the specific data that people oppose and approve, the sentiments it evokes, and the underlying reasoning. Biometric Mirror also presents an opportunity to reflect on whether the plausible future of artificial intelligence is a future we want to see take shape.

Big data and artificial intelligence are some of today’s most popular buzzwords. Both are promised to help deliver insights that were previously too complex for computer systems to calculate. With examples ranging from personalised recommendation systems to automatic facial analyses, user-generated data is now analysed by algorithms to identify patterns and predict outcomes. And the common view is that these developments will have a positive impact on society.

Within the realm of artificial intelligence (AI), facial analysis gains popularity. Today, CCTV cameras and advertising screens increasingly link with analysis systems that are able to detect emotions, age, gender and demographic information of people passing by. It has proven to increase advertising effectiveness in retail environments, since campaigns can now be tailored to specific audience profiles and situations. But facial analysis models are also being developed to predict your aggression levelsexual preferencelife expectancy and likeliness of being a terrorist (or an academic) by simply monitoring surveillance camera footage or analysing a single photograph. Some of these developments have gained widespread media coverage for their innovative nature, but often the ethical and social impact is only a side thought.

Current technological developments approach ethical boundaries of the artificial intelligence age. Facial recognition and analysis in public space raise concerns as people are photographed without prior consent, and their photos disappear into a commercial operator’s infrastructure. It remains unclear how the data is processed, how the data is tailored for specific purposes and how the data is retained or disposed of. People also do not have the opportunity to review or amend their facial recognition data. Perhaps most worryingly, artificial intelligence systems may make decisions or deliver feedback based on the data, regardless of its accuracy or completeness. While facial recognition and analysis may be harmless for tailored advertising in retail environments or to unlock your phone, it quickly pushes ethical boundaries when the general purpose is to more closely monitor society… (More).

Social media big data analytics: A survey


Norjihan Abdul Ghani et al in Computers in Human Behavior: “Big data analytics has recently emerged as an important research area due to the popularity of the Internet and the advent of the Web 2.0 technologies. Moreover, the proliferation and adoption of social media applications have provided extensive opportunities and challenges for researchers and practitioners. The massive amount of data generated by users using social media platforms is the result of the integration of their background details and daily activities.

This enormous volume of generated data known as “big data” has been intensively researched recently. A review of the recent works is presented to obtain a broad perspective of the social media big data analytics research topic. We classify the literature based on important aspects. This study also compares possible big data analytics techniques and their quality attributes. Moreover, we provide a discussion on the applications of social media big data analytics by highlighting the state-of-the-art techniques, methods, and the quality attributes of various studies. Open research challenges in big data analytics are described as well….(More)”.

When Westlaw Fuels Ice Surveillance: Ethics in the Big Data Policing Era


Sarah Lamdan at New York University Review of Law & Social Change: “Legal research companies are selling surveillance data and services to U.S. Immigration and Customs Enforcement (ICE) and other law enforcement agencies.

This article discusses ethical issues that arise when lawyers buy and use legal research services sold by the vendors that build ICE’s surveillance systems. As the legal profession collectively pays millions of dollars for computer assisted legal research services, lawyers should consider whether doing so in the era of big data policing compromises their confidentiality requirements and their obligation to supervise third party vendors….(More)”