"Towards Value-Centric Big Data" e-SIDES Workshop - Slide-deck
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This document summarizes a workshop session on value-centric big data. The session included presentations on several projects aiming to develop big data technologies and applications in an ethical manner that respects privacy and provides value. Speakers discussed challenges around privacy, profiling, and ensuring equitable and fair treatment when using big data. They also shared proposed solutions and best practices for addressing these challenges developed through their projects. The session concluded with an open discussion on experiences and approaches to overcoming barriers to responsible big data innovation.
"Towards Value-Centric Big Data" e-SIDES Workshop - Slide-deck
- 1. Towards Value- Centric Big Data e-SIDES Workshop Session
- 2. 11.30 Welcome and Introduction Stefania Aguzzi, e-SIDES 11.35 e-SIDES: Towards Value-Centric Big Data: Community Position Paper Daniel Bachlechner, e-SIDES 11.45 Mentimeter Question Stefania Aguzzi, e-SIDES 11.50 Safe-DEED Vision & Use Cases Ioannis Markopoulos, Director, Innovation & Project Management ForthNet, Safe-DEED 12.00 Track&Know: At the edge of GDPR: PII and Driver Profiling Dr Aggelos Liapis 12.10 A value-centric & privacy respectful Big Data initiative in the PSPS domain: AEGIS project and its Ethics White Paper Marina Da Bormida, R&I Lawyer and Ethics Expert AEGIS 12.20 Lynx: Beyond Regulatory Compliance? Ethical Considerations about Language Resources in the H2020 Prêt-à-LLOD project Víctor Rodríguez Doncel Elena Montiel 12.30 Panel session Richard Stevens, e-SIDES Which types of challenges faced in the context of your project / in your experience with RRI for big data and analytics and types of solution approaches as opportunity to overcome challenges Tour de table 12.55 Mentimeter results Stefania Aguzzi e-SIDES 13.00 End of session The value you’ll get today
- 3. e-SIDES: Towards Value-Centric Big Data Community Position Paper
- 4. e-Sides Ethical and Societal Implications of Data Sciences 4 Key questions What is e-SIDES doing for value- centric big data? What’s the Community Position Paper and how does it help? Who is the community and how can I contribute? What do we already have and what are the next steps?
- 5. e-Sides Ethical and Societal Implications of Data Sciences 5 Key questions What is e-SIDES doing for value- centric big data? What’s the Community Position Paper and how does it help? Who is the community and how can I contribute? What do we already have and what are the next steps?
- 6. e-Sides Ethical and Societal Implications of Data Sciences 6 What is e-SIDES doing... 1) Identify ethical and societal issues 2) Identify existing technologies 3) Assess existing technologies 4) Conduct a gap analysis 5) Identify design requirements 6) Assess solutions under development 7) Identify implementation barriers 8) Make recommendations What? ▪ Liaise with researchers, business leaders, policy makers and civil society through community events ▪ Provide an Internet-based meeting place for discussion, learning and networking ▪ Provide an agreed-upon and collective community position paper and recommendations How? Why? ▪ Reach a common vision for an ethically sound approach to big data and facilitate responsible research and innovation in the field ▪ Improve the dialogue between stakeholders and the confidence of citizens towards big data technologies and data markets
- 7. 7 ...for value-centric big data? Data-driven innovation that at the same time provides opportunities for economic added value and respects ethical and societal values is what e-SIDES aims at.
- 8. e-Sides Ethical and Societal Implications of Data Sciences 8 Key questions What is e-SIDES doing for value- centric big data? What’s the Community Position Paper and how does it help? Who is the community and how can I contribute? What do we already have and what are the next steps?
- 9. 9 What is the CPP... The Community Position Paper is a document on responsible data-driven innovation written by and for the big data community. 3) Challenges 1) Introduction 2) Stakeholders 4) Opportunities Structure 5) Conclusion
- 10. 10 ...and how does it help? The Community Position Paper ▪ provides a solid decision basis. ▪ documents best practices. ▪ highlights necessary actions. ▪ drives a lively debate.
- 11. e-Sides Ethical and Societal Implications of Data Sciences 11 Key questions What is e-SIDES doing for value- centric big data? What’s the Community Position Paper and how does it help? Who is the community and how can I contribute? What do we already have and what are the next steps?
