Week 1 Big Tech & The fundamentals of regulation 3
What does Big Tech mean? 3
Main features of big tech and challenges: 3
José van Dijck, The platform society as a contested concept 5
What is regulation? (Ogus and public/private interest) 5
How can regulating (big tech) be justified (Baldwin rationales)? 7
How to regulate big tech? (regulation pyramid) 10
Why to regulate big tech? (three points) 11
Who should regulate big tech? 12
When to regulate big tech? 13
Week 2 Competition law, abuse of dominance and platform power 13
European Competition law 13
Article 102 TFEU: Prohibition on the abuse of dominance 14
Zooming in further: specific cases of abuse of a dominant position 16
Digital markets, platform power and characteristics of platforms 18
What are the concrete limits and non-market challenges to Article 102 TFEU? Examples
of non-market concerns. 19
Should competition law deal with non-market public issues? 20
Key challenges for Article 102 TFEU regarding Big Tech: 22
The Digital Markets Act and Digital Services Act (Amazon) 22
Khan: Sources of Tech Platform Power. Breaking down market power: gatekeeper /
leveraging / information 22
Week 3 Regulating online disinformation 24
What is disinformation / misinformation / malinformation? 24
Why is disinformation harmful? 25
How is disinformation regulated in the EU (and the code of practice)? 25
Is the Code of Practice/self-regulation an effective means to regulate online
disinformation? code of conduct more top down 26
EU Code of Practice on Disinformation, 6 categories of commitments (+ sub
measures): 26
Should the EU enact new legislation to counter the problem of online disinformation? (!)
27
How can we improve the regulation of online disinformation? 28
IVIR, the legal framework on the dissemination of disinformation through Internet
services and the regulation of political advertising (approaches) 28
Final report of the independent high level group on fake news and online
disinformation 29
Fake news: False fears or real concerns? (not just an umbrella term) 30
Internet monetization 31
Week 4 Intermediary Liability & Platform Responsibility 32
Intermediary liability for unlawful content (What must platforms remove) and debates and
criticism on safe harbor (HC) 33
, Key debates and key criticisms on the safe harbor regime (notice and takedown) and
intermediary liability 34
Why are platforms bad at enforcing content rules? 35
Steps for intermediary liability in the e-commerce directive (ls a platform liable for content
of users’ posts): (!) 36
Basis of content moderation and possibilities to contest 37
Content moderation and fundamental rights in relation to platforms 37
Platforms responsibility for unlawful content (What must platforms not remove?) (right to
free speech?) (HC) 38
Legal instruments to tackle hate speech 40
S. Kulk, The E-Commerce Directive and liability 41
IViR, Study of fundamental rights limitations for online enforcement through
self-regulation 42
N. Alkiviadou, Hate speech on social media networks: Towards a regulatory
framework? 45
Week 5 Big Tech and Personal Data 46
What is meant by Personal Data? 46
Examples of identifiers or identifiability. Is this personal data? 47
Special category of personal data 48
What is meant by Processing? 50
GDPR’s Framework of Legal Basis for processing data (!) 50
Principles of data protection law 52
Investigating personal data handling according to the GDPR 53
Purpose of data protection Law 53
What does Big Tech do for the GDPR? 54
Problems of the GDPR 54
P. Voigt & A. von dem Bussche, The EU General Data Protection Regulation
(GDPR): A practical guide 55
Data portability 57
,Week 1 Big Tech & The fundamentals of regulation
What does Big Tech mean?
Big Tech, also known as the Tech Giants, refers to the largest information technology
industry companies based on market capitalization (beurswaarde). Large companies that
provide digital services through technology (Goldman Sachs). There is no legal definition.
They exist mostly online and are huge in size (earn trillions of dollars) and have a big
influence on society. Their only option is to grow (by mergers and acquisitions to strengthen
the user base/acquiring a technology to incorporate in their services/eliminating competitors
and buying out the market) or to perish. For example Google started as a search engine and
now they are gigantic. For example Amazon: owns a dozen companies in all kinds of
different sectors. It’s a whole ecosystem and it keeps growing. More and more they move in
the public sector (Google Schools, Google healthcare, Amazon healthcare) (Jos van Dijk).
The Big Five Tech Companies: Google, Apple, Microsoft, Meta (former Facebook), Amazon.
Main features of big tech and challenges:
- Economic: Big Tech companies are a selected group of providers with big market
shares (max market capitalization). Big tech is active on several technology markets
with an monopolistic or oligopolistic nature. Big tech has different specific
characteristics:
Network effects: Increased numbers of people or participants improve the value of a good or
service.
Vertical integration: Through mergers and acquisitions allows Big Tech companies to grow
even further. It is the strategy that companies use to control their own suppliers,
distributors, or retail stores to control their value or supply chain. Data flows can
be connected in the back end across all three levels of the ecosystem (e.g. google
chrome laptops have built in software, google cloud, google search and maps, but
also google education).
I. A classic supply chain moves from upstream to downstream, where the
raw material is transformed into products, moved through logistics and
distribution to final customers. A data supply chain moves in the opposite
direction. The raw data is “sourced” from the customer/user. As it moves
downstream, it gets processed and refined by proprietary algorithms and
stored in data centers.
