This document discusses innovation management and provides information on various related topics. It outlines key aspects of innovation management including context, lens, trend watching, exponential technologies, innovation techniques, and tools. It also lists various innovation "gurus" and discusses Moore's Law and its implications. Finally, it provides overviews of different innovation management strategies, frameworks like TRIZ, and emerging digital realities.
This document discusses Porter's five forces framework for analyzing industry competition and profitability. It explains the five competitive forces - competitive rivalry, threat of new entry, bargaining power of suppliers, bargaining power of buyers, and threat of substitutes. It provides examples of how these forces impact industry profitability. Strategies are presented for differentiating products to minimize competitive forces, including using perceptual maps to identify brand positioning opportunities.
Green indexes used in CAST to measure the energy consumption in codeCAST
This document describes CAST's Green IT Index, which aims to measure the energy consumption of code. CAST analyzes software at the system, module, and program levels using over 1500 checks. The Green IT Index aggregates quality rules related to efficiency and robustness, which impact energy usage. It is calculated based on rules in 5 technical criteria for efficiency and 3 for robustness. The index helps identify parts of software that could be optimized to reduce wasted CPU resources and lower energy consumption. CAST is seeking feedback on this approach to refine how the Green IT Index is composed.
A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processin...Konstantinos Demertzis
The document describes an Intelligent Policies Analysis Mechanism (IPAM) that is part of the ADVOCATE framework. IPAM uses machine learning methods like Fuzzy Cognitive Maps and Extreme Learning Machines to identify potentially conflicting rules or consents from a user that could lead to personal data collection and profiling without consent. The framework aims to help users maintain control over their personal data as required by GDPR regulations. IPAM simulates how smart devices collect personal data and identifies rules that may enable profiling, training on example data to learn how to detect such instances.
The Impact of the Consumerization of IT on the Public SectorGovLoop
The document discusses how the consumerization of IT is transforming the public sector workforce by allowing government employees to work anywhere, anytime, and on any device. It highlights opportunities like improved employee morale and increased work flexibility. However, it also notes challenges in ensuring cybersecurity, data security, and addressing legal issues with policies that have not caught up with changing technologies. The document advocates that agencies build flexible infrastructures that can support new devices and workstyles while still maintaining proper governance over data and systems.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
This document discusses innovation management and provides information on various related topics. It outlines key aspects of innovation management including context, lens, trend watching, exponential technologies, innovation techniques, and tools. It also lists various innovation "gurus" and discusses Moore's Law and its implications. Finally, it provides overviews of different innovation management strategies, frameworks like TRIZ, and emerging digital realities.
This document discusses Porter's five forces framework for analyzing industry competition and profitability. It explains the five competitive forces - competitive rivalry, threat of new entry, bargaining power of suppliers, bargaining power of buyers, and threat of substitutes. It provides examples of how these forces impact industry profitability. Strategies are presented for differentiating products to minimize competitive forces, including using perceptual maps to identify brand positioning opportunities.
Green indexes used in CAST to measure the energy consumption in codeCAST
This document describes CAST's Green IT Index, which aims to measure the energy consumption of code. CAST analyzes software at the system, module, and program levels using over 1500 checks. The Green IT Index aggregates quality rules related to efficiency and robustness, which impact energy usage. It is calculated based on rules in 5 technical criteria for efficiency and 3 for robustness. The index helps identify parts of software that could be optimized to reduce wasted CPU resources and lower energy consumption. CAST is seeking feedback on this approach to refine how the Green IT Index is composed.
A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processin...Konstantinos Demertzis
The document describes an Intelligent Policies Analysis Mechanism (IPAM) that is part of the ADVOCATE framework. IPAM uses machine learning methods like Fuzzy Cognitive Maps and Extreme Learning Machines to identify potentially conflicting rules or consents from a user that could lead to personal data collection and profiling without consent. The framework aims to help users maintain control over their personal data as required by GDPR regulations. IPAM simulates how smart devices collect personal data and identifies rules that may enable profiling, training on example data to learn how to detect such instances.
The Impact of the Consumerization of IT on the Public SectorGovLoop
The document discusses how the consumerization of IT is transforming the public sector workforce by allowing government employees to work anywhere, anytime, and on any device. It highlights opportunities like improved employee morale and increased work flexibility. However, it also notes challenges in ensuring cybersecurity, data security, and addressing legal issues with policies that have not caught up with changing technologies. The document advocates that agencies build flexible infrastructures that can support new devices and workstyles while still maintaining proper governance over data and systems.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Paper presented at "Perspectives on Global Crises: Challenges and Opportunities" - 16th Max Weber Fellows June Conference, 15-17 June 2022, Badia Fiesolana
Impact of GDPR on the pre dominant business model for digital economiesEquiGov Institute
A brief description of the impact the General Data Protection Regulation (GDPR) could have on the proposed move towards a digital economy, especially for the Caribbean
On Digital Markets, Data, and Concentric DiversificationBernhard Rieder
This document discusses how large tech companies like Google and Facebook have expanded from their original businesses through a strategy of concentric diversification. It argues that their accumulation of large data assets and algorithmic capabilities allows them to computerize new domains. For example, Google uses its knowledge bases and machine learning to expand from search into areas like self-driving cars. Facebook leverages its social graph and identity resolution to enter new ad tech businesses. The document analyzes how these companies' technological systems grow more valuable as their assets transfer to new sectors, creating economies of scale that affect market dynamics and relationships between firms.
16 C O M M U N I C AT I O N S O F T H E A C M N O.docxRAJU852744
16 C O M M U N I C AT I O N S O F T H E A C M | N O V E M B E R 2 0 1 8 | V O L . 6 1 | N O . 1 1
news
I
M
A
G
E
B
Y
M
I
X
M
A
G
I
C
with the advertisements that you ac-
cept. And I think people are becoming
more and more aware of the fact that
their personal data do have a value.”
