Benjamin Chen and Xiaohan Yin on Data Still Needs Theory: Collider Bias in Empirical Legal Research (HKLJ)

"Data Still Needs Theory: Collider Bias in Empirical Legal Research"
Benjamin Chen and Xiaohan Yin (PhD candidate)
Hong Kong Law Journal, Vol. 53, Part 3 of 2023, pp.1241 - 1258
Abstract: Big data is characterised not only by the amount but also the kinds of information that can be created, stored, and processed. This explosion of data, accompanied by the capacity to analyse them, has catalyzed large n, quantitative approaches to the study of law and legal institutions. But neither size nor quality guarantees the validity of causal inferences drawn from observational data. For example, although the inclusion of control variables can help isolate causal effects, not all variables are good controls. Bad controls are not harmless and can create the impression of a causal relationship where none exists. This spurious association is called collider bias. We introduce the concept of collider bias and give motivated examples of how it can arise in empirical legal research. The selection of good controls requires knowledge and assumptions about causal structures. Theory and domain knowledge are essential for quantitative analysis, even in the era of big data.

Please click here to view the full article on SSRN.

Sida Liu and Sitao Li on How to Do Empirical Legal Studies without Numbers? (HKLJ)

"How to Do Empirical Legal Studies without Numbers?"
Sida Liu and Sitao Li
Hong Kong Law Journal, Vol. 53, Part 3 of 2023, pp.1260 - 1273

Abstract: How to do empirical legal studies without numbers? This article addresses this methodological question at a crossroads of empirical legal studies in China. It does not aim to provide a normative defence for the value of qualitative methods. Instead, we demonstrate how a ‘scientific turn’ in the 2010s has made empirical legal research in China almost exclusively about quantitative research and then illustrate how qualitative methods can also benefit from the rise of digital technology. We draw on three recent studies as examples to compare and contrast the methodological challenges and opportunities for doing empirical legal studies without numbers: (1) Ke Li’s book Marriage Unbound as an example of ethnography in combination with archival research; (2) Sitao Li’s article ‘Face-Work in Chinese Routine Criminal Trials’ as an example of trial video observation; and, (3) Di Wang and Sida Liu’s article ‘Performing “Artivism”’ as an example of online ethnography. The discussion shows that, despite the rising popularity of ‘big data’ computational analysis in recent years, quantitative methods are not necessarily more technologically advanced than qualitative ones. Technology-assisted interviews and ethnography can open up many new possibilities in data collection and data analysis, sometimes resulting in more exciting and innovative research.

Please click here to view the full article on SSRN.

Gary Meggitt on Marine insurance fraud and emerging technology (New book chapter)

"Marine insurance fraud and emerging technology"
Gary Meggitt
in Research Handbook on Marine Insurance Law, edited by Özlem Gürses (Edward Elgar Publishing, March 2024), Chapter 14, pp. 275 - 305
Published online: March 2024

Abstract: Marine insurance fraud is probably as old as marine insurance itself. Year after year, the courts hand out judgments- for or against insurers- following lengthy, complex and costly litigation. The losses to innocent shipping companies, cargo owners, agents, brokers, insurers and others, however, go on. Might technology succeed in defeating- or at least deterring- fraud where the ‘law’ has failed? This chapter looks at the use of emerging technology, including smart contracts and artificial intelligence (AI), by insurers and the marine transport industry to combat those who perpetrate marine insurance fraud. It considers how such technology may have resolved some of the more controversial fraud litigation of recent years and how the relationship between this technology and the law may develop in the future.