II. in the digital world, vertical integration happens when a company can control the
primary access points to acquire data from consumers 🡪 also look at gatekeeper
power
, Economy of scale (schaalvoordeel ontstaan door vergroten productiecapaciteit): Many
technology products boast a high economy of scale, particularly software. This is due to
having more fixed costs than marginal costs. As a result, the cost of creating more of your
software program is minimal-to-nothing. Once you’ve paid the fixed costs of creating the
software to begin with, mass producing it is as simple as copy and paste. You simply reuse
the code. The economy of scale means successful tech companies can see their profits
snowball. So, this side of tech economics is a major contributing factor to the tech market
monopoly phenomenon.
Economy of scope (synergievoordelen ontstaan door één productiefaciliteit voor twee
producten te gebruiken): refers to the reduced expense of creating a range of distinct products
using shared resources. The idea is to create a complementary set of products while sharing
fixed costs. For software, companies might produce a range of programs and feature sets.
(Using the same development office and tools.) For hardware, a computer manufacturer
might leverage their resources to also make tablets and laptops. Technology quite often has a
good economy of scope due to low marginal costs. As a result, you capitalize on getting the
most you can out of your fixed costs. (Which are then offset by the good economy of scale.)
Challenges: No meaningful rivalry due to the monopolistic/oligopolistic structure of the
companies and abuse of dominance.
- Informational: Big Tech companies have brought social change via tech because
people are now socializing online. They have become central actors in our
information societies and they control our access to news/content/information/facts.
Challenges: Disinformation, fake news, deep fakes, filter bubbles, hate speech. Influence on
democracy because they control our access to information and they facilitate the spread of
disinformation (filter bubbles, algorithmes that only show you what you want to see) or hate
speech.
- Infrastructural: Hardware (the hosting servers), login to other services online, data
and algorithms. Besides, the physical infrastructures (e.g. transportation), multi-level
markets. (Personal) data processing and data collection is a key ingredient of their
business models (privacy breaches and scandals)
- Societal and democratic: Big Tech companies have a public role/function,
connecting people, entertainment, and resource sharing: In computing, a shared
resource, or network share, is a computer resource made available from one host to
other hosts on a computer network.
Challenges: Addictive design, impact on health/public health, influencing elections (so
influence on our democracies). Big tech can pose a danger to some of our fundamental rights,
for example our right to privacy (by collecting so much of your personal data) or our freedom
of speech (for example moderating online content in a way that can intrude this (censorship).
What does Big Tech mean? 3
Main features of big tech and challenges: 3
José van Dijck, The platform society as a contested concept 5
What is regulation? (Ogus and public/private interest) 5
How can regulating (big tech) be justified (Baldwin rationales)? 7
How to regulate big tech? (regulation pyramid) 10
Why to regulate big tech? (three points) 11
Who should regulate big tech? 12
When to regulate big tech? 13
Week 2 Competition law, abuse of dominance and platform power 13
European Competition law 13
Article 102 TFEU: Prohibition on the abuse of dominance 14
Zooming in further: specific cases of abuse of a dominant position 16
Digital markets, platform power and characteristics of platforms 18
What are the concrete limits and non-market challenges to Article 102 TFEU? Examples
of non-market concerns. 19
Should competition law deal with non-market public issues? 20
Key challenges for Article 102 TFEU regarding Big Tech: 22
The Digital Markets Act and Digital Services Act (Amazon) 22
Khan: Sources of Tech Platform Power. Breaking down market power: gatekeeper /
leveraging / information 22
Week 3 Regulating online disinformation 24
What is disinformation / misinformation / malinformation? 24
Why is disinformation harmful? 25
How is disinformation regulated in the EU (and the code of practice)? 25
Is the Code of Practice/self-regulation an effective means to regulate online
disinformation? code of conduct more top down 26
EU Code of Practice on Disinformation, 6 categories of commitments (+ sub
measures): 26
Should the EU enact new legislation to counter the problem of online disinformation? (!)
27
How can we improve the regulation of online disinformation? 28
IVIR, the legal framework on the dissemination of disinformation through Internet
services and the regulation of political advertising (approaches) 28
Final report of the independent high level group on fake news and online
disinformation 29
Fake news: False fears or real concerns? (not just an umbrella term) 30
Internet monetization 31
Week 4 Intermediary Liability & Platform Responsibility 32
Intermediary liability for unlawful content (What must platforms remove) and debates and
criticism on safe harbor (HC) 33
, Key debates and key criticisms on the safe harbor regime (notice and takedown) and
intermediary liability 34
Why are platforms bad at enforcing content rules? 35
Steps for intermediary liability in the e-commerce directive (ls a platform liable for content
of users’ posts): (!) 36
Basis of content moderation and possibilities to contest 37
Content moderation and fundamental rights in relation to platforms 37
Platforms responsibility for unlawful content (What must platforms not remove?) (right to
free speech?) (HC) 38
Legal instruments to tackle hate speech 40
S. Kulk, The E-Commerce Directive and liability 41
IViR, Study of fundamental rights limitations for online enforcement through
self-regulation 42
N. Alkiviadou, Hate speech on social media networks: Towards a regulatory
framework? 45
Week 5 Big Tech and Personal Data 46
What is meant by Personal Data? 46
Examples of identifiers or identifiability. Is this personal data? 47
Special category of personal data 48
What is meant by Processing? 50
GDPR’s Framework of Legal Basis for processing data (!) 50
Principles of data protection law 52
Investigating personal data handling according to the GDPR 53
Purpose of data protection Law 53
What does Big Tech do for the GDPR? 54
Problems of the GDPR 54
P. Voigt & A. von dem Bussche, The EU General Data Protection Regulation
(GDPR): A practical guide 55
Data portability 57
,Week 1 Big Tech & The fundamentals of regulation
What does Big Tech mean?
