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ANTITRUST ASPECTS OF BIG

DATA AND ALGORITHMS 22 January 2018

Presentation: 1 hour

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Kevin Wright Partner, Chair, Canadian

International Trade,

Regulatory and

Government Affairs Group

T: +1 604 643 6461

kevin.wright@dlapiper.com

Lesli C. Esposito Partner, Co-Chair, US Antitrust

and Trade Regulation Group

T: +1 215 656 2432

lesli.esposito@dlapiper.com

Catherine Pawluch Partner

T: +1 416 369 5272

catherine.pawluch@dlapiper.com

Alexandra Kamerling Partner

T: +44 (0)20 7796 6490

alexandra.kamerling@dlapiper.com

Nathan Bush Partner, Head of

Investigations, Asia

Head of Antitrust and

Competition, Asia

T: +65 6512 6065

nathan.bush@dlapiper.com

MODERATOR

United States

Canada

European Union

Asia

PANELISTS

Canada

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Data has been collected, analyzed and used for many years in different

industries - What is the big deal with “Big” Data?

4 “Vs” - Volume, Velocity, Variety, Veracity

Innovations in how data is generated and collected

Innovations in how data is processed and the use of algorithms

Introduction: Features of Big Data

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Market Power and Barriers to Entry

– Network effects

Access to Data (when is refusal to share data anticompetitive?)

Transparency (when is sharing data anticompetitive?)

Collusion through algorithms?

Introduction: Competition/Antitrust Issues Raised by Big Data and Algorithms

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Competition and antitrust authorities study big data

– French/German authorities joint study (May 2016)

– OECD: Bringing Competition Policy to the Digital Era (Nov. 2016)

– DOJ/FTC Paper on Algorithms in Price Setting (May 2017)

– Italian authorities open joint Sector Inquiry on Big Data (May 2017)

– OECD: Note from the EU on Algorithms and Collusion (June 2017)

– Canada (Competition Bureau) Draft White Paper (Sept. 2017)

– UK CMA final report on Digital Comparison Tools (Sept. 2017)

– Merger and Conduct investigations of Google, Facebook, etc.

Introduction: Studies and Investigations

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Do competition/antitrust authorities need to change how they approach the

assessment of competitive harm in view of Big Data?

Should we refine substantive laws?

Should we refine investigative and adjudicative processes?

Balance between intervention and interfering with innovation

Introduction: Issues for Policy and Enforcement

United States Lesli C. Esposito Partner, Co-Chair, US Antitrust

and Trade Regulation Group

T: +1 215 656 2432

lesli.esposito@dlapiper.com

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Sharing of data must be “reasonable,” meaning not likely to harm competition

Ask whether the purpose or likely effect of the exchange is to promote or to

hinder competition

Content matters

– Avoid sharing data related to price, cost, output, customers, or current or

future operating and business plans

– Data that is highly technical is not as likely to raise competitive concerns

(e.g. technical cyber threat information)

When possible, stay within the “safety zone”

– The data exchange is managed by a third-party, like a trade association;

– The data is more than three months old

– Share anonymous aggregated statistics such that no participant can discern

the data of any one participant

United States: Data Sharing

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U.S. authorities have so far taken a more hesitant approach to the role of big

data in Section 2 enforcement

– Department of Justice, Deputy Assistant Attorney General Barry Nigro

– Federal Trade Commission, Nominated Chairman Joseph Simmons

When considering whether big data creates market power, track record of the

agencies suggests that they will look at:

– The uniqueness of the data

– The uniqueness of the ability to analyze the data

Big data in the merger control arena suggests that it may emerge as a Section

2 issue in the future

United States: Abuse of Dominance

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The effects of big data are becoming part of U.S. merger control analysis

Regulators treating big data like any other asset

FTC recently challenged a merger between two companies in the market for

educational marketing data

– FTC contended that the companies’ data was unique in the marketplace,

and challenged the merger on a unilateral effects theory, meaning that the

transaction would result in unilaterally increased prices or decreased output

DOJ challenged a merger between providers of financial data

– DOJ required the companies to divest copies of three financial datasets in

order to proceed with the acquisition

Regulators are determining the value of the data by analyzing its uniqueness

United States: Merger Control

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Consumer protection issues may be intertwined with antitrust in area of big

data

U.S. enforcers generally try to maintain a distinction between antitrust and

consumer protection enforcement to a degree that some international

enforcers may not

– Recently, privacy advocates urged FTC to block a merger of two technology

companies on the premise that the combination of the companies’

respective personal datasets could be exploited in a way that threatened

consumers’ privacy

– FTC responded by emphasizing that the sole objective in merger review is

to identify and challenge transactions that harm competition

– FTC did consider whether privacy protection was a dimension of non-price

competition between the companies

United States: Other Legal Issues Implicated By Big Data

Canada Catherine Pawluch Partner

T: +1 416 369 5272

catherine.pawluch@dlapiper.com

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The Canadian Competition Bureau released Big data and Innovation: Implications for Competition

Policy in Canada for public consultation (“White Paper”) and invited views (by November 17, 2017).

