Месечни архиви: декември 2020

What if blockchain could guarantee ethical AI?

Written by Mihalis Kritikos,

© Adobe Stock

Blockchain has the potential to promote compliance with traditional ethical principles, especially in the fields of healthcare, supply chain management and food safety. As artificial intelligence (AI) companies and other organisations seek ways to comply with ethical principles and requirements, blockchain could be seen as a means to ensure that AI is deployed in an ethically sound manner, under certain specific conditions.

Blockchains are open, decentralised ledgers that record transactions between two parties without the need for third-party authentication. Their ability to ensure that data are secure, well-protected and reliable, and thus can be shared in a secure and auditable manner, mean that blockchain applications are being used in a growing number of domains, such as healthcare management, cross-border payments and supply chain monitoring. Their implementation raises ethical concerns about security vulnerabilities, environmental impact – given the high amount of computing power needed, accountability, privacy and the apparent enabling of cybercrime. Various policy initiatives have been launched to address these challenges in the form of ethical design frameworks, guiding principles and the Blockchain Code of Ethics. At the same time, blockchain has emerged lately as a carrier of ethical values that could resolve societal challenges of high ethical import in several domains. Can the intrinsic features of blockchain technology help AI developers comply with the multiplicity of ethical demands in their field and in effect contribute to the ethical design and deployment of AI applications?

Potential impacts and developments

Blockchain technology has the potential to create ethical value by creating more transparent and traceable food supply chains to tackle major challenges such as unethical labour practices and environmental degradation. Its ethical value also lies in its ability to provide for secured proof of origin and ethical sourcing. It can also facilitate the sharing of medical data via the automation of some aspects of consent and data collection. Beyond the indirect effects of blockchain on the achievement of certain ethical aims within specific policy domains, this emerging technology appears to offer the means to facilitate the compliance of AI, in its various manifestations, with ethical principles and human rights standards.

It is well known that the quality, accuracy and representative nature of the data needed to train algorithms and develop human-centric machine learning models is central to the ethical soundness of AI applications. However, as there is no oversight mechanism and no standard methodologies to review the fairness of these algorithms or the privacy-friendly nature of the data analytics used, multiple calls have been made for the development of ethics standards and frameworks. The opacity of algorithmic operations and the use of self-learning algorithms for predictive policing, social security or diagnostics is currently at the epicentre of the ethical debate at EU level.

This is precisely where blockchain technologies can play an important role in helping AI applications and systems be designed and implemented in an ethically sound manner. One of the main advantages of blockchain lies in its ability to ensure that data are secure, private, reliable and valid, and thus personal data are not compromised. Therefore, blockchain enables cooperative and safe data-sharing, by cryptographically ensuring the trustworthiness of data. It may therefore be seen as a way to enable users to share their data with trusted stakeholders before the data are collected and processed by powerful AI systems in the context of specific decentralised AI platforms. In other words, the introduction of decentralised blockchain solutions in the context of AI may facilitate the removal of false or incorrect data sets, strengthen the privacy-friendly nature of AI data infrastructure and, essentially, contribute to its ethical design and deployment.

Given that blockchain can operate as a transparency machine where its users are assured that the data stored, on a datapoint-by-datapoint basis, have not been tampered with through the use of cryptographic hashing, digital signatures or smart contracts, it can increase the trust that is so necessary in the field of AI. Transparency is in fact one of the seven ethical requirements put forward by the High-Level Expert Group on AI and endorsed by the European Commission. This is also a necessary step to promote and protect the principle of explicability: the need for AI processes to be transparent and explainable.

As blockchain technologies offer users a detailed view of how data are being used, the introduction of these properties into the AI context could potentially help developers to design human-centric and responsible algorithms, and citizens to exercise their right to explanation and to effective remedy. In fact, allowing advanced AI models and large datasets to be widely shared, updated and re-trained could boost trust in algorithmic decision-making systems.

Moreover, blockchain’s traceability and data integrity features and its capacity to operate in a decentralised manner could be crucial in ensuring that the data used in AI systems are reliable, of high quality and bias-free. Blockchain’s use of immutable records of all the data, variables, and processes used by AI for its algorithmic decision-making processes could enable decision-makers in the field of AI to audit the main tenets of the systems/applications used, review and diversify datasets, set aside data that could lead to false negatives, and identify biased algorithms. That could eventually enable AI applications to be viewed as reliable sources of information and knowledge that could not develop any discriminatory or manipulative effects via deep fakes or predictive behaviour algorithms.

As a result, the accessibility and transparency qualities of blockchain programming, making it possible to audit all steps of the process – from data entry to processing outcomes – could serve as a solid basis for demystifying AI, enhancing the ethical nature of algorithmic decision-making systems and solving the AI black box problem through transparency and algorithmic impact assessments. That way, blockchain, by being publicly auditable, would help the public understand machine learning decisions, thus increasing the explainability of AI systems. Blockchain’s recording properties can help AI users and decision-makers trace, review and reframe all variables that feed into decisions made on the basis of machine-learning procedures.

Last but not least, not only could blockchain’s ability to operate without intermediaries prevent data manipulation, it could also allow small AI companies to obtain trustworthy data directly from their creators through decentralised blockchain data networks. This is particularly important for the ethical development of AI, as blockchain programming can also create an incentive system that could encourage users to contribute and share their data. Such a system could, in effect, enhance the robustness and fairness of data models, strengthen the quality of algorithmic data sets and bring forward a paradigm shift in the ethical governance of AI applications.

Anticipatory policy-making

As the issue of AI ethics has become a key part of discussions on governance and regulatory control of this transformative technology at both organisational and policy-making levels, new and creative ways need to be found to secure the efficient operationalisation of commonly agreed ethical principles. That requires not only the development of practical implementation guidance but also the employment of new tools.

Blockchain, due to its specific design qualities, can become part of ethical problem-solving in the field of AI in various ways. As legislators across the world seek ways to identify the sources and address the effects of data bias in the context of AI, and to introduce a proportionate risk assessment and management framework, blockchain architecture can become an integral element in the ethics-by-design approach. This concept has been proposed repeatedly by the European Parliament and was also reflected in its recent resolution on the framework of ethical aspects of artificial intelligence, robotics and related technologies.

In the light of the Commission’s upcoming legislative proposal on the control of AI and the recently proposed data governance act, the regulatory features of blockchain could offer numerous advantages, including anonymisation, enhanced data security, immutability, and consensus-driven tools. Its integration into the AI world could provide developers and users alike with an ecosystem of modalities and features that would enhance the effective implementation of ethical requirements and principles and, in effect, increase public trust in AI systems. Given the potential benefits of the introduction of blockchain properties into AI platforms, the establishment of EU-wide hybrid pilot platforms that could facilitate the convergence of AI and blockchain architectures could unleash the potential of blockchain as an ethical game-changer in the field of AI.