- 12. 12 Who is the community... Industry Academia Policy makers Standards bodies Trade associations Investors Civil society Data protection authorities
- 13. 13 ...and how can it contribute? Contribute online and offline.
- 14. e-Sides Ethical and Societal Implications of Data Sciences 14 Key questions What is e-SIDES doing for value- centric big data? What’s the Community Position Paper and how does it help? Who is the community and how can I contribute? What do we already have and what are the next steps?
- 15. 15 What do we already have... 3) Discrepancy between legal compliance and ethics 1) Differences in attitudes and contexts 2) Empowerment vs. cognitive overload Challenges 4) Difficulties of conducting assessments Opportunities 3) Reference points of accountability 1) Raising awareness and increasing transparency 2) Tools of accountability 4) Bodies and mechanisms of oversight
- 16. 16 ...and what are the next steps? The collaborative platform is up and running. The organisation of further community events is on the way. A stable draft of the CPP will be ready in the end of September.
- 17. e-Sides Ethical and Societal Implications of Data Sciences 17 Key questions What is e-SIDES doing for value- centric big data? What’s the Community Position Paper and how does it help? Who is the community and how can I contribute? What do we already have and what are the next steps?
- 18. e-Sides Ethical and Societal Implications of Data Sciences 18 Contribute now
- 19. Mentimeter: Take your smartphone!
- 20. Safe-DEED Vision & Use Cases Ioannis Markopoulos1 and Mihai Lupu2 1. Forthnet, Greece 2. Research Studio Data Science, RSA FG, Austria
- 21. Vision A competitive Europe where individuals and companies are fully aware of the value of the data they possess and can feel safe to use it 21
- 22. Partners and progress 3 research centers ; 3 companies; 2 universities Timeline 22 Dec 1st, 2018 Nov 30th, 2021
- 23. Concept 23
- 24. 24 Forthnet Use Case (WP6) • Demonstrate how industries can benefit from big data marketplaces while trying to extract value of existing data by correlating with external sources and analysts securely and GDPR compliant. • Will support the set-up of such novel services and enable integration within the core infrastructures of several European industries; defining, preparing and implementing trials at Forthnet • Will produce detailed recommendations for further take-up and deployment of the services in close cooperation with the WP6 Implements the use-cases on the private personal data exchange on real life testbeds and provides feedback using CRM and Live Productions Viewership data
- 25. Forthnet is a market pioneer and the company that brought Internet and Pay TV in Greece This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825225 1st successful example of ‘’spin off’’ company 1995 1999 2000 2007 2009 2013 2006 2008 2012 2016 1st satellite digital TV platform 1st Telco & Pay TV bundled services 1st 3Play service ULL launch / 1st 2Play Service Successful Capital increase €30M Listing on ASE Forthnet acquired Nova Successful Capital increase €120Μ 1st OTT TV Everywhere service 2014 Successful Convertible bond €70M
- 26. Forthnet Leading position in Greek Market This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825225 unique households672k households with 3Play services 280k (€) total revenues (LTM)292m shops all over Greece115 km of fibre optic network 40k SMEs and Corporate subs Broadband and Pay TV subscriptions 952k 5.9k (€) adjusted EBITDA (LTM)42m Serving 16% of households in Greece Company Key figures 2018 H1 Number of HHs: 4.2M, Hellenic Statistical Authority
- 27. Forthnet Innovation Areas of Interest This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825225 Customer Experience measurement & improvement Big Data Analytics (e.g. client profiling, network performance, etc.) OTT Services & New ways to create content Connected Home, Smart Cities Data Center, Managed Cloud Services, SDN & 5G Privacy & Security
- 28. 28 Thanks! ∙ @safeDEED ∙ safe-deed.eu Coordinator: Patrick Ofner KNOW Center, Graz, Austria pofner@know-center.at Scientific Coordinator: Mihai Lupu Research Studio Data Science, RSA FG, Vienna, Austria mihai.lupu@researchstudio.at
- 29. Big Data for Mobility Tracking Knowledge Extraction in Urban Areas (Track&Know) Dr. Angelos Liapis Konnekt-able Technologies Ltd. (IE) 29 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No 780754. At the edge of GDPR: PII and Driver Profiling
- 30. Consortium 30
- 31. 31 T&K Vision Track&Know will research, develop and exploit a new software framework that aims at increasing the efficiency of Big Data applications in the transport, mobility, motor insurance and health sectors. Stemming from industrial cases, Track&Know will develop user friendly toolboxes that will be readily applicable in the addressed markets. T&K vision is in accordance with … • the EU Big Data Value Reference model • the US Big Data Reference Architecture model Source: NIST Big Data Interoperability Framework: Volume 6, Reference Architecture, v.2, Jun. 2018. https://doi.org/10.6028/NIST.SP.1500-6r1 Source: European Big Data Value Strategic Research Innovation Agenda (SRIA), v.4.0, Oct. 2017. http://bdva.eu/sites/default/files/BDVA_SRIA_v4_Ed1.1.pdf