Big Tech, also known as the Tech Giants, refers to the largest information technology
industry companies based on market capitalization (beurswaarde). Large companies that
provide digital services through technology (Goldman Sachs). There is no legal definition.
They exist mostly online and are huge in size (earn trillions of dollars) and have a big
influence on society. Their only option is to grow (by mergers and acquisitions to strengthen
the user base/acquiring a technology to incorporate in their services/eliminating competitors
and buying out the market) or to perish. For example Google started as a search engine and
now they are gigantic. For example Amazon: owns a dozen companies in all kinds of
different sectors. It’s a whole ecosystem and it keeps growing. More and more they move in
the public sector (Google Schools, Google healthcare, Amazon healthcare) (Jos van Dijk).
The Big Five Tech Companies: Google, Apple, Microsoft, Meta (former Facebook), Amazon.
Main features of big tech and challenges:
- Economic: Big Tech companies are a selected group of providers with big market
shares (max market capitalization). Big tech is active on several technology markets
with an monopolistic or oligopolistic nature. Big tech has different specific
characteristics:
Network effects: Increased numbers of people or participants improve the value of a good or
service.
Vertical integration: Through mergers and acquisitions allows Big Tech companies to grow
even further. It is the strategy that companies use to control their own suppliers,
distributors, or retail stores to control their value or supply chain. Data flows can
be connected in the back end across all three levels of the ecosystem (e.g. google
chrome laptops have built in software, google cloud, google search and maps, but
also google education).
I. A classic supply chain moves from upstream to downstream, where the
raw material is transformed into products, moved through logistics and
distribution to final customers. A data supply chain moves in the opposite
direction. The raw data is “sourced” from the customer/user. As it moves
downstream, it gets processed and refined by proprietary algorithms and
stored in data centers.
II. in the digital world, vertical integration happens when a company can control the
primary access points to acquire data from consumers 🡪 also look at gatekeeper
power
, Economy of scale (schaalvoordeel ontstaan door vergroten productiecapaciteit): Many
technology products boast a high economy of scale, particularly software. This is due to
having more fixed costs than marginal costs. As a result, the cost of creating more of your
software program is minimal-to-nothing. Once you’ve paid the fixed costs of creating the
software to begin with, mass producing it is as simple as copy and paste. You simply reuse
the code. The economy of scale means successful tech companies can see their profits
snowball. So, this side of tech economics is a major contributing factor to the tech market
monopoly phenomenon.
Economy of scope (synergievoordelen ontstaan door één productiefaciliteit voor twee
producten te gebruiken): refers to the reduced expense of creating a range of distinct products
using shared resources. The idea is to create a complementary set of products while sharing
fixed costs. For software, companies might produce a range of programs and feature sets.
(Using the same development office and tools.) For hardware, a computer manufacturer
might leverage their resources to also make tablets and laptops. Technology quite often has a
good economy of scope due to low marginal costs. As a result, you capitalize on getting the
most you can out of your fixed costs. (Which are then offset by the good economy of scale.)
Challenges: No meaningful rivalry due to the monopolistic/oligopolistic structure of the
companies and abuse of dominance.
- Informational: Big Tech companies have brought social change via tech because
people are now socializing online. They have become central actors in our
information societies and they control our access to news/content/information/facts.
Challenges: Disinformation, fake news, deep fakes, filter bubbles, hate speech. Influence on
democracy because they control our access to information and they facilitate the spread of
disinformation (filter bubbles, algorithmes that only show you what you want to see) or hate
speech.
- Infrastructural: Hardware (the hosting servers), login to other services online, data
and algorithms. Besides, the physical infrastructures (e.g. transportation), multi-level
markets. (Personal) data processing and data collection is a key ingredient of their
business models (privacy breaches and scandals)
- Societal and democratic: Big Tech companies have a public role/function,
connecting people, entertainment, and resource sharing: In computing, a shared
resource, or network share, is a computer resource made available from one host to
other hosts on a computer network.
Challenges: Addictive design, impact on health/public health, influencing elections (so
influence on our democracies). Big tech can pose a danger to some of our fundamental rights,
for example our right to privacy (by collecting so much of your personal data) or our freedom
of speech (for example moderating online content in a way that can intrude this (censorship).