Read this ‘at a glance’ on ‘What if blockchain could guarantee ethical AI?‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/26/what-if-blockchain-could-guarantee-ethical-ai/

What if AI could improve thermal imaging, to help fight coronavirus?

Written by Mihalis Kritikos,

© Adobe Stock

Thermal imaging cameras have been widely installed in recent months in office buildings, hospitals, shopping malls, schools and airports as a means of detecting people with fever-like symptoms. Given their capacity to perform temperature checks from a distance, they have been seen as an effective means to limit the spread of the highly contagious Covid-19 virus. Looking beyond manual temperature checking, this note provides an overview of the use of thermal-imaging empowered with artificial intelligence (AI) capabilities, its suitability in the context of the current pandemic and the core technical advantages and limitations of this technology. The main legal responses and ethical concerns related to the use of AI in the context of thermal imaging at entry points, to identify and triage people who may have elevated temperatures, are also examined.

Infrared thermal-imaging cameras can measure radiated energy emitted from the human skin in a contactless, safe and fast manner. Adding machine-learning capabilities allows them to survey large groups of people at points of entry in an inexpensive, non-invasive way and to process their temperatures in seconds in the context of the current pandemic. Within this context, AI-enhanced thermal imaging cameras are currently being used in sensitive locations around the world to spot those who may have a symptom of the virus. Is general fever measurement through thermal cameras an effective means to tackle Covid‑19? Are these cameras designed or sufficiently operationally mature to operate as medical devices or diagnostic tests? Should we consider the possible legal and ethical implications related to the use of these cameras, especially when paired with facial recognition software and movement-predictive algorithms? Are employees and passengers aware of their data protection rights, including their right to rectification as well as their right to benefit from a second measurement?

Potential impacts and developments

AI-based thermal-imaging technology allows for fast and scalable screening of employees and travellers, from a distance, while they are moving, and without asking individuals to queue for individual checks. It can identify potential Covid‑19 carriers by automating and streamlining the monitoring of an individual’s temperature, simplifying and standardising record-keeping, and by reducing the need for invasive or potentially error-prone manual tracking procedures.

The integration of optimised algorithms for fever detection and facial-detection algorithms, as well as mask wearing detection functions, in thermal cameras allows them to  recognise human faces obscured by masks and glasses and distinguish faces from nearby objects in real time by excluding other heat sources. Through the use of machine-learning algorithms, automated recalibration procedures and AI-powered statistical analytics, thermal imaging can achieve high measurement speed and accurate temperature screening of up to 95 %. The incorporation of advanced AI image-processing and video-analytics algorithms not only allows the detection of elevated skin temperature in high-traffic public places through quick multiple target screening but also facilitates the emission of automatic alerts to security personnel. The integration of accurate mask-on face-recognition functionalities in thermal-imaging cameras is currently being tested in the United States of America, Israel, China and several Latin American countries.

Some authorities are now able to identify patients with an elevated temperature, revisit their location history through automated analyses of closed-circuit television (CCTV) footage, and provide audio and visual notification of temperature-screening passes and failures. London Heathrow Airport has used the technology to carry out large-scale passenger temperature checks, whereas Los Angeles International Airport has begun piloting thermal-imaging cameras that can detect fever in travellers. The installation of these cameras in entry points could reduce bottlenecks and delays, screen dozens or hundreds of people without the latter violating social distancing requirements, but also requiring less manpower for temperature checks.

At the same time however, the European Union Aviation Safety Agency, the European Centre for Disease Prevention and Control and the World Health Organization (WHO) have concluded that thermal screening of passengers is a ‘high-cost, low-efficiency measure‘. There is little evidence of its effectiveness and accuracy in detecting and mitigating Covid‑19 cases, given that temperature is a bad proxy for having the disease. In addition, these measuring devices are sometimes not very accurate when used in high-traffic areas, where several individuals are moving in different directions at once, while being presented to the camera from different distances and at different angles.

Moreover, scanning may not detect people with early-stage illness, asymptomatic illness, those with symptoms that do not include fever, or those who take medicines to reduce their temperature. The UK Medicines and Healthcare products Regulatory Agency has noted that thermal cameras are not a reliable way to detect if people have the virus whereas the US Food and Drug Administration has concluded that, despite their multiple advantages, are not effective at determining if someone definitively has Covid-19.

Anticipatory policy-making

In view of the absence of a common international standard for health-screening at airports and workplace locations, the use of thermal-screening cameras triggers questions about their compliance with ISO 13154, which sets the standard for deployment and implementation, and operational guidelines for identifying febrile humans using a screening thermograph, as well as with IEC 80601-2-59:2017 requirements for the basic safety and essential performance of screening thermographs for human febrile temperature screening.

Do temperature checks using AI-assisted thermal cameras constitute processing of personal data wholly or partly by automated means within the meaning of Article 2(5) of the General Data Protection Regulation (GDPR)? Is the use of cameras to perform mass checks justified under the duty of care of the employer towards employees and proportionate under data protection and human rights laws? Is temperature-related data going to be analysed along with other biometric identifiers?

Many European Data Protection Authorities (DPAs), including in Belgium, France, Czechia, the Netherlands and Poland, have made a series of recommendations that range from absolute prohibition of their use for triaging people, to allowing thermal scanning under specific conditions. Such conditions include an analysis of the data life cycle and the verification that there is no recording of thermal images in accordance with the orientations on body temperature checks in the context of the Covid‑19 crisis that were recently issued by the European Data Protection Supervisor. An assessment of this kind should take account of the necessity, proportionality and effectiveness of this technological solution and provide for meaningful human involvement.

Beyond privacy concerns, the gradual installation of AI-enhanced thermal-imaging cameras enabled by facial recognition technology as a fever-detection tool in public spaces raises questions about their effects on the civil liberties of travellers and employees alike including questions of surveillance creep, namely the collection of biometric data beyond the current emergency context. The gradual introduction of this technology in airports and office buildings to proactively detect an elevated temperature also raises questions about what happens when people are detected as having fever, especially in cases of false positives:

Can they be banned from the airport or their workplace? Are robust safeguards in place to verify the technical accuracy of these public health measures, including meaningful human overview and control of the system? Are ethically and legally sound standardised technical protocols in place that could prescribe additional tests and temperature checks, data verification, and robust data protection safeguards?