Calvin Ho et al on GA4GH: International Policies and Standards for Data Sharing across Genomic Research and Healthcare (Cell Genomics)

"GA4GH: International policies and standards for data sharing across genomic research and healthcare"

Heidi L. Rehm, Angela J.H. Page, Lindsay Smith, Jeremy B. Adams, Gil Alterovitz, Lawrence, J. Babb, Maxmillian P. Barkley, Michael Baudis, Michael J.S. Beauvais, Tim Beck, Jacques, S. Beckmann, Sergi Beltran, David Bernick, Alexander Bernier, James K. Bonfield, Tiffany F. Boughtwood, Guillaume Bourque, Sarion R. Bowers, Anthony J. Brookes, Michael Brudno, Matthew H. Brush, David Bujold, Tony Burdett, Orion J. Buske, Moran N. Cabili, Daniel L. Cameron, Robert J. Carroll, Esmeralda Casas-Silva, Debyani Chakravarty, Bimal P. Chaudhari, Shu Hui Chen,  J Michael Cherry, Justina Chung, Melissa Cline. Hayley L. Clissold, Robert M. Cook-Deegan, Mélanie Courtot, Fiona Cunningham, Miro Cupak, Robert M. Davies, Danielle Denisko, Megan J.Doerr, Lena I. Dolman, Edward S. Dove, L. Jonathan Dursi, Stephanie O.M. Dyke, James A. Eddy, Karen Eilbeck, Kyle P. Ellrott, Susan Fairley, Khalid A. Fakhro, Helen V. Firth, Michael S. Fitzsimons, Marc Fiume, Paul Flicek, Ian M.Fore, Mallory A.F reeberg, Robert R.Freimuth, Lauren A.Fromont, JonathanFuerth, Clara L.Gaff, Weiniu Gan, Elena M. Ghanaim, David Glazer, Robert C. Green, Malachi Griffith, Obi L.Griffith, Robert L. Grossman, Tudor Groza, Jaime M.Guidry Auvil, Roderic Guigó, Dipayan Gupta, Melissa A. Haendel, Ada Hamosh, David P .Hansen, Reece K.Hart, Dean Mitchell Hartley, David Haussler, Rachele M. Hendricks-Sturrup, Calvin W.L.Ho, Ashley E.Hobb, Michael M. Hoffmanm, Oliver M.Hofmann, PetrHolub, Jacob ShujuiHsu, Jean-Pierre Hubaux, Sarah E.Hunt, Ammar Husami, Julius O.J acobsen, Saumya S. Jamuar, Elizabeth L. Janes, Francis Jeanson, Aina Jeném Amber L. Johns, Yann Joly, Steven J.M. Jones, Alexander Kanitz, Kazuto Kato, Thomas M.Keane, Kristina Kekesi-Lafrance, Jerome Kelleher, Giselle Kerry, Seik-SoonKhor, Bartha M. Knoppers, Melissa A. Konopko, Kenjiro Kosaki, Martin Kuba, Jonathan Lawson, Rasko Leinonen, Stephanie Li, Michael F. Lin, Mikael Linden, Xianglin Liu, Isuru Udara Liyanage, Javier Lopez, Anneke M. Lucassen, Michael Lukowski, Alice L.Mann, John Marshall, Michele Mattioni, Alejandro Metke-Jimenez, Anna Middleton, Richard J. Milne, Fruzsina Molnár-Gábor, Nicola Mulder, Monica C.Munoz-Torres, RishiNag, Hidewaki Nakagawa, Jamal Nasir, Arcadi Navarro, Tristan H. Nelson, Ania Niewielska, Amy Nisselle, Jeffrey Niu, Tommi H.Nyrönen, Brian D. O’Connor, Sabine Oesterle, Soichi Ogishima, VivianOta Wang, Laura A.D.Paglione, Emilio Palumbo, Helen E. Parkinson, Anthony A. Philippakis, Angel D.Pizarro, Andreas Prlic, Jordi Rambla, Augusto Rendon, Renee A.Rider, Peter N.Robinson, Kurt W.Rodarmer, Laura Lyman Rodriguez, Alan F.Rubin, Manuel Rueda, Gregory A.Rushton, Rosalyn S.Ryan, Gary I. Saunders, Helen Schuilenburg, Torsten Schwede, Serena Scollen, Alexander Senf, Nathan C.Sheffield, Neerjah Skantharajah, Albert V. Smith, Heidi J. Sofia, Dylan Spalding, Amanda B.Spurdle, Zornitza Stark, Lincoln D.Stein, Makoto Suematsu, Patrick Tan, Jonathan A. Tedds, Alastair A. Thomson, Adrian Thorogood, Timothy L.Tickle1 Katsushi Tokunaga, Juha Törnroos, David Torrents, Sean Upchurch, Alfonso Valencia, Roman Valls Guimera ,Jessica Vamathevan, Susheel Varma, Danya F. Vears, Coby Viner, Craig Voisin, Alex H. Wagner, Susan E. Wallace, Brian P.Walsh, Marc S.Williams, Eva C.Winkler, Barbara J.Wold, Grant M. Wood, J. Patrick Woolley, Chisato Yamasaki, Andrew D.Yates, Christina K.Yung, Lyndon J.Zass, Ksenia Zaytseva, Junjun Zhang, Peter Goodhand, Kathryn North1, Ewan Birney