Says Alison Cool, assistant profes-
sor of anthropology at the University
of Colorado, Boulder, “There are a
lot of questions and ambiguities that
must be addressed, but it’s clear that
GDPR will significantly change the
data landscape.”
While the U.S. and a number of
other countries have adopted an opt-
out approach to data collection—es-
sentially, a consumer must instruct
a company if he or she doesn’t want
his or her data used or shared in cer-
tain ways—Europe has implemented
a more restrictive opt-in approach.
However, GDPR takes this concept to
a new and previously untested level.
Besides giving consumers near-total
control of their data, they can have
W
H E N T H E E U R O P E A N
U N I O N (EU) General
Data Protection Regu-
lation (GDPR) went
into effect on May 25,
2018, it represented the most sweeping
effort yet to oversee the way businesses
collect and manage consumer data.
The law, established to create consis-
tent data standards and protect EU
citizens from potential privacy abus-
es, sent ripples—if not tidal waves—
across the world.
GDPR gives European citizens great-
er control of their data while establish-
ing strong penalties for businesses
that do not comply. What is more, any
data that involves EU citizens or touch-
es EU companies is covered by GDPR.
The initiative replaces an older data
privacy initiative called the Data Pro-
tection Directive 95/46/EC, which was
introduced in 1995.
The implications and ramifications
are enormous—and the initiative’s
reach is global. GDPR will change ev-
erything from the way data collection
takes place to the way corporate data-
bases are designed and used. It also will
potentially change the way research
and development takes place, and
will impact cybersecurity practices, as
well as introducing a practical array of
challenges revolving around sites and
repositories where groups share com-
ments, information, and other data.
“It’s a groundbreaking initiative,”
says Brett M. Frischmann, Charles
Widger Endowed University Professor
in Law, Business, and Economics at Vil-
lanova University, and Affiliate Scholar
of the Center for Internet and Society
at Stanford Law School. “Europe has
flipped a switch and prompted recon-
sideration of how data can be collected,
managed, and used.” The EU takes the
position that a person owns his or her
data, and their privacy is a fundamen-
tal right that is “basic to the integrity of
a human being,” Frischmann adds.
Data Wars
Digital technology has inexorably
changed the face of privacy. Today,
there is a perception—and plenty of
evidence to support it—that personally
ident.
16 C O M M U N I C AT I O N S O F T H E A C M N O.docxaulasnilda
16 C O M M U N I C AT I O N S O F T H E A C M | N O V E M B E R 2 0 1 8 | V O L . 6 1 | N O . 1 1
news
I
M
A
G
E
B
Y
M
I
X
M
A
G
I
C
with the advertisements that you ac-
cept. And I think people are becoming
more and more aware of the fact that
their personal data do have a value.”
Says Alison Cool, assistant profes-
sor of anthropology at the University
of Colorado, Boulder, “There are a
lot of questions and ambiguities that
must be addressed, but it’s clear that
GDPR will significantly change the
data landscape.”
While the U.S. and a number of
other countries have adopted an opt-
out approach to data collection—es-
sentially, a consumer must instruct
a company if he or she doesn’t want
his or her data used or shared in cer-
tain ways—Europe has implemented
a more restrictive opt-in approach.
However, GDPR takes this concept to
a new and previously untested level.
Besides giving consumers near-total
control of their data, they can have
W
H E N T H E E U R O P E A N
U N I O N (EU) General
Data Protection Regu-
lation (GDPR) went
into effect on May 25,
2018, it represented the most sweeping
effort yet to oversee the way businesses
collect and manage consumer data.
The law, established to create consis-
tent data standards and protect EU
citizens from potential privacy abus-
es, sent ripples—if not tidal waves—
across the world.
GDPR gives European citizens great-
er control of their data while establish-
ing strong penalties for businesses
that do not comply. What is more, any
data that involves EU citizens or touch-
es EU companies is covered by GDPR.
The initiative replaces an older data
privacy initiative called the Data Pro-
tection Directive 95/46/EC, which was
introduced in 1995.
The implications and ramifications
are enormous—and the initiative’s
reach is global. GDPR will change ev-
erything from the way data collection
takes place to the way corporate data-
bases are designed and used. It also will
potentially change the way research
and development takes place, and
will impact cybersecurity practices, as
well as introducing a practical array of
challenges revolving around sites and
repositories where groups share com-
ments, information, and other data.
“It’s a groundbreaking initiative,”
says Brett M. Frischmann, Charles
Widger Endowed University Professor
in Law, Business, and Economics at Vil-
lanova University, and Affiliate Scholar
of the Center for Internet and Society
at Stanford Law School. “Europe has
flipped a switch and prompted recon-
sideration of how data can be collected,
managed, and used.” The EU takes the
position that a person owns his or her
data, and their privacy is a fundamen-
tal right that is “basic to the integrity of
a human being,” Frischmann adds.
Data Wars
Digital technology has inexorably
changed the face of privacy. Today,
there is a perception—and plenty of
evidence to support it—that personally
ident ...
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
An insight in the legal challenges and opportunities of Artificial Intelligence (AI). By Matthias Dobbelaere-Welvaert, managing partner of theJurists Europe.
Deloitte the case for disruptive technology in the legal profession 2017 Ian Beckett
1. The document discusses how disruptive technologies like artificial intelligence, big data, blockchain, and cloud computing are transforming the legal profession. These technologies increase efficiency, reduce costs, and create new areas of practice.
2. However, many legal professionals have been slow to adopt these technologies. For example, only 38% of lawyers currently use cloud computing. Those who do not learn to harness new technologies risk becoming obsolete as the industry changes.
3. The technologies are reshaping the business model of law firms. Jobs like basic document review that can be automated will disappear, reducing the need for junior lawyers. The firms of the future will have leaner workforces and draw talent from new disciplines like technology.