- 32. 32 • Platform to handle heterogeneous streaming and archival data through the use of newly created toolboxes • Software Toolboxes, i.e. • data-intensive computing • interoperability • usability • standardisation • All data from streaming and archival data sources, as well as results from toolboxes can take full benefit of the computations of others, also taking advantage of seamless interoperability between their results. • Platform Architecture T&K Big Data Platform
- 33. 33 Commonality Between the Pilot Domains - Tracking of movement via the Big Data platform TransportationPilot trackstransport vehicle fleet (trucks) InsurancePilot – tracksrentalcars HealthPilot – tracksequipment andfacility use
- 34. 34 Data Sources: GPS location data from vehicle black boxes (provided by Octo Telematics), historic data, environmental data, demographics Business cases: • Insurance: using historic telematics, environmental, demographic and geographic information, … gain in-depth and accurate crash probability estimation • Electric Cars: (i) cost-benefit of a switching to an electric car mobility; (ii) matching global charging times and charging points to drivers’ habits • Car Pooling: (i) park decreasing due to sharable routes; (ii) cost- benefit of switching to a sharing mobility paradigm; (iii) likelihood of finding a proper sharable route that matches time and geographical zone Locations: London (metropolitan city), Rome (metropolitan city), Tuscany, Italy (country-urban mixed area) Challenges: Privacy, Heterogeneity of data, PROFILING Insurance Domain Pilot (Sistematica S.p.A)
- 35. Data Sources: Hospital IT System Data - Anonymised historic patient appointment and risk factor data, GPS data, demographic data, environmental data Business cases: • BC1 - Healthcare service optimisation • Reduction of unnecessary travel (patient travel distances, courier travel, CO2 emissions) • Cost efficiency gains (serve more patients with same resource) • Reduction of NoShow rate (reduce resource waste) • BC2 – OSA Driving profile extraction • Validation of phone app in clinical setting for driving profiling • Validation of sleep-deprived driving pattern Location: Cambridgeshire UK Challenges: Privacy, Ethics, data heterogeneity, PROFILING 35 Health Service Pilot (Royal Papworth Hospital)
- 36. 36 Data Sources: GPS and other sensor data (incl. fuel level and driver behavior data) from vehicles (tracks) and their drivers, historic data, environmental data, demographics Business cases: • Predictive maintenance • Anomaly detection, reduction of false alarms • Correlation of Fleet Data with external Weather and Traffic services • Fleet costs reduction • Fleet downtime reduction • Fleet response time improvement • Improve driver behavior and reduce accidents Locations: Greece, Albania, Cyprus and few in various EU countries Challenges: Privacy, Heterogeneity of data, PROFILING Fleet Management Domain (Vodaphone Innovus) Images produced by FRHF (Apr. 2018)
- 37. The ability to profile driver behavior to infer: • Fast / reckless driving, • Eco friendly driving, • dangerous driving, • sleepy driving From enriched GPS based mobility data 37 PROFILING: Unique Value Proposition
- 38. Driving Data: • Can Bus Data • Black box Data • Personalized App data External Data: • Road & Network Data • Weather Data • Points of Interest Data • Traffic Data 38 Data Sources
- 39. 39 Participants Professional Drivers from Commercial Fleets (approximately 9000) Average users with insurance black boxes from London, Rome and Tuscany (approximately 622,000 vehicles) A pool of up to 300 shift and non—shift staff with the NHS Volunteer OSA patients (survey indicates potential 25- 50 recruitments each week are possible, which is high for a patient study, total patient pool 10 000 per year))
- 40. 40 Measures in place to ensure protection of Citizen Data Better than industry standard security and encryption procedures • Very limited access to the data • High-levels of encryption of data at transmit, process and storage phases Testing data to identify biases before making available on the platform • Ethical and informed processes to identify biases created by historic, socio- demographics, and geo-political factors Informed consent of end users through: • Training patient facing staff to adequately inform patients of the aims and expected outcomes of the research, what data is being collected, all actors involved, and how the data is handled and processed for the medical pilots • Involvement of citizen advocacy and support groups as active stakeholders in the research process to help identify the potential of disenfranchisement of citizens by adopted research practices • A robust and continuous ethical process review process to monitor data, tasks, outcomes and deliverables
- 41. 41 Blackbox and Professional Drivers • Drivers from the fleet management pilot, and insurance pilots have been told at the outset that devices are being installed to monitor their driving behavior and that there are associated penalties for infractions.