Cell Genomics
Published in Nov 2021

Summary: The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Calvin Ho et al on Trustworthy Use of AI & Big Data Analytics in Health Insurance (WHO Bulletin)

"Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance"

Calvin W L Ho, Joseph Alib & Karel Caalsc
Bulletin of the World Health Organization
Volume 98, Number 4, pp. 229-296

Published in April 2020

Abstract: Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.  Click here to read the full article.

摘要: 确保人工智能和大数据分析在医疗保险中的可靠使用 大数据（即，可以快速处理大量不同来源的高度差异 化数据）、数据科学和人工智能领域的技术进步可以 改善医疗系统功能，促进个性化护理和公益服务。然 而，这些技术不会取代医疗系统中的道德领导和治理 等基本组成要素，也不会消除对稳健的道德和监管环 境的需求。在本文中，我们讨论了医疗保险大数据分 析中的稳健道德和监管环境可能是什么样子的，并且 举例描述了应该建立的保障和参与机制。首先，一个 清晰有效的数据治理框架至关重要。需要制定法律标 准，并且鼓励和激励保险公司在设计和使用大数据分析和人工智能方面秉承以人为本的理念。第二，必须 有一个明确的问责流程来解释可以使用哪些信息以及 如何使用这些信息。第三，对于数据被采用的人员， 应该通过积极参与决定如何管理和治理其个人数据的 方式为其赋权。第四，保险公司和治理机构，包括监 管机构和政策制定者，需携手合作，确保基于人工智 能开发的大数据分析是透明且准确的。除非具备有利 的道德环境，否则使用此类分析很可能会导致未连接 的数据系统的分散，加剧现有的不均衡情况，并降低 可靠性和可信度。

Anne Cheung on Moving Beyond Consent For Citizen Science in Big Data Health and Medical Research (Northwestern J Tech & IP)

"Moving Beyond Consent For Citizen Science in Big Data Health and Medical Research"

Anne Cheung
Northwestern Journal of Technology and Intellectual Property
Summer 2018, Volume 16, Issue 1

Abstract: Consent has been the cornerstone of the personal data privacy regime. This notion is premised on the liberal tenets of individual autonomy, freedom of choice, and rationality. The above concern is particularly pertinent to citizen science in health and medical research, in which the nature of research is often data intensive with serious implications for individual privacy and other interests. Although there is no standard definition for citizen science, it includes generally the gathering and volunteering of data by non-professionals, the participation of non-experts in analysis and scientific experimentation, and public input into research and projects. Consent from citizen scientists determines the responsibility and accountability of data users. Yet with the advancement of data mining and big data technologies, risks and harm of subsequent data use may not be known at the time of data collection. Progress of research often extends beyond the existing data. In other words, consent becomes problematic in citizen science in the big data era. The notion that one can fully specify the terms of participation through notice and consent has become a fallacy. Is consent still valid? Should it still be one of the critical criteria in citizen science health and medical research which is collaborative and contributory by nature? With a focus on the issue of consent and privacy protection, this study analyzes not only the traditional informed consent model but also the alternative models. Facing the challenges that big data and citizen science pose to personal data protection and privacy, this article explores the legal, social, and ethical concerns behind the concept of consent. It argues that we need to move beyond the consent paradigm and take into account the much broader context of harm and risk assessment, focusing on the values behind consent – autonomy, fairness and propriety in the name of research.  Click here to download the full article.