Jan 2017 Submission to AG Re: Metadata use in civil proceedingsTimothy Holborn
The document discusses challenges around privacy and data protection in the modern era of rapid technological change. It notes that key concepts like "data", "metadata", and "artificial intelligence" are not clearly defined in the 1979 Telecommunications Act and recommends updating the Act to address today's data-driven society. The document advocates for a decentralized, rights-based approach to "data democratization" to ensure data policies support societal advancement while protecting citizens' privacy and dignity.
This document provides an overview of digital transformation and big data. It discusses key trends driving digital transformation like digitalization, social media, and mobility. It also covers what big data is, various sources of big data, how insights can be gained from big data analysis, and some of the ethical considerations around big data. The document outlines approaches for analyzing big data, including dealing with false correlations and overfitting models to vast amounts of data.
Law, Ethics and Tech Aspects for an Irrevocable BlockChain Based Curriculum V...eraser Juan José Calderón
Law, Ethics and Tech Aspects for an Irrevocable
BlockChain Based Curriculum Vitae Created by Big
Data Analytics Fed by Internet of Things, Sensors and
Approved Data Sources. Vasilios Kanavas, Athanasios Zisopoulos & Konstantinos Spinthiropoulos
Ethics Case Study Review_JKostak_APA_StyleJohn Kostak
This document provides a summary and analysis of ethics issues arising in modern networked businesses. It discusses how the integration of corporate, social, and community networks blurs traditional information boundaries and policies. Key points addressed include:
- New stakeholders and dynamic engagement models require updated communications strategies.
- Ethics and governance issues become interwoven across functions as lines are blurred.
- Network security vulnerabilities increase with virtual networks, requiring updated privacy and security policies.
- There must be a balancing of customer privacy, security needs, and transparency demands with business interests.
- A new "Virtual Enterprise Ethics Engagement Model" is proposed to define and manage ethics in integrated virtual networks.
Digital Authoritarianism: Implications, Ethics, and SafegaurdsAndy Aukerman
Artificial intelligence, or AI, constitutes a common motif in science fiction literature – the aspect of a “robot uprising”, where AI becomes sufficiently advanced such that it surpasses human intelligence and escapes human control. Common perceptions of AI focus on the ethical and human impacts of a malevolent, artificially intelligent agent itself. In this document, I wish to instead focus on an equally important, and, I will argue, more plausible case of sufficiently advanced AI which poses immense risk to human activity: the use of AI in conjunction with big data for authoritarian rule and population control. In these scenarios, AI has no agency, and instead serves as a sufficiently advanced and intelligent tool for human agents. Throughout this document, I summarize current and potential applications of this type of AI, explore the ethical ramifications, and last, propose and evaluate solutions and safeguards.
Artificial Intelligence - intersection with compliance. How AI principles work with compliance principles around data protection. AI and Compliance. AI - SYSC 13.7 - FCA Compliance. AI and regulation. AI and FCA regulation. AI and ICO regulation.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
Organisations that embrace the move towards personalised technology can create more opportunities for collaboration and find new ways to grow the business.
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...IJCI JOURNAL
This study is focused on the ethics of Artificial Intelligence and its application in the United States, the
paper highlights the impact AI has in every sector of the US economy and multiple facets of the
technological space and the resultant effect on entities spanning businesses, government, academia, and
civil society. There is a need for ethical considerations as these entities are beginning to depend on AI for
delivering various crucial tasks, which immensely influence their operations, decision-making, and
interactions with each other. The adoption of ethical principles, guidelines, and standards of work is
therefore required throughout the entire process of AI development, deployment, and usage to ensure
responsible and ethical AI practices. Our discussion explores eleven fundamental 'ethical principles'
structured as overarching themes. These encompass Transparency, Justice, Fairness, Equity, NonMaleficence, Responsibility, Accountability, Privacy, Beneficence, Freedom, Autonomy, Trust, Dignity,
Sustainability, and Solidarity. These principles collectively serve as a guiding framework, directing the
ethical path for the responsible development, deployment, and utilization of artificial intelligence (AI)
technologies across diverse sectors and entities within the United States. The paper also discusses the
revolutionary impact of AI applications, such as Machine Learning, and explores various approaches used
to implement AI ethics. This examination is crucial to address the growing concerns surrounding the
inherent risks associated with the widespread use of artificial intelligence.
The document discusses the GDPR requirements for data masking and pseudonymization. It provides context on the GDPR and how it aims to update privacy laws for a modern, digital world. The GDPR introduces legal definitions for pseudonymization, which refers to approaches like data masking that secure personal data in a way that indirect identities are still protected. It highlights how data masking technologies can help companies comply with the GDPR while maintaining data quality for analysis. Companies that fail to implement appropriate measures like pseudonymization could face fines up to 4% of global turnover under the GDPR.
Presentation by Keita Nishiyama at the OECD Global Conference on Governance Innovation which took place in Paris on 13-14 January 2020. Further information is available at http://www.oecd.org/gov/regulatory-policy/oecd-global-conference-on-governance-innovation.htm.