- 42. 42 THE MAIN CHALLENGE IS PROFILING TransportationPilot trackstransport vehicle fleet (trucks) InsurancePilot – tracksrentalcars HealthPilot – tracksequipment andfacility use WE DON’T HAVE ALL THE ANSWERS… YET
- 43. Thank You Project Manager and Point of Contact Dr Ibad Kureshi ibad.kureshi@inlecomsystems.com Inlecom BVBA, BE This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No 780754. Dr. Angelos Liapis Konnekt-able Technologies Ltd. aliapis@konnektable.com
- 44. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732189 • Marina Da Bormida, R&I Lawyer and Ethics Expert • June, 27th 2019, e-SIDES Workshop, Riga A VALUE-CENTRIC & PRIVACY RESPECTFUL BIG DATA INITIATIVE IN THE PSPS DOMAIN: AEGIS PROJECT AND ITS ETHICS WHITE PAPER ©dem10/iStock ADVANCED BIG DATA VALUE CHAIN FOR PUBLIC SAFETY AND PERSONAL SECURITY
- 45. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732189 • An overview PART 1: AEGIS SYSTEM AND DEMONSTRATORS 45
- 46. 46AEGIS AT A GLANCE AEGIS brings together the data, the network and the technologies to create a curated, semantically enhanced, interlinked and multilingual repository for “Public Safety and Personal Security”-related Big Data. Topic: ICT-14-2016-2017 Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation
- 47. • Public Safety and Personal Security (PSPS) refers to the welfare and protection of the general public and of individuals through prevention and protection from dangers affecting safety such as crimes, accidents or disasters. • Lack of collaboration in PSPS related domains, including public sector, insurance, environment, health, automotive, smart home, etc. • Offerings based on fragmented and domain-specific data without leveraging the plethora of data sources (from other domains) that could further enhance and add value in such baseline services, if properly processed and combined. • Inability to effectively provide innovative cross-domain services in the private sector that cultivate a more caring and danger mitigating client base. MOTIVATION 47 ©Musterfotograf/AEGIS
- 48. 48PROJECT OBJECTIVES ❑ To identify and semantically link diverse cross-sector and multi-lingual information sources ❑ To supply a methodology for data handling services ❑ To roll-out improved intelligence conveying cross- sector and multi-lingual tools, ❑ To deliver an open, secure, privacy-respectful, configurable, scalable cloud based Big Data infrastructure as a Service ❑ To validate and optimize the AEGIS platform through 3 demonstrators ❑ To introduce new Business Models ❑ To establish an Open Innovation Community Scientific&InnovationObjectives TechnicalObjectives BusinessObjectives
- 49. 49DEMONSTRATORS Demonstrator I: Automotive and Road Safety Data Demonstrator II: Smart Home and Assisted Living Demonstrator III: Smart Insurance • Infer road conditions, map broken roads • Detect safety critical events to increase safe driving • Estimate the regional safety risk for e.g. crossroads, streets or whole cities • Monitoring and Alert Services for people at risk • Personalised guidance and well-being recommendations • Smart Home Automation for Well-being • Financial impact, customer support and services • Personalised early warning systems for asset protection • Business plan and marketing strategy
- 50. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732189 • Implementation of an ethical- driven approach and resulting best practices & recommendations PART 2: ETHICS AND LEGAL ISSUES IN AEGIS: WORK, FINDINGS & ETHICS WHITE PAPER 50
- 51. • Advanced semantic capabilities and tools tailored to the needs of the PSPS domain, including: ➢ Collection, processing and exchange of data relevant to PSPS domain from different sources: closed data (proprietary data) and open data, data with different IPR ➢ Data anonymisation & cleansing, privacy preservation ➢ Smart contracts for data exchange (distributed database of secure transactions) AEGIS AS A PRIVACY FRIENDLY AND ETHICALLY SOUND DATA PLATFORM 51 ©Musterfotograf/AEGIS Online big & small data analytics cloud-based platform supporting data IPR handling, security and privacy in a trustful and transparent manner Trustworthy data sharing community and ecosystem Collaboration for the shared value generation and expansion of AEGIS