Two important contextual points made by the Bureau:

1. “The purpose of competition law in Canada is not to regulate prices, profits, market shares or the

amount of data that companies gather and use.”

Competition policy does not assume that “big is bad”

Focus on ensuring that market forces (as opposed to market outcomes) are working

i.e. that companies compete on the merits, customers are able to make well-informed decisions

and regulations are minimally intrusive

2. Bureau is mindful of risks of “over-enforcement” i.e., taking action where there may be no

genuine harm to competition, which risks slowing or even stopping advances

Firms are increasingly harnessing big data in ways that drive innovation and quality improvements

White Paper posits that Bureau’s current analytical framework can be applied to big data cases

Canada: Context

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Will antitrust laws be used to force the sharing of data?

TREB (2017) decision - potentially significant in driving the opening up of data sources

Abuse of dominance case - involved restrictions imposed by the Toronto Real Estate Board (TREB), a

trade association, on its members relating to access, display and use of multiple listing data

controlled by TREB

Commissioner of Competition alleged that

– restrictions had the effect of SLC in the supply of residential real estate brokerage services in the

Greater Toronto Area

– restrictions prevented the emergence of innovative, Internet-based business models and service

offerings, such as virtual office websites

Federal Court of Appeal confirmed Competition Tribunal’s decision - TREB, by restricting access to

data was engaged in an anticompetitive practice and abusing its dominant position

Canada: Data Sharing

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What are antitrust risks in sharing data?

Increasing availability of digitised, real-time pricing data has increased transparency.

Advanced technology companies can quickly, efficiently, and effectively use the pricing data of their

competitors to make more strategic decisions about their own pricing.

“Conscious parallelism” includes situations where competitors unilaterally adopt similar or identical

business practices or pricing.

Conscious parallelism is not criminal under the Competition Act.

Risk: where parallel behaviour is accompanied by “facilitating practices” i.e. activities that have the

potential to facilitate, or may be indicators of the existence of an agreement between competitors.

Cartel regime - Section 45(3) of the Competition Act allows a court to infer the existence of a

conspiracy, agreement or arrangement from circumstantial evidence, with or without direct evidence

of communication between or among the alleged parties to it.

Canada: Data Sharing

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Examples of the kinds of digital tools and platforms that may be areas of concern in antitrust:

sharing pricing algorithms with competitors. (This could be seen as akin to distributing a price list to

competitors.)

disseminating pricing information using a digital platform - may lead to higher prices if competitors are

able to propose higher prices and gauge a likely competitive response without the risk of losing

business

outsourcing the creation of algorithms to the same IT companies and programmers, creating a “hub

and spoke” scenario where coordination is caused by competitors using the same “hub” for

developing their pricing algorithms and end up relying on the same algorithms to develop their pricing

strategies

using signalling algorithms and unilateral price announcements - firms may program snapshot price

changes during the middle of the night, which won’t have any impact on sales but may be identified

as a signal by rivals’ algorithms

using algorithms to publicly disclose a lot of detailed data that is used as a code to propose and

negotiate price increases (ATPCO 1994)

Self-learning algorithms - use of machine learning and deep learning technologies may potentially

enable a monopoly outcome even without competitors explicitly programming algorithms to do

so; there is a risk that algorithms with powerful predictive capacity, by constantly learning and

readapting to the actions of other market players ( whether human or artificial agents) will be able to

collude without the need for any human intervention.

Question: whether liability could ever be imposed on firms that use deep learning algorithms and

unknowingly reach a collusive outcome?

See: OECD Algorithms and Collusion: Competition Policy in the Digital Age (2017)

Canada: Data Sharing (cont’d)

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What is the competitive significance of data in the context of merger review?

Coordinated effects - consider the impact of big data on the ability of firms to coordinate in markets and

whether this exacerbates the impact of a merger or anticompetitive practice.

McKesson / Katz Group’s health care business (Rexall) merger (2016) - Bureau concluded

that the combination of the data collected by McKesson’s wholesale business and the retail data

collected by the Katz Group was likely to result in SLC at the retail level as it would enable the

merged entity to better anticipate its competitors’ behaviour and coordinate its response.

McKesson entered into a Consent Agreement - agreed to establish a series of firewalls

restricting the transmission of data between the various parts of the merged entity’s business.