Socializing digital work via the courts? Antonio Aloisi (Marie Skłodowska-Curie Fellow and Assistant Professor, IE Law School, IE University, Madrid)
Labour rights & the digital transition: https://www.etui.org/events/labour-rights-digital-transition
Full paper:
Aloisi, Antonio and De Stefano, Valerio, Regulation and the Future of Work: The Employment Relationship as an Innovation Facilitator. International Labour Review, 159, 1, pp. 47-69, 2020, Available at SSRN: https://ssrn.com/abstract=3572584
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Paper presented at "Perspectives on Global Crises: Challenges and Opportunities" - 16th Max Weber Fellows June Conference, 15-17 June 2022, Badia Fiesolana
Impact of GDPR on the pre dominant business model for digital economiesEquiGov Institute
A brief description of the impact the General Data Protection Regulation (GDPR) could have on the proposed move towards a digital economy, especially for the Caribbean
On Digital Markets, Data, and Concentric DiversificationBernhard Rieder
This document discusses how large tech companies like Google and Facebook have expanded from their original businesses through a strategy of concentric diversification. It argues that their accumulation of large data assets and algorithmic capabilities allows them to computerize new domains. For example, Google uses its knowledge bases and machine learning to expand from search into areas like self-driving cars. Facebook leverages its social graph and identity resolution to enter new ad tech businesses. The document analyzes how these companies' technological systems grow more valuable as their assets transfer to new sectors, creating economies of scale that affect market dynamics and relationships between firms.
16 C O M M U N I C AT I O N S O F T H E A C M N O.docxRAJU852744
16 C O M M U N I C AT I O N S O F T H E A C M | N O V E M B E R 2 0 1 8 | V O L . 6 1 | N O . 1 1
news
I
M
A
G
E
B
Y
M
I
X
M
A
G
I
C
with the advertisements that you ac-
cept. And I think people are becoming
more and more aware of the fact that
their personal data do have a value.”
Says Alison Cool, assistant profes-
sor of anthropology at the University
of Colorado, Boulder, “There are a
lot of questions and ambiguities that
must be addressed, but it’s clear that
GDPR will significantly change the
data landscape.”
While the U.S. and a number of
other countries have adopted an opt-
out approach to data collection—es-
sentially, a consumer must instruct
a company if he or she doesn’t want
his or her data used or shared in cer-
tain ways—Europe has implemented
a more restrictive opt-in approach.
However, GDPR takes this concept to
a new and previously untested level.
Besides giving consumers near-total
control of their data, they can have
W
H E N T H E E U R O P E A N
U N I O N (EU) General
Data Protection Regu-
lation (GDPR) went
into effect on May 25,
2018, it represented the most sweeping
effort yet to oversee the way businesses
collect and manage consumer data.
The law, established to create consis-
tent data standards and protect EU
citizens from potential privacy abus-
es, sent ripples—if not tidal waves—
across the world.
GDPR gives European citizens great-
er control of their data while establish-
ing strong penalties for businesses
that do not comply. What is more, any
data that involves EU citizens or touch-
es EU companies is covered by GDPR.
The initiative replaces an older data
privacy initiative called the Data Pro-
tection Directive 95/46/EC, which was
introduced in 1995.
The implications and ramifications
are enormous—and the initiative’s
reach is global. GDPR will change ev-
erything from the way data collection
takes place to the way corporate data-
bases are designed and used. It also will
potentially change the way research
and development takes place, and
will impact cybersecurity practices, as
well as introducing a practical array of
challenges revolving around sites and
repositories where groups share com-
ments, information, and other data.
“It’s a groundbreaking initiative,”
says Brett M. Frischmann, Charles
Widger Endowed University Professor
in Law, Business, and Economics at Vil-
lanova University, and Affiliate Scholar
of the Center for Internet and Society
at Stanford Law School. “Europe has
flipped a switch and prompted recon-
sideration of how data can be collected,
managed, and used.” The EU takes the
position that a person owns his or her
data, and their privacy is a fundamen-
tal right that is “basic to the integrity of
a human being,” Frischmann adds.
Data Wars
Digital technology has inexorably
changed the face of privacy. Today,
there is a perception—and plenty of
evidence to support it—that personally
ident.
16 C O M M U N I C AT I O N S O F T H E A C M N O.docxaulasnilda
16 C O M M U N I C AT I O N S O F T H E A C M | N O V E M B E R 2 0 1 8 | V O L . 6 1 | N O . 1 1
news
I
M
A
G
E
B
Y
M
I
X
M
A
G
I
C
with the advertisements that you ac-
cept. And I think people are becoming
more and more aware of the fact that
their personal data do have a value.”
Says Alison Cool, assistant profes-
sor of anthropology at the University
of Colorado, Boulder, “There are a
lot of questions and ambiguities that
must be addressed, but it’s clear that
GDPR will significantly change the
data landscape.”
While the U.S. and a number of
other countries have adopted an opt-
out approach to data collection—es-
sentially, a consumer must instruct
a company if he or she doesn’t want
his or her data used or shared in cer-
tain ways—Europe has implemented
a more restrictive opt-in approach.
However, GDPR takes this concept to
a new and previously untested level.
Besides giving consumers near-total
control of their data, they can have
W
H E N T H E E U R O P E A N
U N I O N (EU) General
Data Protection Regu-
lation (GDPR) went
into effect on May 25,
2018, it represented the most sweeping
effort yet to oversee the way businesses
collect and manage consumer data.
The law, established to create consis-
tent data standards and protect EU
citizens from potential privacy abus-
es, sent ripples—if not tidal waves—
across the world.
GDPR gives European citizens great-
er control of their data while establish-
ing strong penalties for businesses
that do not comply. What is more, any
data that involves EU citizens or touch-
es EU companies is covered by GDPR.
The initiative replaces an older data
privacy initiative called the Data Pro-
tection Directive 95/46/EC, which was
introduced in 1995.
The implications and ramifications
are enormous—and the initiative’s
reach is global. GDPR will change ev-
erything from the way data collection
takes place to the way corporate data-
bases are designed and used. It also will
potentially change the way research
and development takes place, and
will impact cybersecurity practices, as
well as introducing a practical array of
challenges revolving around sites and
repositories where groups share com-
ments, information, and other data.
“It’s a groundbreaking initiative,”
says Brett M. Frischmann, Charles
Widger Endowed University Professor
in Law, Business, and Economics at Vil-
lanova University, and Affiliate Scholar
of the Center for Internet and Society
at Stanford Law School. “Europe has
flipped a switch and prompted recon-
sideration of how data can be collected,
managed, and used.” The EU takes the
position that a person owns his or her
data, and their privacy is a fundamen-
tal right that is “basic to the integrity of
a human being,” Frischmann adds.