In view of the novelty and possible limitations of the technological solutions being proposed, it would seem reasonable that any remote temperature-screening finding should be accompanied by secondary temperature screening, temperature checks by a healthcare professional and health questionnaires, and should be directed by public health guidance. They should be viewed as only one layer of protection in the context of the wider ecosystem of public-health emergency responses to the current pandemic.


Read this ‘at a glance’ on ‘What if AI could improve thermal imaging, to help fight coronavirus?‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/25/what-if-ai-could-improve-thermal-imaging-to-help-fight-coronavirus/

What if technology and culture combined to boost a green recovery?

Written by Vadim Kononenko,

© Adobe Stock

Technological innovation has always been an indispensable part of recovery from economic, social and environmental crises. Technology is often diametrically opposed to matters of aesthetics and culture. Yet historical experience and foresight suggest that in times of recovery technology and culture can combine to create a virtuous feedback loop. This could facilitate the EU’s post-pandemic recovery and also help tackle the potentially disruptive effects of the ‘green transition’.

With its current European Green Deal plan, the EU is striving to achieve climate neutrality in its economy by 2050 and, simultaneously, set itself on the path to recovery from the adverse effects of the global pandemic. Technology will inevitably play a significant part in this process. However, history also suggests that culture and aesthetics have a significant role to play in recovery from a crisis, be it war, economic recession or an epidemic.

Well-known artistic and architectural movements such as the Renaissance, Romanticism and Neo-Classicism came about in direct or indirect response to various shocks in Europe, for example, the plague of the 13th century, the Industrial Revolution of the 18th century, and political upheavals of the 19th century. Most recently, the 20th-century modernist movement was spearheaded by the recovery from the two world wars and skyrocketing post-war economic growth. None of these cultural movements developed autonomously from technology, however. Modernism, for example, was underpinned by the invention of steel and concrete construction techniques.

Potential impacts and developments

It is logical to assume that the EUs unprecedented green transition to a carbon-free economy will be accompanied by new technologies and also, perhaps, a new cultural movement. Some policies featuring cultural and technological aspects have emerged in recent years, in the form of the Davos Declaration and Baukultur and, most recently, the New European Bauhaus initiative of the European Commission. Yet the question remains: how can technology and culture align to further a green post-pandemic recovery in Europe, particularly given the extreme negative impacts of the pandemic on the cultural sector?

Among the many pertinent aspects of the interconnected dynamics of technology and culture, two in particular stand out:

  • There is a disruptive side to every recovery, as the many accompanying changes leave ‘stranded assets’ in their wake: investments that prematurely lose their value. Rapidly changing technology is often a factor that contributes to this phenomenon. For example, recovery from the Great Depression was closely related to the rise of the automobile industry, leading to the decline of American urban city centres. The green recovery, which is based on renewable energy technologies, is generating different kinds of stranded assets, for example, coal mines and pipelines or the beautiful but single-glazed windows of historical buildings. The rapid rise in teleworking is leaving a massive amount of unused office space as stranded assets. Culture could be the answer in cases such as these. To match the circular economy, a new aesthetic of car-free urbanism and ‘green and blue‘ cities is emerging. Likewise, re‑purposing old coal plants and empty office buildings into green neighbourhood and museum clusters is an increasingly popular and effective solution. In this regard, culture is helping to tackle the social impacts of technological disruption.
  • Conversely, culture itself can benefit from technological change. This has been seen during the pandemic, in which digital technologies have greatly aided the creative sector. Museums, concert halls and other cultural institutions have taken to live-streaming, online events, and open access to digital material. It is likely that these technologies will continue to serve the cultural sector in the recovery phase as well. However, the question is how sustainable this online content is, both for the economic survival of institutions and creators, and for the alignment of digital technologies with environmental targets.

Overall, however, both culture and technology have the potential to open up opportunities for sustainable and inclusive recovery. According to a recent analysis by the Organisation for Economic Co-operation and Development (OECD), cities and regions should consider the cultural and creative sectors and cultural participation as drivers of both economic and social advancement.

Anticipatory policy-making

When it comes to maximising the effectiveness of the contribution of technology and culture, anticipatory policy-making is key. In recent years, future-oriented strategic thinking has proliferated in new cultural domains, including architecture, design, and heritage. Foresight in these cultural domains can help policy-makers design policies to aid the green recovery. As far as the technology–culture nexus is concerned, anticipatory policy-making could explore the following three areas:

Citizen-centred approach: People’s collective memories, beliefs and attitudes to particular aesthetics constitute what anthropologists call ‘tacit culture’. It functions as a link between function and form, and appears to transcend political preference, age and ethnicity. For example, a recent poll suggests that 75 % of Americans prefer a classical style in public buildings, whereas only 25 % prefer a modernist style. Another study examining the views of hospital patients in Europe and Japan showed a consensus across countries on what people consider important in terms of the aesthetics of a hospital environment. This means that when working out policies on how buildings should be retrofitted to be rendered climate-neutral and how cities need to change according to circular economy principles, policy-makers would benefit from considering these tacit cultural trends and consulting widely on citizens’ aesthetic preferences. A useful step towards citizens’ dialogues would be the inclusion of cultural and heritage-oriented themes in the Conference on the Future of Europe process.

Strategic foresight and impact assessment: As EU policy-making is currently undergoing a profound embedding of foresight and impact assessment into its workings, culture remains somewhat overlooked. The current Better Regulation guidelines list environmental, social and economic impacts as the most important, with increased attention being directed towards the impact on the United Nations sustainable development goals (SDGs). As culture and heritage are at the heart of the SDGs, there is room for a more robust assessment of European added value in the fields of culture and heritage. Useful work has been done in this regard by Unesco, putting heritage impact assessment (HIA) on a par with the more widely used environmental impact assessment (EIA). The impact of new technologies in the cultural sector, such as digitalisation and AI, could be improved with the aid of such assessment.

Culture as innovation: As the OECD report notes, cultural institutions have difficulties gaining recognition as an innovative sector and accessing support measures that are typically reserved for more technological forms of innovation. While many innovations in the cultural sector do include technology – digitalisation, for example – there are other forms of innovation that are based on creative content. Examples include projects in which citizens ‘adopt’ a monument, social-media projects that popularise sustainable renovation and cultural heritage among young people, and grassroots non-profit cooperatives that promote and facilitate the salvage and reuse of construction materials.