Clement Chen & Anne Cheung on The Transparent Self under Big Data Profiling: Privacy and Chinese Legislation on the Social Credit System (Journal of Comparative Law)

"The Transparent Self under Big Data Profiling: Privacy and Chinese Legislation on the Social Credit System"

Yongxi Chen & Anne Cheung
The Journal of Comparative Law
published in Feb 2018
Volume 12, Issue 2, pp. 356-378

Introduction: Big data is one of the buzz phrases of the 21st century, concerning not only the digitalisation of data on billions of individuals, but also what those in power are able to do with that data.  The defining characteristic of big data is the capacity to search, aggregate and cross-reference large datasets for analysis to identify previously undetectable patterns, as well as the power to profile individuals, calculate risks, and monitor and even predict behaviour.  When big data is harvested by governments, the worry is that the totality of individuals' lives will be captured, that citizens will be monitored and that the Orwellian state will become a reality.
     In China, such a worry seems far from unfounded given the Chinese Communist Party's (CCP) roll-out of its powerful Social Credit System (SCS).  Launched at the national level in 2014, the system's aim is to assess the trustworthiness of Chinese citizens in keeping their promises and complying with legal rules, moral norms, and professional and ethical standards.  It is essentially an all-encompassing, penetrative system of personal data processing, manifested by the comprehensive collection and expansive use of personal data with the explicit intention on the Chinese government's part of harnessing the ambition and power of big data technology.  The SCS rates both business entities and individuals.  According to its blueprint, the records that are collected can be extensively used by the authorities and business entities alike for a variety of purposes broadly related to 'encouraging trustworthiness and punishing untrustworthiness'.
     Whilst the use of big data analytics in the context of credit scoring and the rating of individuals is not unique to China, in other jurisdictions it is usually confined to the financial arena and regulated by law.  What differentiates China is the scale of the data collected, the scope of its use and, particularly important for the purposes of this article, the apparent lack of a comprehensive legal system to protect personal data.  Despite the introduction of the Cyber Security Law in 2016 in relation to online data, the extension of civil law protection to consumer data in 2013, and the criminalisation of the unlawful gathering, receipt and sale of personal data in 2009, personal data as a general subject has yet to be clearly defined and effectively protected under Chinese law.   The rights that data subjects are entitled to under a personal data protection regime are rarely mentioned in China and are, at best, provided for under scattered sector-specific laws.
     Given the inadequate protection afforded to personal data in China, the country is an ideal social laboratory for big data experimentation, data intelligence and mass surveillance.  Individuals risk being reduced to transparent selves before the state in this uneven battle.  They are uncertain about what contributes to their social credit scores, how those scores are combined with the state system and how their data is interpreted and used.  In short, the big data-driven SCS is confronting Chinese citizens with major challenges to their privacy and personal data.
     Although the State Council's Planning Outline for the Construction of the Social Credit System ('SCS Outline' hereafter) sketches out an ambitious blueprint, it is the pilot legislation implemented at the local level since 2014 that has institutionalised the collection and use of social credit-related data.  To analyse China's emerging SCS under existing international legal principles concerning personal data protection, this article identifies and compares typical examples of relevant legislation at the local level and discusses their implications for personal data protection.  It argues that existing legislation and proposed regulations require substantial revisions to mitigate the impact of the SCS on data privacy and other interests critical to individual citizens.
     The article begins by mapping out the background to the construction of China's big data social laboratory and the SCS.  The next section examines the system's social management aim and comprehensive sanction system, as well as its nature as a collaborate project between the authorities and the business sector.  The section which follows then summarises the legislative history and evolving concept of social credit and analyses the nature of individuals' rights to personal data protection under China's uncoordinated legal framework.  The article then reviews local social credit legislation with reference to the three cardinal principles of personal data protection most closely related to data subjects' control over the processing of their data: firstly, the data collection principle,;secondly, the data usage principle, and thirdly, data subjects' right to access and correct their own data.  The final section concludes that although local legislation provides nominal rights of access to, and a few restrictions on, the collection and use of data, it has largely failed to secure meaningful control over personal data for individuals.  These legislative defects relate to the very purpose of the SCS and to extra-legal restrictions inherited from the pre-reform party-state regime.