Data Wars
Digital technology has inexorably
changed the face of privacy. Today,
there is a perception—and plenty of
evidence to support it—that personally
ident ...
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
An insight in the legal challenges and opportunities of Artificial Intelligence (AI). By Matthias Dobbelaere-Welvaert, managing partner of theJurists Europe.
Deloitte the case for disruptive technology in the legal profession 2017 Ian Beckett
1. The document discusses how disruptive technologies like artificial intelligence, big data, blockchain, and cloud computing are transforming the legal profession. These technologies increase efficiency, reduce costs, and create new areas of practice.
2. However, many legal professionals have been slow to adopt these technologies. For example, only 38% of lawyers currently use cloud computing. Those who do not learn to harness new technologies risk becoming obsolete as the industry changes.
3. The technologies are reshaping the business model of law firms. Jobs like basic document review that can be automated will disappear, reducing the need for junior lawyers. The firms of the future will have leaner workforces and draw talent from new disciplines like technology.
Jan 2017 Submission to AG Re: Metadata use in civil proceedingsTimothy Holborn
The document discusses challenges around privacy and data protection in the modern era of rapid technological change. It notes that key concepts like "data", "metadata", and "artificial intelligence" are not clearly defined in the 1979 Telecommunications Act and recommends updating the Act to address today's data-driven society. The document advocates for a decentralized, rights-based approach to "data democratization" to ensure data policies support societal advancement while protecting citizens' privacy and dignity.
This document provides an overview of digital transformation and big data. It discusses key trends driving digital transformation like digitalization, social media, and mobility. It also covers what big data is, various sources of big data, how insights can be gained from big data analysis, and some of the ethical considerations around big data. The document outlines approaches for analyzing big data, including dealing with false correlations and overfitting models to vast amounts of data.
Law, Ethics and Tech Aspects for an Irrevocable BlockChain Based Curriculum V...eraser Juan José Calderón
Law, Ethics and Tech Aspects for an Irrevocable
BlockChain Based Curriculum Vitae Created by Big
Data Analytics Fed by Internet of Things, Sensors and
Approved Data Sources. Vasilios Kanavas, Athanasios Zisopoulos & Konstantinos Spinthiropoulos
Ethics Case Study Review_JKostak_APA_StyleJohn Kostak
This document provides a summary and analysis of ethics issues arising in modern networked businesses. It discusses how the integration of corporate, social, and community networks blurs traditional information boundaries and policies. Key points addressed include:
- New stakeholders and dynamic engagement models require updated communications strategies.
- Ethics and governance issues become interwoven across functions as lines are blurred.
- Network security vulnerabilities increase with virtual networks, requiring updated privacy and security policies.
- There must be a balancing of customer privacy, security needs, and transparency demands with business interests.
- A new "Virtual Enterprise Ethics Engagement Model" is proposed to define and manage ethics in integrated virtual networks.
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Artificial Intelligence is Watching You at Work - Digital Surveillance, Employee Monitoring & Regulatory Issues from a Civil Law Perspective
1. Artificial Intelligence
is Watching You at Work
Digital Surveillance, Employee
Monitoring & Regulatory Issues
from a Civil Law Perspective
Antonio Aloisi European University Institute, Florence
Elena Gramano Goethe University, Frankfurt
ILO, Geneva, 28 Feb. - 1 Mar. 2019
2. outline of today’s presentation
AI & control
power
2
GDPR &
automated
processes
National
cases
(FR, DE, IT)
Final
remarks
1 2 3 4
3. big data analytics and algorithmic governance
POWER RELATIONSHIPS
New technologies are reshaping the
world of work in an ever-growing number
of fields. They have been redesigning
workplace interactions and power
relationships since ever, but the full
potential of some digital instruments has
not been unleashed in terms of pace,
scale and scope
AI BOOM
The wave of industrial development is
acclaimed as the “second machine age”,
boosted by the proliferation of
cyber-physical infrastructure and
interconnected systems making possible
practices of surveillance and profiling,
the resulting gigantic datasets lay the
groundwork for the AI boom
Powerful and multifarious innovations which, when it comes to recruiting,
organizing, monitoring and disciplining the workforce, can be considered as
an effective combination of big data analytics and algorithmic governance.
3
>
4. “instead of facilitating an emancipating new
environment, intrusive technology are
used to deepen hierarchy and control
over work performance, team dynamics,
usage habits, social behavior and even
biometric characteristics
5. Only recently, European and
domestic institutions started
considering how to update
existing regulation in order to face
the complex and far-reaching
challenges posed by tech devices
and, more specifically, by artificial
intelligence, a general-purpose
application able to mimic adaptive
and predictive “functions that
humans associate with their own
intelligence”
5
6. AI & control power:
a preliminary
conceptualization
6
7. direct interaction with new generation machineries
▸ increasingly blurred boundaries between professional and private lives,
▸ cheap, massive and less visible production, capturing, collection and
usage of data,
▸ effective cloud storage and computing, machine learning, Internet of
Things (IoT), neuronal networks and mobile robotics...
represents the lifeblood of the current remodeling, “creating significant
challenges to privacy and data protection” [WP29, Opinion 2/2017 on data
processing at work, WP 249, 8 June 2017]
today almost every occupation, standard or non-standard and
across different contexts (NOT only gig work!), involves a direct
interaction with tech devices, or even a complete reliance on them.
7
8. beyond the workless future
It is not the number of jobs lost
through advanced automation
(artificial artificial intelligence)
which should worry public opinion
most but rather the subtle potential
of AI and algorithms, leading to a
model of control and appraisal
without an intuitive link between
what is done when “logged-in” and
how it is assessed.