Read this ‘at a glance’ on ‘What if technology and culture combined to boost a green recovery?‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/24/what-if-technology-and-culture-combined-to-boost-a-green-recovery/

The public sector loan facility under the Just Transition Mechanism [EU Legislation in Progress]

Written by Sidonia Mazur and Christiaan Van Lierop (1st edition),

© small smiles / Adobe Stock

The public sector loan facility is the third pillar of the Just Transition Mechanism (JTM), along with the Just Transition Fund and just transition scheme under Invest EU. The facility will consist of a grant and a loan component. With the contribution of €1.525 billion for the grant component from the Union budget and EIB lending of €10 billion from its own resources, the aim is for the public sector loan facility to mobilise between €25 and 30 billion in public investment over the 2021-2027 period. Funding will be available to all Member States, while focusing on the regions with the biggest transition challenges. In the European Parliament, the Committee on Budgets (BUDG) and the Committee on Economic and Monetary Affairs (ECON) have joint responsibility for this file. Their report was adopted at a joint sitting of the two committees on 16 October 2020. Parliament subsequently confirmed the committees’ mandate to open trilogue negotiations.

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Source Article from https://epthinktank.eu/2020/12/23/the-public-sector-loan-facility-under-the-just-transition-mechanism-eu-legislation-in-progress/

UK trade agreements with third countries: Implications for the EU

Written by Issam Hallak,

© Rawf8 / Adobe Stock

The United Kingdom (UK) left the European Union (EU) on 1 February 2020 and will regain competence for its own international trade policy as soon as the transition period concludes at the end of 2020. Freedom to determine its own trade relationships was a major reason for the UK’s withdrawal from the EU: its new international trade policy is based on the goal of establishing ‘global Britain’, a country asserting that it is strongly committed to trade openness with international leadership.

To this end, the UK has concluded as many continuity agreements as possible, in order to roll over existing EU free trade agreements (FTAs), such as that with South Korea. It has also renegotiated, rather than simply roll over, the provisions of EU FTAs, with partners who so demanded, including Japan. Beyond those countries with EU FTAs to which the UK has been party, it has expanded the range of its FTA negotiations to Australia, New Zealand and the United States (US), three of its major trading partners. When it comes to geographic scope, the UK has set the Pacific as a high priority, its objective being to access the newly established Comprehensive and Progressive Trans-Pacific Partnership (CPTPP). In addition, the UK aims to use its advantage in digital trade and services to become a ‘world digital trade powerhouse’, and has stressed that FTA provisions should promote digital trade and foster regulatory cooperation in the field.

The EU represents 50 % of the UK’s total trade, and the UK economy is integrated with and reliant on the EU. Therefore, although the UK is facing obstacles in signing trade agreements, its new strategy has a number of implications for the EU. The UK is committed to remaining an open country with respect to international trade and its focus on digital trade and services, which depend less on geography, is seen as a way to diversify away from the EU.


Read the complete briefing on ‘UK trade agreements with third countries: Implications for the EU‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/23/uk-trade-agreements-with-third-countries-implications-for-the-eu/

What if artificial intelligence in medical imaging could accelerate Covid-19 treatment?

Written by Mihalis Kritikos,

© Adobe Stock

Artificial intelligence (AI) solutions can help radiologists with the triage, quantification and trend analysis of patient data. AI-powered medical imaging is already used to detect critical diseases, and medical imaging has played a significant role in the fight against Covid-19, easing the pressure on healthcare systems. Although AI imaging as a diagnostic tool is still surrounded by various challenges and uncertainties, its use in the context of Covid-19 has assisted clinicians with its faster image-processing times – as little as 10 seconds compared with up to 15 minutes for a manual reading of a computerised tomography (CT) scan.

Medical imaging has always been one of the most advanced areas of AI application showing remarkable accuracy and sensitivity in the identification of imaging abnormalities. In the context of Covid-19, medical imaging has facilitated incidental diagnosis, offering supporting evidence in clinical situations where false negative RT-PCR tests are suspected and helping evaluate treatment outcomes, disease progression and anticipated prognosis.

AI-empowered image processing can automate searches through large databases and deliver more precise demarcation of infections in X-ray and CT images, facilitating fast evaluation of CT scans and identification of Covid-19 findings. Clinicians and radiologists can use machine learning (ML) algorithms to examine information contained in medical scans or images as these provide better tools for localisation and quantification of disease features. The result can be better early detection, diagnostic performance and prognostic value while also easing the burden on laboratory testing. Could these AI systems, with their several advantages, replace human (medical) judgement in the context of the Covid-19 pandemic?

Potential impacts and developments

AI-supported medical imaging can be vital in the fast detection and classification of Covid-19, as it can immediately flag chest CT scans showing suspected Covid-19 allowing the patients concerned to be tested promptly. Image recognition algorithms can bring together and analyse chest CT scan findings, clinical symptoms, exposure patterns and other forms of testing, thus providing clinicians and clinical decision-making systems in general with essential information.

Using well-curated medical imaging data, AI algorithms can be developed, trained and validated so as to anticipate possible clinical deterioration or improvement. These evidence-based predictions could in effect help hospitals plan workflow in an emergency context such as the current one. They would provide consistent, quantifiable information to evaluate precisely the gravity of a patient’s illness, enabling medical personnel to effectively triage patients and thus alleviate the ever-growing patient backlog.

Recently, a new algorithm has been developed combining CT images of patients’ lungs with non-imaging data to identify Covid-19-positive patients who require immediate intervention; another, meanwhile, offers an automated tool for rapid identification of patients with suspicious chest imaging for isolation and further testing.

AI can reduce the time taken in the medical imaging process by examining thousands of images from a chest CT scan. It can also increase patient safety by improving X-ray exposure parameters and producing low-dose CT scans. AI-empowered visual sensors can meanwhile accelerate scanning, automate risk stratification and, in effect, reduce unnecessary radiation exposure in the clinical setting.

AI medical imaging models have been deployed in a number of hospitals around the world. The US Food and Drug Administration (FDA) has authorised the use of AI algorithms that detect Covid-19 in partially imaged lungs as an incidental finding, whereas the EU is funding the Imaging Covid-19 AI initiative, a multi-centre European project, to enhance the use of CT in the diagnosis of Covid-19 by using AI. Last but not least, a group of Belgian hospitals have recently developed the first CE-marked AI solution for CT that offers fast quantification of lung pathology on chest CT scans in Covid-19 patients. Are these initiatives sufficient to cope with the current needs for safe and accurate AI-powered diagnostic tools? Or are more multicentre and multidisciplinary clinical studies needed to address the current knowledge gaps?