Anne Cheung and Clement Chen's Work on Big Data in China Profiled in HKU's Bulletin (Jan 2018)

"Big Data's Dark Side"

Bulletin

Jan 2018, Vol 19, No 2

How can individuals be protected when their personal data is constantly being collected for uses that may not be apparent until some future date? And when it may not be obvious who is collecting that data?

     As giants like Google, Facebook, WeChat and Alibaba track their users every minute of the day, these questions are rising high on government agendas around the world. In little more than a decade, most people now share personal information in order to gain access to services – whether socialising, shopping, seeking entertainment, or checking up on their health. Even our whereabouts can be tracked at every moment if the location service on our phones is turned on.

     That goldmine of information is being used by both businesses and governments to make decisions about individuals and groups, such as how much to charge certain users for services, whether to deny them access and what trends are revealed by their data. And therein lie several problems.

    First, the story told by big data may not be an accurate one. Professor John Bacon-Shone of the Faculty of Social Sciences, a statistician with an interest in big data and privacy who also advises the Hong Kong Government on the issues, cites the example of the Google Flu Trends web service which aggregated search queries about flu to predict outbreaks. “The problem is, it’s just an association, not causation, and it doesn’t work well at prediction. If you have a different type of flu, the whole thing falls apart,” he said...

     Personal data protection laws typically require banks and other institutions to keep accurate up-to-date information and disclose how it will be used. But when the technology is changing rapidly, with new and unanticipated uses becoming possible, this may no longer be sufficient.

     Professor Anne SY Cheung of the Faculty of Law has been studying privacy and personal data protection and is co-editor of the 2015 book Privacy and Legal Issues in Cloud Computing. “Recent legal reforms and position papers from the European Union (EU), the UK and the US have raised concerns about the problem of profiling, predictive decisions and discrimination, and the harm that may result from that. This is because the use of big data is very different from our traditional understanding of how to regulate personal data.

     “The traditional approach is essentially one of notice and consent: the collection of personal data is allowed only for a specific and limited purpose. But in the age of big data, the more data one has, the more accurate and arguably useful one’s conclusions will be. So the collector tries to collect as much data as possible and only after they have it and have done their analysis, will they find correlations and identify the purpose,” she said...

China: Big data, big brother?

The use of big data in China is of an altogether different level of concern from commercial uses of personal information.  The central government is in the process of rolling out a social credit system that draws on big data to rate each individual's reputation based on their political leanings, purchase history, social interactions and other factors.  

     "China is like a big data laboratory," said Professor Cheung, who has been studying the situation there with colleague Dr Clement Chen.  "Arguably, there is 360-degree surveillance watching individuals and gathering data. They have real-name registration [for mobile and internet services] and close connections between the government and the banking system and internet companies"...  Click here to read the full article.

New Issue: SSRN Legal Studies Research Paper Series (HKU)

Vol. 7, No. 3: 7 June 2017

Table of Contents

1. Does Hong Kong Remain an Equivalent Jurisdiction for the EU Credit Rating Regime?

Syren Johnstone, Faculty of Law, University of Hong Kong, Asian Institute of International Financial Law

2. Moving beyond Consent for Citizen Science in Big Data Health Research

Anne S. Y. Cheung, The University of Hong Kong - Faculty of Law

3. From FinTech to TechFin: The Regulatory Challenges of Data-Driven Finance

Dirk A. Zetzsche, ADA Chair in Financial Law / Inclusive Finance, University of Luxembourg, Heinrich Heine University Duesseldorf - Faculty of Law - Center for Business & Corporate Law (CBC)