- 52. Balancing opposite interests AEGIS Ethical, Privacy, Data Protection and IPR Strategy Ethical and Legal Requirements Ethical and Legal issues in the AEGIS Workplan Ethics Advisory Board Ethics Procedures Ethics Workshops Assessment, oversight and alignment: Ethical and Data Protection Impact Assessment 52AEGIS COMMITMENT IN ETHICAL AND LEGAL COMPLIANCE: AN OVERVIEW AEGIS design, development and validation in a responsible way, protecting human rights and ethical values and fostering positive societal impact
- 53. 53AEGIS PRIORITISATION PARADIGM Seeking for balance between opposite interests and values: AEGIS as an example of how data exchange can be exploited for the common good/public interest in conjunction with private business’ priorities and protection of individual rights EGE Group, jurisprudence (ECHR, CJEU) Priorities may differ in different context Not absolute rights (privacy, data protection, informational self- determination) Going beyond the traditional drastic trade-off approaches
- 54. Notice and consent prior to data collection Regulatory compliance and continuous legitimate ground of data processing Equilibrium and balancing (legal and ethical requirements & oversight) Privacy Protection Goal & Privacy-by-Design Approach Data Protection Impact Assessment Methodology (risk analysis, assessment scheme, mitigating measures) Technological fixes, including deidentification/anonymisat ion/ pseudonymisation (CloudTeams Anonymiser,…) AEGIS PRIORITISATION PARADIGM: PILLARS 54
- 55. • Comprehensive Framework for Data Protection, Legal Compliance, IPR handling and Ethical Issues, driving AEGIS platform design (including DPF) and validation • Living document (D1.2, D1.3) • Two Parts • I part: project’s implementation phase (e.g. ethics processes, EAB,…) • II part : AEGIS solutions (e.g. approach , requirements,…) • Elicitation of the legal, data protection and ethical requirements • Assessment Tool (project implementation and final AEGIS system). AEGIS ETHICAL, PRIVACY, DATA PROTECTION AND IPR STRATEGY (EPS) 55 ©Musterfotograf/AEGIS
- 56. 56ETHICAL, PRIVACY, DATA PROTECTION AND IPR HANDLING REQUIREMENTS / 1 Guideline on how to conceive, develop and use AEGIS architecture and tools, without forgetting checkpoints Approach for the elicitation • Privacy by Design and by Default (7 principles) • Privacy Protection Goal • Security Protection Goals (CIA Triad • Privacy protection goals • Balance against other protection goals • Legal sources for the lawfulness and fairness of the processing; GDPR, EFHRs,, national legislations, ethical guidelines) Iterative elicitation Basis for the development of the AEGIS Data Policy Framework Detailed description combinet with summary in table format
- 57. 57ETHICAL, PRIVACY, DATA PROTECTION AND IPR HANDLING REQUIREMENTS / 2
- 58. Implementation of the requirements and recommendations in a user-friendly way Blockchain-based IP and data sharing Model supporting data IPR handling, security and privacy (and collaboration) in a trustful and transparent manner •Advantages (difficult manipulation, self-governance transfer of ownership) •Tags and extra-tags •Micro-contracts Security and privacy technologies used together with Blockchains, such as anonymisation technologies (e.g. CloudTeams Anonymisation Tool) and encryption AEGIS BLOCKCHAIN POWERED DATA POLICY FRAMEWORK 58 ©Musterfotograf/AEGIS Framework setting the appropriate security, data privacy, data quality probing and IPR policies to resolve on-the fly how data can be handled by each stakeholder group, based on its content, its value and peer-to-peer agreements that will be reached between the collaborating entities