Anticipatory policy-making

Along with great benefits, the introduction of AI to medical imaging also raises a significant number of legal questions and ethical considerations. The deployment of AI in the context of the current pandemic is also subject to numerous challenges that could undermine the accuracy and usefulness of its eventual clinical findings. These relate to the overall gap in knowledge of the long-term effects of Covid-19 and the lack of historical data to enable training on large-scale prognosis data. The result is the over-use of small incomprehensive public datasets and a combined lack of robustness and interpretability of AI models in clinical practice. One additional important diagnostic challenge lies with the non-specificity of Covid-19 patterns and their differentiation from non‑Covid‑19 viral pneumonia or asymptomatic patients with unaffected lungs.

The primary challenge in this context is that of accessing large volumes of data for AI development and the lack of representative data to train and validate algorithms. As the effectiveness of AI-supported devices relies on the accuracy of training data, grounding modelling and clinical decisions on sub-optimal data may compromise accuracy and reliability and result in deficient medical diagnoses. In fact, the accelerating development of AI‑based diagnostic tools in response to the current pandemic has brought to the fore the absence of standardised protocols for training and validating ML algorithms in this domain and the lack of large and diverse image datasets from a variety of certified sources, as required to train the algorithm.

The training, testing and eventual validation of AI-based algorithms for use in the current public health crisis requires access to large and curated datasets developed in accordance with existing privacy norms and data protection rules. The absence of such large datasets means AI-supported medical scans may be biased by technical factors owing to subtle differences in data from different scanning techniques or ill-curated data that train algorithms and deep networks on Covid-19. The integration of AI techniques in radiology in this particular context also raises questions about the ethics of the procedures and protocols followed for collecting and processing this medical data, including issues of informed consent, privacy and data ownership.

Under the General Data Protection Regulation, patients must give prior informed explicit consent for the use of their medical scans and imaging data in developing an AI algorithm, and this must be renewed before the design and training of each new version. Is that plausible given the time pressure to deliver fast clinical findings and solutions? The grounding of diagnostic decisions on AI-powered processing also raises liability questions: who should be held liable for an ineffective medical diagnosis? The doctor or the software developer?

Under the EU Medical Devices Regulation, a radiologist could be held liable if they depart from the AI-powered diagnostic medical imaging equipment’s diagnosis. However, the question is whether or not these AI algorithms have been subject to the same rigorous pre-market authorisation and auditing standards followed for the assessment and eventual deployment of other medical devices? Or have these regulatory procedures been fast-tracked owing to the urgent demand for Covid-19 related diagnostic solutions?

Beyond the issue of the availability, quality and representativeness of the datasets used to train algorithms, the majority of hospitals lack the technological infrastructure, manpower and knowhow to manage these complex AI systems effectively, since most of them use outdated computer-assisted diagnostics tools or only perform visual checks on medical scans.

Uptake of image recognition AI in medical diagnostics currently sits between 1 and 20 % depending on the disease area. Consequently, the use of AI-powered imaging in resource-limited settings remains a major technological and policy challenge that must be addressed as a matter of urgency, not least because of its potential benefits in boosting public health systems’ capacity to cope with the current extraordinary global health crisis.


Read this ‘at a glance’ on ‘What if artificial intelligence in medical imaging could accelerate Covid-19 treatment?‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/23/what-if-artificial-intelligence-in-medical-imaging-could-accelerate-covid-19-treatment/

Workplace monitoring in the era of artificial intelligence

Written by Mihalis Kritikos,

© Adobe Stock

Workers’ interests should always be at the forefront of company approaches to privacy and data protection and worker representatives must always be consulted when a new technology is considered for workplace operations and analytics. This was one of the main conclusions of the study ‘Data subjects, digital surveillance, AI and the future of work’, which was carried out by Professor Phoebe Moore of the University of Leicester at the request of the STOA Panel, following a proposal from Lina Galvez Munoz (S&D, Spain), member of the Panel. This new STOA study provides a timely, in depth overview of the social, political and economic urgencies in identifying what we call the ‘new surveillance workplace’.

A wide range of technologies are gradually being introduced to monitor, track and, ultimately, surveil workers. Workplace surveillance is age-old, but it has become easier and more common, as new technologies enable more varied, pervasive and widespread monitoring practices and have increased employers’ ability to monitor apparently every aspect of workers’ lives. New technological innovations include surveillance cameras and keylogging software on work laptops to biometric sensors and GPS tracking, micro-chip implants, automated video pattern recognition and biometric access control.

Digital transformation, work design experimentation and new technologies are, indeed, overwhelming methods with intensified potential to process personal data in the workplace. New issues are emerging to do with ownership of data, power dynamics of work-related surveillance, usage of data, human resource practices and workplace pressures in ways that cut across all socio-economic classes.

The current pandemic has expanded the use of AI-empowered real-time work place monitoring systems and workforce analytics software that quantifies the previously un-measurable factors for team success, like collaboration and communication that are essential for productivity and performance. During the last few months, workplace monitoring appears to be stress-inducing, demotivating and dehumanising, leading to phenomena of presenteeism, a growing datafication of employment and the blurring of the boundaries between public and private spheres. Such technological practices threaten to alter workplaces in fundamental ways and to undermine trust between employers and employees.

How are institutions responding to the widespread uptake of new tracking technologies in workplaces, from the office, to the contact centre, to the factory? What are the parameters to protect the privacy and other rights of workers, given the unprecedented and ever-pervasive functions of monitoring technologies? The report evidences how and where new technologies are being implemented; looks at the impact that surveillance workplaces are having on the employment relationship and on workers themselves at the psychosocial level; and outlines the social, legal and institutional frameworks within which this is occurring, across the EU and beyond, ultimately arguing that more worker representation is necessary to protect the data rights of workers.

The study carries out a thorough analysis of automated decision-making, considering the extent to which it is admissible, the safeguard measures to be adopted, and whether data subjects have a right to individual explanations. It then considers the extent to which the General Date Protection Regulation (GDPR) provides for a preventive risk-based approach, focused on data protection by design and by default. In adopting an interdisciplinary perspective, the study identifies all major tensions between the traditional data protection principles — purpose limitation, data minimisation, special treatment of ‘sensitive data’, limitations on automated decisions — and the full deployment of the power of AI and big data. The vague and open-ended GDPR prescriptions are analysed in detail regarding the development of AI and big data applications. The analysis sheds light on the limited guidance offered by the GDPR on how to balance competing interests, which aggravates the uncertainties associated with the novel and complex character of new and emerging AI applications. As a result of this limited guidance, controllers are expected to manage risks amidst significant uncertainties about the requirements for compliance and under the threat of heavy sanctions.