Ross P. Buckley, University of New South Wales (UNSW) - Faculty of Law

Douglas W. Arner, University of Hong Kong - Faculty of Law

Janos Nathan Barberis, The University of Hong Kong - Faculty of Law

4. Chinese Constitutionalism: An Oxymoron?

Wen-Chen Chang, National Taiwan University College of Law

David S. Law, Washington University in St. Louis - School of Law, The University of Hong Kong - Faculty of Law, Washington University in St. Louis - Department of Political Science

HKU Symposium on Big Data and Data Governance 2016 (14-15 Oct 2016)

Symposium on Big Data and Data Governance 2016

October 14 - 15, 2016

Academic Conference Room, 11/F Cheng Yu Tung Tower, The University of Hong Kong

Big data has become a buzz word in business planning, government regulation and academic research. Its potentials, and pitfalls are gradually making its impacts on various sectors and in our daily lives. This Symposium addresses challenges posed by the power of big data, and explores the approaches of data governance so as to ensure its fair use. We bring together international and local experts from the academics, industries, and regulatory authority to discuss algorithm decision making, code, and data-driven surveillance. They will also review the transformation of regulatory regimes and protective techniques, such as consent and encryption. A Keynote Dialogue, in the format of a special round table, will be held to exchange ideas and insights on the legal and policy concerns related to big data governance. Furthermore, a Young Scholars Forum will be held to spark collaborations and debate among graduate students and young researchers in Hong Kong and Germany.

     The Symposium continues the discussion initiated by the Big Data and Privacy Workshop that was successfully held in 2015. The two events are part of the RGC (Germany-Hong Kong)-DAAD Joint Research Project conducted by Professor Anne Cheung, Faculty of Law HKU, and Professor Wolfgang Schulz, Hans Bredow Institute, University of Hamburg, and UNESCO Chair for Freedom of Communication and Information.

      For more information, please visit: www.lawtech.hk/bigdata2016.  All are welcome. Registration is open on a first-come first-served basis: https://hkuems1.hku.hk/hkuems/ec_hdetail.aspx?guest=Y&ueid=45609 Inquiries: Ms. Grace Chan (mcgrace@hku.hk / 3917-4727)

Report on the HKU Big Data and Privacy Workshop

The Law and Technology Centre (the “Centre”) of The University of Hong Kong’s Faculty of Law had the pleasure to host the Workshop: Big Data and Privacy on 30 November 2015. The Workshop was part of a collaborative research project on Big Data and Privacy by Professor Anne SY Cheung of the Centre and Professor Wolfgang Schulz of the University of Hamburg. It was supported by the Germany-Hong Kong (“DAAD”) Research Grant.

     Revolutionary means of generating and processing voluminous and diverse data sets across different sectors are constantly being developed, with big data increasingly being employed in business, governance and social life. While big data has the potential to add immense social and economic value and serve the common good, it also impacts on the privacy of individuals and challenges the effectiveness of traditional legal frameworks for data protection.

     As solutions to many of these data privacy issues remain obscure, speakers from the University of Hamburg, Germany, Academic Sinica, Taiwan, and HKU (not only from the Faculty of Law, but also from the Faculty of Social Science, the Departments of Computer Science and Statistics) gathered to discuss these weighty issues. In addition to having input from academics, the Workshop also had speakers from regulatory body, the IT industry and a private law firm, including the Privacy Commissioner’s Office, Microsoft and Winston & Strawn LLP.

     Speakers reviewed the challenges that Big Data has posed for business, medical and healthcare providers and social movements. Furthermore, it also explored privacy implications and data protection measures in data-driven businesses, including profiling, monitoring and predictive analysis. Rather than providing ready solutions, the workshop aimed to shed light on our understanding of the desirable use of Big Data. Legal developments in Hong Kong, Taiwan, mainland China, Germany and the European Union were discussed... Click here to read the rest of the report published in Hong Kong Lawyer (Jan 2016).  The photos from the workshop can be accessed here.