- 59. • Composition: 3 members (expertise on ethics and legal issues) • EAB working closely with AEGIS Consortium during the course of the project on tackling ethical, legal and data privacy issues. • • Role: i) evaluate the AEGIS’s progress and results and supervise the operation of the project (compliance, requirements); ii) periodically report to the EC iii) advise the partners how to proceed in an ethically correct way and in compliance with the applicable legislations iv) co-create and/or peer review of selected parts of the ethics and privacy related deliverables; • • Reporting activities: D8.2 (M18) for timely ensure that the project is on the right tracks just before the completion of WP1 (AEGIS Data Value Chain Definition and Project Methodology); D8.3 at M30 final assessment ETHICS ADVISORY BOARD (EAB) 59 ©Musterfotograf/AEGIS
- 60. Local dedicated services for anonymisation and filtering of data Data tagging with different policies including security and privacy/trust levels, as well as IPR clearance reflected in specific micro-services Two kind of repository for the storage, depending on the applied disclosure and data privacy and IPR policy → public repository (SLOD space) and private/internal repository (ALLDS) + additonal safeguard measures Privacy-friendly and IPR-preserving modalities and tools for the extraction of linked data analytics from private harmonised data stored in private repositories or produced linked data with them resulting from the information exchange among ALLDS and the SLOD space. Virtual currency and blockchain technology used to safeguard the proper data sharing principles of the platform. ASSESSMENT OF AEGIS SYSTEM 60 ©Musterfotograf/AEGIS
- 61. Not every demonstrator collects personal data (some only require technical data) Personal data collection amount is restricted to minimum necessary to provide the respective, personalized service Purpose limitation Participants are able to monitor and access their personal as well as non-personal data for the purpose of correctness and data quality. AEGIS offline anonymisation or similar anonymisation procedures used by each demonstrator AEGIS platform will not collect any personal data. Security measures against external attacks and unauthorized access & internal management access control All researchers are aware of their ethical responsibility ASSESSMENT OF AEGIS DEMONSTRATORS 61 ©Musterfotograf/AEGIS
- 62. CONCLUSIONS 62 ©Musterfotograf/AEGIS The Consortium dealt in a proper manner with all arising privacy and data protection issues, as well as with the ethical and societal dimension of AEGIS, demonstrating a high level of awareness, attention and knowledge System’s design and deployment Demonstration activities Inspiring other Big Data and AI H2020 Projects: EC invited AEGIS EAB to prepare an Ethics White Paper for facilitating replication of AEGIS ethics-related experience, considered as a best practice
- 63. - Topics described above as core content of the WP - Project-specific part: activities, findings and recommendations for post-project phase - General part containing comprehensive recommendations and guidelines: 3 phases (proposal, project development, uptake/exploitation) - Expected release: mid-July 2019 - Feedback, suggestions, commments - https://www.aegis-bigdata.eu/research-papers/ - On-demand: further assistance and advice for fine- tuning for other projets AEGIS ETHICS WHITE PAPER 63 Set of lessons learnt, best practicies and recommendations on how to deal with ethical issues raised by H2020 Projects in the Big Data and AI domain, capitalizing on AEGIS experience
- 64. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732189 • Marina Da Bormida • R&I Lawyer and Ethics Expert, member of AEGIS EAB • m.dabormida@eurolawyer.it THANK YOU! 64
- 65. http://lynx-project.eu/ Beyond regulatory compliance? Elena Montiel Ponsoda, Víctor Rodríguez-Doncel Riga, June 2019 H2020 Lynx project
- 66. http://lynx-project.eu/ Beyond regulatory compliance? Elena Montiel Ponsoda, Víctor Rodríguez-Doncel Riga, June 2019 H2020 Lynx project
- 75. Panel session: Challenges of RRI for big data and analytics, and types of solution approaches as opportunity to overcome them
- 77. Thank you! @eSIDES_EU #valuecentricbigdata eSIDES_EU info@e-sides.eu Community Position Paper Participate in our Community Consultation