It should be noted that the author makes several interesting findings, including the rapid increase of employees’ stress and anxiety as well as the augmented accuracy of tracking and monitoring technologies, but also the marginal role that the concept of consent and the workers’ representatives has so far exerted in the frame of the relevant technological and policy debates. The study’s added value lies not only in the detailed legal analysis but also in its methodological rigour: its findings are based on a wide range of country case studies and ‘worker cameos’ that are based on semi-structured interviews carried out with a series of workers to identify where electronic performance monitoring (EPM) and tracking are occurring. The study then proposes a wide range of concrete and applicable policy options about how to ensure union/worker involvement at all stages, how to introduce and enforce co-determination into labour law in all EU Member States, how to require businesses to compile certification and codes of conduct and how to prioritise collective governance. The study emphasises the need to guarantee worker representatives’ involvement at each increment of the life cycle of any technological tracking procedure and for EU states to establish co-determination rights in a firm manner. The author’s proposal concerning full inclusion – beyond trade unions – of employer associations in writing codes of conduct for data tracking and processing activities as partners is of practical importance. The arguments and findings of the study offer both theoretical insight and practical suggestions for action that policy-makers will hopefully find stimulating and worth pursuing.

Read the full report and accompanying STOA Options Brief to find out more. You can also watch the video of the presentation of interim findings to the STOA Panel.

Source Article from https://epthinktank.eu/2020/12/22/workplace-monitoring-in-the-era-of-artificial-intelligence/

What if AI-powered passenger locator forms could help stop the spread of Covid-19?

Written by Mihalis Kritikos,

© Adobe Stock

Asking passengers to complete a passenger locator form (PLF) prior to their flights has been seen in recent months as an efficient way to help public health authorities trace travellers potentially exposed to Covid-19 in airports and ports and at other border check points. This digital identification form, which has increasingly been viewed as an essential travel document, could become a key health measure in the context of contact tracing and targeted testing, helping Member States perform risk assessments of arrivals. The accelerating use of this hybrid contact-tracing system in several European airports raises issues of transparency, accountability and privacy that need to be addressed in an efficient and responsible manner.

Several Member States have developed a screening procedure that allows them both to perform targeted testing and strengthen their contact-tracing efforts. According to this procedure, travellers are obliged to fill out a form online at least 24 hours before entering the country. This standardised form has been developed jointly by the World Health Organization (WHO), the International Civil Aviation Organization (ICAO) and the International Air Transport Association (IATA). The form contains essential location information about the traveller’s visit including personal and travel details, such as their country of origin and the countries they have visited in the last 15 days, the accommodation they will stay in and their family status. Once the PLF is completed, the details of the tracking form are processed by special software for the creation of risk profiles and the categorisation of travellers. Data are evaluated by machine-learning algorithms that produce a unique quick response (QR) code. This code is sent to the passenger, who shows it either in print or on their smartphone upon their arrival in the country. The availability of machine learning as a special kind of artificial intelligence (AI) application is essential for the widespread and effective use of PLFs.

The availability of passenger locator data is crucial for the success and effectiveness of contact-tracing operations and the strengthening of countries’ capacity to combat Covid-19 at points of entry. The ICAO Guidelines for States concerning the management of communicable disease posing a serious public health risk state that the PLF ‘provides an appropriate method of rapidly collecting traveller contact information’. In comparison to other contact-tracing methods and static controls, the main advantage of this screening model lies in its capacity to facilitate targeted screening of travellers at borders and analyse real-time data to allocate resources. The selection of who should be tested is based on an algorithmic analysis of the data contained in the PLFs. This AI-based system should take into account, for instance, the passenger’s risk profile, the number of tests available and Covid-19 hospital beds available, the number of flights arriving and the epidemiological situation and transmission patterns in the country of departure.

What are the main advantages of this newly introduced system compared with other contact-tracing applications? Can countries rely on this particular system to control the spread of the disease despite the technical limitations? Does this sampling tool deter people from travelling abroad or even strike the right balance between the need to restore economic activities while protecting the health of passengers and local people alike? What kind of legal safeguards are needed for the responsible deployment of this screening tool, whose operation is based on the processing of travellers’ data by newly formed algorithmic models?

Potential impacts and developments

Several EU countries have recently introduced targeted Covid-19 testing of foreign travellers arriving at their borders. The form is currently required in most EU countries. Greece was the first country to use dynamic machine-learning algorithms to create a real-time dashboard to organise its diagnostic testing system at its borders. This is done through an AI system called EVA, which uses real-time data and optimisation techniques to perform risk predictions and allocate testing resources within the framework of Greece’s current Covid-19 screening capacity. Given the limited laboratory testing capacity of several airports, the lack of available large-scale testing kits and of health providers who could administer the tests and validate the results within a limited time-frame, this smart processing of PLFs may facilitate efficient resource management. The screening system can also supplement traditional contact-tracing procedures, as the data contained in the form can help authorities trace the contacts of all travellers, should a fellow passenger be confirmed as having tested positive for Covid-19.

At the same time, the processing of the data contained in PLFs raises several issues about their compliance with the relevant data protection standards and whether and how informed consent requirements can be met given that it is mandatory to complete this form when travelling to certain countries. In addition to the challenges associated with the management of huge quantities of travellers’ data, the efficiency of the PLF system may be undermined by incorrect phone numbers and other false or inaccurate information provided by travellers. How can travellers who provide inaccurate contact details be traced? Furthermore, the performance of targeted testing on the basis of data collected and processed using algorithmic models that are still under development carries the risk of errors. This could, for instance, pose the risk of public identification or stigmatisation of confirmed or suspected individuals. Finally, the usefulness of the system’s deployment from a public health perspective will depend not only on its actual technical effectiveness but also on whether its use can be combined with efficient diagnostic tests, other contact-tracing tools and comprehensive monitoring schemes.

Anticipatory policy-making

Public health authorities should collect and process the personal data from the PLFs for targeted testing in accordance with Regulation 2016/679, and the privacy framework and recommendation on health data governance of the Organisation for Economic Co-operation and Development. The European Centre for Disease Prevention and Control has issued Considerations relating to passenger locator data, entry and exit screening and health declarations and proposes collecting a minimum data set, the rest of the data to be obtained during the contact-tracing interview. As United Nations experts have stressed, ’emergency responses to the coronavirus must be proportionate, necessary and non-discriminatory’. The proportionate use of location data should consider the medical relevance of the data collected and safeguard its effective anonymisation and storage limitation, so as to prevent accidental disclosure of names of possibly infected persons.