8
9. the ILO’s view
process
management
tasks
matching tasks
ride-hailing services,
accommodation
services, retail or human
resource management
classification tasks
9
“reducing costs on finding
customers or suppliers and
offering less expensive
solutions to their growing
customer base”
“integrate supply chains
through better information
about product quality,
certification schemes and
market conditions”
“recognition techniques in
relation to the increase in
surveillance”
10. AI’s functions
scan and hire
control and
discipline
10
data, data, data
for what else?
>
assess
>
11. Janus-faced
While AI may embody several
benefits for workers and even
save lives as many employers are
starting to use computers to
check whether employees are
wearing appropriate safety gears,
goggles and gloves, it may also
endanger live as it increases
pressure to meet deadlines,
statistics and KPI.
technology is not neutral
Lack of transparency
Tracing, scoring, incentivization,
rankings and all the metrics can
be manipulated and repurposed
to infer unspecified
characteristics or to predict
unknown behaviors + systems
that, once designed and
calibrated, run automatically and
gather enormous amounts of
granular data about workers’.
11
12. all AI on us: the surveillance potential of technologies
What is old
AI unique because of
its blend of existing
authoritative
practices, namely
data/people analytics
and algorithmic
governance, in an
optimized manner.
What is new
this “marriage of
convenience”
explains in part why
there are several
concerns about some
of the concrete
functions of this
innovation.
Why do we care
automated choices
for recruiting,
remuneration & even
dismissals, giving
free rein to
discriminatory
biases, exacerbating
inequality.
12
13. a “genetic variation” of managerial prerogative
▸ invasive devices, from badges to tablets, from wearables to
exoskeletons, from cloud software to virtual personal assistant,
constitute a burgeoning terrain for new forms of surveillance
▸ from a legal perspective, they constantly collect, produce, share and
combine data, thus leading to a “genetic variation” of organizational,
control and disciplinary prerogative, considered as the core of the
employment contract, and to informational asymmetry
the prevailing approach is uncritical, and a considerable number of
“users” seem ready to accept a renunciation of privacy so as not to
forego the access to services presented as sources of connection,
optimization, convenience, and pleasure
13
14. A question worth asking is whether authority
today is the same as authority in the past.
The scope of application of provisions on
data protection – based on an “analogue”
understanding of technology – in several civil
law jurisdictions may fall short in providing
an up-to-date model, capable of coping with
unforeseeable technological advances.
14
#1 Research questions
15. Assessing the effects of AI and algorithms
on employment relationships, with a view to
understanding the way how legal and social
institutions should act, react or adapt to a
potential experience of unprecedented “total
surveillance” in the workplace, entrenching
command-and-control relationships between
management and workers.
15
#2 Research questions
16. Examining whether and to what extent the
current legal framework – in EU
countries/civil law systems – is suited to
accommodate and regulate the “augmented”
magnitude of control power: is there a need
for new legislation or is a more effective
enforcement of existing regulation enough?
How can competing interests be reconciled?
16
#3 Research questions
17. 2. Europe taking the lead
“GDPR” and the balance between
companies’ legitimate interests &
privacy expectations of employees
17
18. an “integrated” overview and the “Brussels effect”
▸ the “integrated” study of the General Data Protection Regulation (GDPR)
→ a step forward, leading to a homogenization of national models
instead of promoting a mere harmonization
▹ hailed as one of the best examples of the so-called “Brussels
effect”, the “global power that the EU is exercising through its legal
institutions and standards, successfully export[ing] that influence to
the rest of the world” (Europeanization).
▹ international companies decided to voluntarily implement such
provisions globally → international convergence
▹ the law extends its reach beyond the boundaries of the EU to any
company processing the data of EU citizens
18
19. what’s new
▸ adopted in April 2016 and entered into force in May 2018
▸ Regulation (EU) 2016/679 does not pursue anymore primarily
commercial or economic interests
▸ Recital 4 of GDPR states that the right to the protection of personal data
“is not an absolute right” and “must be considered in relation to its
function in society and be balanced against other fundamental rights, in
accordance with the principle of proportionality”
▸ personal data “which [has] undergone pseudonymisation, which could be
attributed to a natural person by the use of additional information”
should be considered as falling within its scope
19
20. delegated powers
▸ Art. 88 of the GDPR specifies that Member States “may, by law or by
collective agreements, provide for more specific rules to ensure the
protection of the rights and freedoms in respect of the processing of
employees’ personal data in the employment context […]”.
▸ Room for maneuver for the design of “integrative legislation designed to
respond to the risk connected to big data analytics in the employment
relationship”, by devising procedural rules more incisively.
▸ Whether or not social partners would seize this opportunity by imposing
their agenda to the rule-makers still has to be determined
▹ collective bargaining may well represent an effective and flexible way to
“negotiate” the digital transformation of work
20
21. definitions
▸ “processing” = “any operation or set of operations which is performed on
personal data or on sets of personal data, whether or not by automated
means, such as collection, recording, organisation, structuring, storage,
adaptation or alteration, retrieval, consultation, use, disclosure by
transmission, dissemination or otherwise making available, alignment or
combination, restriction, erasure or destruction” (Art. 4, 1 and 2).
▸ the concept of personal data is broad, defined as “any information
relating to an identified or identifiable natural person (‘data subject’)”.
▸ only personal data containing “anonymous information, […] which does
not relate to an identified or identifiable natural person” are excluded
from the scope of the GDPR
21
22. complementary texts
GDPR
focus on a limited
number of strictly
labor-related
provisions
Opinion 2/2017
on data
protection at
work, adopted
in June 2017*
Guidelines on
automated
decision
making &
profiling for the
purposes of
GDPR
22
* 9 scenarios: recruitment process, in-employment screening, monitoring ICT usage both at and outside the
workplace (e.g. home and remote working, “bring your own device” practices, wearable devices), monitoring of
time and attendance, or through video systems, vehicle applications and processing involving the transfer of
employee data to third parties or international operators
Independent EU Advisory Body on Data Protection and Privacy (Article 29 Working Party, “WP29”), comprised of
the heads of the national data protection authorities, that anticipate the future work of its successor, the European
Data Protection Board, in issuing guidelines, recommendations and best practices
23. main principles
▸ Art. 5(1)(a) processing must be:
▹ lawful,
▹ fair,
▹ transparent
▸ Other principles incorporated:
▹ purpose limitation,
▹ data minimization,
▹ accuracy,
▹ storage limitation,
▹ integrity,
▹ confidentiality,
▹ accountability.
23
▸ Art. 6, conditions of lawfulness:
▹ consent by the data subject,
▹ necessity in a contract,
▹ compliance with a legal obligation,
▹ protection of vital interest of the
data subject,
▹ performance of a task carried out
in the public interest or in the
exercise of official authority
vested in the controller,
▹ legitimate interest, provided that
the interests or fundamental rights
and freedom of the data subject
are not jeopardized
24. consent in the context of an employment relationship
▸ as regards data protection in the context of an employment relationship,
according to Article 7, “consent” is not in itself sufficient
▸ the requirement is reinforced by obligations of intelligibility, clarity and
transparency with respect to the modalities of the request and by
strengthening the right to withdraw consent (art. 7(2)(3)).
▸ in particular, in order to assess the genuineness of such a consent,
“utmost account shall be taken of whether, inter alia, the performance of
a contract is conditional on consent”.
▹ as stated in the WP29 Opinion 2/2017’s introduction, “employees
are seldom in a position to freely give, refuse or revoke consent”
24
25. consent in the context of an employment relationship
▸ both consent and a legitimate interest are in itself not sufficient to
override the rights and freedoms of employees
▸ a proportionality test should be undertaken prior to its commencement
to consider whether the processing is necessary to achieve a legitimate
purpose, whether the processing outweighs the data protection right, as
well as the measures that have to be taken to ensure that infringements
of the rights to private life and secrecy of communications are limited to
a minimum (“in the least intrusive manner possible and targeted to the
specific area of risk”)
▸ this can form part of a Data Protection Impact Assessment (DPIA),
according to art. 35 of the GDPR
25
26. key pitfalls and the risk of circumventing the GDPR
▸ many companies have reacted to restrictions based on reinforced
consent and ban on profiling:
▹ by asking individuals to consent to various uses of their personal
data for very widely defined purposes, by getting data subjects to
accept a broadly phrased consent agreement in the first instance –
a practice which has become more questionable
▹ justifying the use of data for statistical purposes, which constitute
an explicitly permitted reuse of data without explicitly abandoning
the purpose limitation principle (“for archiving purposes in the public
interest, scientific or historical research purposes or statistical
purposes”, art. 5(1)(b)) + implementing “appropriate safeguards”
26
28. inferential analytics
▸ inferential analytics, one of the strongest AI applications aimed at
deducing conducts by simply extrapolating patterns and recurrences
from large amounts of data, seem largely unregulated
▹ an unintelligible “black box”, which is indented to keep most workers
in the darkness as regards tech-driven strategies
▹ although partially autonomous, they answer to specific
organizational needs and reflect managerial preferences
▹ they are increasingly obscure and unaccountable, yet consequential
the answer? Article 22 regulating “automated individual
decision-making” processes
28
29. article 22 (automatization of organizational procedures)
▸ this is probably the most forward-looking chapter of the Regulation
▸ the Regulation ought to be understood as precluding “a decision based
solely on automated processing, […], which produces legal effects
concerning [the data subject] or similarly significantly affects him or her”
▸ the data subject, i.e. the worker, has the right not to be subject to
decisions “based solely on automated processing, including profiling,
which produces legal effects”
▹ Article 4(4) defines “profiling” as “any form of automated processing of personal data
consisting of the use of personal data to evaluate certain personal aspects relating to a
natural person, in particular to analyse or predict aspects concerning that natural person’s
performance at work, economic situation, health, personal preferences, interests,
reliability, behaviour, location or movements”.
29
30. automated decision-making processes at work
▸ Article 22 does not apply in the case when the automated process
▹ “(a) is necessary for entering into, or performance of, a contract
between the data subject and a data controller”,
▹ (b) when authorized by Union or Member State,
▹ “(c) is based on the data subject’s explicit consent”.
▸ in the (a) and (c) case, the data controller “shall implement suitable
measures to safeguard the data subject’s rights, freedoms & interests”.
▸ if the decision is based on “special” categories of data as defined in art.
9 of the GDPR (i.e. sensitive data), automated decision-making
processes are only allowed on the basis of explicit consent or substantial
public interest, as long as safeguards are put in place
30
31. “the right to obtain human intervention”
▸ under article 22(3), “the right to obtain human intervention on the part of
the controller, to express his or her point of view and to contest the
decision” is established
▹ however, in a machine learning or AI context, “it is not clear who this ‘human’
should be and how to review a process that may have been based on third
party algorithms, pre-learned models or data sets (individuals’ personal data)
or on opaque machine learning models”
▹ explanation may not be feasible in situations where decisions are taken in
response to data in real time
▸ the question arises of how individuals, “who have differing levels of
comprehension”, could access, understand and challenge the info
31
33. rationalizing the bureaucratic power of employers
▸ Employment law has been conceived as a set of rules aimed at
rationalizing the managerial prerogative, including surveillance authority
(watchfulness of management), since its emergence
▸ the issue of “humanizing” the bureaucratic power of employers by
means of mandatory provisions or collectively taken countermeasures is
a defining feature of labor law in various legal systems all over Europe,
the very core of labour regulation
→ the traditional legal arsenal regulating the monitoring power of
employer and the right to privacy of employees, compounded by the
most recent regulatory interventions, such as the EU General Data
Protection Regulation, is a starting point
33
35. ▸ general right of personality (allgemeines Persönlichkeitsrecht)
▹ important limit to the question of which measures are permitted and
which are prohibited when monitoring an employee
▹ not expressly mentioned in the German Constitution (Grundgesetz,
GG) but represents the consolidated interpretative result of the
principle of human dignity (it shall be “inviolable” – unverletztlich)
and the right to the free development of personality
▸ Federal Constitutional Court emphasises the openness of the general
right of personality to development, which is why a conclusive definition
of the general right of personality has deliberately not yet been made
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Germany
36. ▸ monitoring of employees at the workplace is only lawful under strict
limits → Data Protection Act (BDSG):
1) Interferences with the right of personality must be justified by
legitimate interests of the employer, other holders of fundamental
rights or other important objectives;
2) expressly permits the processing of employees’ data on the basis of
“collective agreements” (Kollektivvereinbarungen and
Betriebsvereinbarungen) → cannot justify a violation of the
fundamental general right of personality.