Given the transnational nature of this public health emergency, as passengers travel across Europe, the data contained in PLFs and the results of testing and contact-tracing efforts could be collected in a common European database. Τhe Joint European roadmap towards lifting Covid-19 containment measures recognises that coordinated action between Member States should include actions to gather harmonised data, harmonise protocols, and share reference standards. The Commission’s recommendation on a common Union toolbox for the use of technology and data to combat the Covid-19 crisis strongly advocates the development of a common methodology on monitoring and sharing assessments of the effectiveness of contact-tracing applications. The European Commission recently announced that it is preparing, in collaboration with EASA and the EU Healthy Gateways joint action, to launch a common EU digital PLF as one of a number of measures to facilitate safe travel in the post-Covid-19 era, to be available by the end of 202010 January 2021. EU-wide initiatives may enhance the overall efficacy of the PLF system as an EU screening tool and minimise possible overlaps and inconsistencies.

However, the gradual deployment of this tool in European airports should be treated with caution given the limited scientific knowledge and technical experience in relation to the effectiveness, thoroughness and credibility of algorithmic decision-making systems of this kind. Τhere are questions about the type and quality of data used for the development and operation of the algorithms, and the rigour of the testing and operational protocols used for their design and deployment. Thus, there is an immediate need for algorithmic impact assessments to improve the quality, explainability and transparency of these screening procedures. As decisions about who should get tested in an airport are important from both public health and privacy perspectives, contact-tracing and targeted testing based on PLFs should be subject to thorough validation and accountability requirements so as to gain public trust and acceptance. Last but not least, the deployment of this screening system should remain part of a wider public health emergency response that needs to consider the essential nature of air travel in the context of Covid-19 and be constantly monitored by public health and data protection authorities alike given the novelty of the technology being used.


Read this ‘at a glance’ on ‘What if AI-powered passenger locator forms could help stop the spread of Covid-19?‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/22/what-if-ai-powered-passenger-locator-forms-could-help-stop-the-spread-of-covid-19/

Citizens’ enquiries on the rule of law and EU funding

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Citizens often send messages to the President of the European Parliament (or to the institution’s public portal) expressing their views on current issues and/or requesting action from the Parliament. The Citizens’ Enquiries Unit (AskEP) within the European Parliamentary Research Service (EPRS) looks into these issues and replies to the messages, which may sometimes be identical as part of wider public campaigns.

The President of the European Parliament has recently received a large number of messages urging the Parliament to take a strong stand in favour of the mechanism making the allocation of European Union (EU) funds conditional on the respect of the rule of law. In its conclusions of 11 December 2020, the European Council stated that the Commission should not launch procedures under the rule of law mechanism until the European Court of Justice has made a decision on whether to annul the instrument. Citizens first began to write to the President on this subject in December 2020. They expressed concerns about delays in the implementation of the rule of law mechanism and about the deterioration of human rights and the rule of law in Poland and Hungary. In its resolution on the rule of law, the European Parliament stressed that the European Council conclusions are ‘superfluous’. The agreement between the European Parliament and Council clearly states that it will apply from 1 January 2021.

Please find below the main points of the reply sent to citizens who took the time to write to the President of the European Parliament on this matter.

Main points made in the reply in English

On 16 December 2020, after the European Parliament gave its consent to the Multiannual Financial Framework (MFF), the President of the European Parliament David Maria Sassoli stated: ‘For the first time in the history of our Union we have ensured that the resources of the European budget are conditional on respect for the rule of law and democracy throughout Europe.’

Furthermore, on 17 December 2020, the European Parliament adopted a resolution on the MFF, Rule of Law Conditionality and Own Resources. Parliament stresses that the 11 December European Council Conclusions, which state that the Commission should not apply the Rule of Law mechanism until the European Court of Justice has made a decision on whether to annul the instrument, are ‘superfluous’. The agreement between European Parliament and Council clearly states that it will apply from 1 January 2021. More information is available in this press release.

In a speech to the European Parliament plenary on 16 December 2020, the President of the European Commission Ursula von der Leyen also underlined that: ‘In essence, as I understand it, there is a fear that the application of the regulation will be delayed and that justice delayed might be justice denied. This will not happen. The regulation will apply from 1 January 2021 onwards. And any breach that occurs from that day onwards will be covered.’

As to the legislation on the rule of law mechanism specifically, on 16 December 2020 also, the European Parliament approved the regulation on the protection of the Union’s budget in case of generalised deficiencies as regards the rule of law in Member States.

The European Parliament tightened the regulation in the negotiations held with EU countries. For instance, the new law does not only apply when EU funds are misused directly, such as cases of corruption or fraud. It will also apply to systemic breaches of fundamental values that all EU countries must respect, such as democracy or the independence of the judiciary, when those breaches affect – or risk affecting – the management of EU funds. The European Parliament also succeeded in securing a specific provision that clarifies the possible scope of the breaches by listing examples of cases, such as threats to the independence of the judiciary, failure to correct arbitrary/unlawful decisions, and limiting legal remedies.

The European Parliament debate of 16 December 2020 on the Conclusions of the European Council, MFF, Rule of Law Conditionality and Own Resources is publicly available. More information on the rule of law mechanism is available in the EP press release, and in the Legislative Observatory and the Legislative Train databases. Moreover, the full text of the Council’s common position as negotiated and agreed by the European Parliament is available online

Specifically on Poland, the European Parliament adopted a resolution on 17 September 2020, on determination of a clear risk of a serious breach by Poland of the rule of law. Parliament expressed concerns regarding the legislative and electoral system, the independence of the judiciary, and fundamental rights in Poland. It strongly deplored both the ‘Polish Stonewall’ mass arrest and subsequent treatment of 48 LGBTI activists on 7 August 2020, and the Polish Episcopate’s official position in favour of ‘conversion therapy’. More information is available in this press release.

On Hungary, on 16 January 2020, the European Parliament adopted a resolution on ongoing hearings under Article 7(1) of the Treaty on European Union regarding Poland and Hungary. Parliament stated that there is a clear risk of a serious breach by Hungary of the values on which the Union is founded. Parliament is concerned about breaches of the independence of the judiciary, freedom of expression, including media freedom, freedom of the arts and sciences, freedom of association and the right to equal treatment. Further information is available in this press release.

Finally, the rule of law is one of the fundamental values of the Union, enshrined in Article 2 of the Treaty on European Union. In this light, the European Commission published the 2020 Rule of Law Report on 30 September 2020. The report includes chapters on all EU countries including Poland and Hungary.