→ Works Councils must be involved on automated processes of personal data +
data protection officer appointed if more than 9 employees involved
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Germany
37. Germany
▸ BUT great relevance of the fundamental right perspective:
Prohibition of total surveillance
Secret surveillance only exceptionally permitted
No excessive pressure to adapt
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38. ▸ Several provisions of the French Labor Code (“Code du travail”) and of
the Law “Technologies and Freedoms” (“Informatique et libertés”, passed
following the Lyon Caen’s report) define a framework for conditions and
restrictions on the use of technologies at the workplace
▹ French workers have a fundamental right to private lige life (“le droit à une vie
personnelle”) → the right to privacy
▹ any restriction of the general right to privacy may only be justified if it is
aimed at the assessment of the work done by employees
▹ no system of surveillance or data collection may be installed without prior
notice to the employees and to the employees’ representatives (Vigneau)
▹ evidence collected without information are illicit and cannot be used for the
purposes of a disciplinary case
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France
39. France
▸ Three principles in the French Civil Code:
▹ transparency or loyalty;
▹ proportionality;
▹ relevance.
▸ If the employer wishes to install a surveillance device (e.g. video,
geolocalization et similia), a mandatory conciliation procedure must be
followed (double information, individual and collective, of the workforce.
▹ the employee representatives (works council and health and safety
committee) must be consulted, the employees must be informed
▹ tools performing or allowing personal data processing are to be “declared” to
the National Commission of Data Processing and Freedoms (“Commission
nationale de l’informatique et des libertés”, CNIL)
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40. Italy
▸ Article 41 Italian Constitution: private economic initiative is free. It may
not be carried out in conflict with social utility or in such a way as to
cause damage to security, freedom or human dignity
▹ Principle of human dignity often used by case law to limit the employer’s
private power
▸ Article 4 of the Workers’ Statute → first formulation in 1970
▹ anticipated the issues of protection of the employee from the employer
control before any rule on privacy existed in Italy
▹ traditionally separated perspectives: the employer’s control on the workplace
/ general privacy issues
▹ recent reform in 2015 deeply changed the disposition, that shall now be
integrated by the Privacy Code
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41. Italy
1) installation of remote control instruments → audiovisual equipment and
other instruments from which derives even the possibility of remote
control of the activities of workers can be used exclusively for
organizational and production needs, for occupational safety and for the
protection of the company’s assets, and may be installed under the
condition that a collective agreement has been signed or with prior
administrative authorization.
2) The conditions do not apply to tools used by the worker to perform the
working activity and to record the access and presence in the premises of
the company.
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42. Italy
3) Full possibility to use the information found through instruments
legitimately installed for all purposes related to the employment
relationship provided that the worker is given adequate information on
the methods of use of the instruments and the implementation of
controls and in compliance with the provisions of the so-called Privacy
Code.
▹ Great space for the employer to monitor the employees through
“working instruments”
▹ Anachronistic distinction between “control instruments” and
“working instruments”
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43. comparative insights on DE, FR, IT
key principle:
awareness of
control
employees shall be
made aware of being
subject of the
(remote) control by
the employer
control power as a
precondition of
disciplinary power
neglected the pure
invasion of the
individual sphere,
reverse the
relationship
is consent
(individual or
collective) enough?
important to define
new rules on consent
that may be insincere
in the context of
employment
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45. is GDPR already obsolete?
▸ GDPR seems conceived on an old-fashioned understanding of how data
are used, in turn based on a three-phase system (as classified by Oosten,
acquisition, analysis, and application) and traditional categorisations
(personal, sensitive, ...)
▸ Wacther has leveled well-founded criticism at the GDPR, “focus[ing] too
much on the input stage, meaning when data is collected, but not enough
on how it is assessed”
given the pace of change, employers may find themselves being
able to make connections they had not anticipated or disclosed:
once the data is lawfully obtained, very little control or is reserved to
inferential analytics, which remains a “no man’s land”
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46. “despite he myth of expanded independence,
AI impacts on freedom, privacy, but also
autonomy and moral reasoning, which is
much more relevant in a society in which
the traditionally separation between private
and work life is dissolving.
47. the way forward
▸ although digital transformation advocates postulate the obsolescence of
existing legal categories, social institutions are places in which forces
are in equilibrium between “freedom and equality, risk and solidarity,
efficiency and sociality” (Romagnoli, 2003)
▸ since social dynamics and implications are neither neutral nor
exogenous, there is a need to create an atmosphere of trust and
accountability regarding the development and use of AI and algorithms
if some of the current results are dystopian, guiding the process of
digital transformation in an informed, fair and sustainable way is the
only recipe for meeting the most ambitious and emancipating
expectations of both the workplace and contemporary society.
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