Source Article from https://epthinktank.eu/2020/12/22/citizens-enquiries-on-the-rule-of-law-and-eu-funding/

Plenary round-up – December 2020

Written by Katarzyna Sochacka and Clare Ferguson,

© European Union 2020 – Source : EP/Alexis HAULOT

The December 2020 plenary session focused on the agreement on EU finances for the coming years, as well as the conclusions of the 10‑11 December European Council meeting. Members debated future relations between the European Union and the United Kingdom, and adopted first-reading positions on temporary contingency measures on air and road connectivity, fisheries and aviation safety, to come into force should no agreement be reached with the UK by the end of this year. Members also discussed the preparation of an EU strategy on Covid‑19 vaccination, including its external dimension, an EU Security Union strategy and a dedicated Council configuration on gender equality. Members discussed the European Citizens’ Initiative, Minority Safepack, seeking to protect minority languages and cultures. Vice-President of the Commission/High Representative of the Union for Foreign Affairs and Security Policy, Josep Borell made statements on recent developments in the Eastern Partnership, on the situation in Mozambique and on the 25th anniversary of the Barcelona Process and the Southern Neighbourhood.

Sakharov Prize

In a formal ceremony, Parliament awarded the 2021 Sakharov Prize to the democratic opposition in Belarus, represented by the Coordination Council, for its peaceful role in opposing the falsification of the August 2020 elections.

Multiannual Financial Framework 2021-2027

Following agreement at the European Council meeting of 10‑11 December, Parliament approved, by large majority, the EU’s 2021-2027 Multiannual Financial Framework (MFF). An Interinstitutional Agreement on budgetary matters, setting out a timetable for the introduction of new own resources, was also approved. Adopted by the Council the following day, the MFF Regulation enters into force on 1 January 2021. The overall ceiling for the MFF is €1074.3 billion (2018 prices), which will be complemented by the €750 billion recovery plan (€390 billion in grants). Financing of the Next Generation EU recovery plan will be made possible by an unprecedented own resources decision authorising the Commission to borrow on markets.

EU general budget 2021

The fruit of the agreement found on 4 December during the budgetary conciliation between Parliament and the Council on the first draft budget for 2021, Members adopted the EU general budget for 2021, voting on the Council’s position (adopted without amendment) on the second draft EU general budget for 2021. Commitment appropriations for 2021 will amount to €164.2 billion and payments to €166.1 billion. The annual budgetary negotiations this year were both complex and delayed, due to the late agreement on the 2021‑2027 MFF, as well as the coronavirus crisis.

Parliament also voted on Draft Amending Budget No 10/2020, to increase EU payment appropriations in 2020, in line with updated forecasts of expenditure and other adjustments to expenditure and revenue. The limited expenditure adjustments proposed allow increased payment appropriations of €1 569.3 million for the European Agriculture Guarantee Fund and certain decentralised agencies.

Transitional rules for support from the common agricultural policy (CAP)

Based on a Parliament proposal and in view of the lengthy negotiations on the EU budget and agricultural policy post‑2020, the European Commission put forward rules aimed at ensuring continuity of EU support for farmers and rural areas. Parliament therefore debated and voted on transitional rules for support from the common agricultural policy (CAP), extending current EU farm policy until the new CAP framework is in place at the end of 2022 and allowing greater focus on environment and climate measures.

REACT-EU

Confirming the compromise agreement reached on the MFF and the considerable funding measures already agreed to combat the coronavirus crisis, Members adopted, by an overwhelming majority, the regulation establishing REACT‑EU. This is intended to mobilise €47.5 billion, offering Member States the flexibility to use EU funds to address the challenges faced by the sectors hardest hit by the pandemic, such as health care, tourism and culture. The funding should support social cohesion and climate objectives.

European Commission implementing powers

Following repeated difficulties and controversy over authorisation of pesticides and genetically modified organisms, the Commission proposed changes to the procedures involved, to encourage Member States to take greater responsibility for decisions in such cases. Members voted on a revised Regulation on the Commission’s implementing powers (Comitology Regulation), aimed at eliminating ‘no-opinion’ deadlock situations in the appeal committee and increasing the transparency of the procedure.

Water legislation

In a joint debate, Members discussed the legislation that ensures that Europeans have access to safe drinking water (particularly on tap, rather than in bottles). Following this debate, Parliament adopted the revised Drinking Water Directive and an associated resolution, changes that come as a direct result of the European citizens’ initiative ‘Right2Water’. The updated rules address concerns about endocrine disruptors, pharmaceuticals and microplastics by introducing a watch-list mechanism, to begin within one year. However, Parliament remains keen to see water quality further improved through other related policies.

European Public Prosecutor’s Office and European Anti-fraud Office

Members debated and voted on the early second-reading agreement reached in trilogue negotiations on the revised Regulation on the European Anti-fraud Office (OLAF), to provide for cooperation with the new European Public Prosecutor’s Office (EPPO). The EPPO will provide an independent and decentralised EU office to investigate, prosecute and bring crimes to judgment. However, this means that the relationship between the EPPO and OLAF needs to be carefully codified in the rules of both organisations, based on principles of close cooperation, information exchange, complementarity and non-duplication.

Nomination to the Court of Auditors

In a secret vote, Members issued a negative opinion on the nomination of Marek Opioła as the Polish member of the Court of Auditors, to replace Janusz Wojciechowski, now EU Commissioner for agriculture. While Parliament’s decision is not legally binding on the Council, the Budgetary Control Committee nevertheless examines each candidate on their ability to perform their duties in complete independence and in the general interest of the EU. The publicity surrounding these hearings and the questionnaire answered by the candidates therefore makes it difficult (but not impossible) for the Council to over-ride any negative opinion delivered by Parliament.

European Year of Rail

Members voted on a provisional agreement on a proposal to designate 2021 as the European Year of Rail, reflecting the EU’s climate ambition, as rail is a highly energy-efficient transport mode, and helping to boost passenger and goods traffic.

Opening of trilogue negotiations

Members confirmed five mandates for negotiations from the Civil Liberties, Justice and Home Affairs Committee: on the proposal for a regulation on European Production and Preservation Orders for electronic evidence in criminal matters; on the proposal for a regulation establishing the conditions for accessing the other EU information systems; on the proposal for a regulation establishing the conditions for accessing other EU information systems for ETIAS purposes; on a proposal for a regulation laying down harmonised rules on the appointment of legal representatives for purpose of gathering evidence in criminal proceedings; and on a proposal for a regulation on a temporary derogation from certain provisions on the use of technologies by number-independent interpersonal communications service providers for the processing of personal and other data for the purpose of combating child abuse online.


Read this ‘at a glance’ on ‘Plenary round-up – December 2020‘ in the Think Tank pages of the European Parliament.

Source Article from https://epthinktank.eu/2020/12/21/plenary-round-up-december-2020/