Prevention of competition - Pre-emptive acquisitions of disruptive firms that have amassed valuable

data, but have not yet generated significant sales raise “substantial prevention of competition”

concerns.

An assessment of such mergers, typically non-notifiable, is challenging due to the prospective

nature of the competitive effects, particularly in fast-changing industries.

Competition Act allows the Commissioner of Competition to review and challenge non-notifiable

mergers up to one year post-closing. Bureau views this as a “safety valve” to deal with big data

cases not caught by notification thresholds for pre-merger review but that have potentially

significant anticompetitive effects in Canada in future.

Canada: Merger Control

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Vertical competition issues - Occurs where data is used as an input and the merger is likely to result

in a substantial lessening or prevention of competition (SLC) in Canada

Google Investigation (2013 - 2016) - Complaints were made against Google for entering into

syndication agreements, specifically, that Google excluded search rivals by denying them data

that would have otherwise been made on their search engines and therefore, denying them the

“search scale” to compete with Google.

Bureau concluded that Google’s agreements had not resulted in an SLC in Canada as multiple

companies competed for syndication agreements as evidenced by bidding activity.

Canada: Merger Control (cont’d)

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Market power is a key element in abuse of dominance cases. Big data cases raise challenges in the

analysis of indicia of market power.

1. Barriers to entry and expansion

Access to and control over critical data that serves as an essential input may confer market power:

TREB (2017)

Switching costs related to data (to transfer from one platform to a competing platform) and restrictive

contracts may entrench dominance

Data and network effects create barriers to entry/ expansion

Exclusive access to proprietary data can make entry/expansion by rival difficult

Softvoyage (2018)

Example where dominant firm controlled where data was actually available (distribution channels) and

how the data was presented to consumers

Involved abuse of dominance claims in two markets related to the supply of all-inclusive vacations:

content management software used by tour operators to create vacation packages and manage

inventories or content, and

distribution software enabling the online sale of vacation packages to consumers

Canada: Abuse of Dominance

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Consent Agreement - as part of the settlement, Softvoyage agreed to:

collaborate in good faith with tour operators to develop External APIs integrated with

Softvoyage’s content management software to provide shopping and booking functions

that can be used by third parties

distribute tour operator products that are managed using content management software of

Softvoyage’s competitors

eliminate restrictive clauses that could prevent or dissuade Softvoyage’s clients (tour operators

and travels agencies) from using competing distribution software

2. Pricing studies

Pricing may not be an indicator of market power in big data cases

Platform may charge high prices on one side to subsidize investments/ zero on the other side to attract

users and their data

3. Market shares

Rapid change and innovation characterize big data cases - a firm’s current position (small share but

amassed valuable data) may not reflect future competitive significance

Canada: Abuse of Dominance (cont’d)

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Big data and deceptive marketing practices

Misleading advertising is both a criminal offence and a civil reviewable practice under the

Competition Act.

Risk of materially deceptive business practices

Essential to provide sufficient information for consumers to make informed choices.

Big data facilitates targeted advertising; geolocation data enables location-specific ads - this

requires balancing against consumer’s right to truth in advertising.

False or misleading representations about collection of data - i.e. how the data will be used,

maintained, shared, and ultimately, erased - may lead consumers to provide information that

would not otherwise have provided

Practices that raise deceptive marketing issues

– “Online lead generation” - used by firms to identify customers that are likely interested in

buying a product / service they offer - involves misleading representations

– Astroturfing - using fake consumer reviews/ testimonials to promote a product

– native advertising - disguising an ad by making it similar to the news, product reviews, etc.

that consumer view online

Canada: Other legal issues implicated by Big Data

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The intersection of big data and IP

A mere exercise of intellectual property rights is not an anticompetitive act: Competition Act,

section 79(5).

Ownership of data may confer market power and its exclusion may have competitive effects in a

market.

Issues as to whether the compilation of data constitutes IP rights, and if so, whether the data

owner’s exclusionary conduct is a mere exercise of IP rights.

The intersection of privacy and competition

A potential remedy available to the Competition Tribunal is a requirement that data be made

available to a firm’s competitors for any use, e.g. compulsory licensing of intellectual property.

Raises serious privacy concerns because privacy legal framework in Canada requires that the

purposes for which an individual’s personal information is to be collected, used or disclosed be

explained in a clear and transparent manner and consent requirement.

Who has jurisdiction over big data?

Competition Bureau

Intellectual Property Office

Privacy Commissioner

Canadian Radio-television and Telecommunications Commission

Some combination of those governmental bodies?

Canada: Other legal issues implicated by Big Data

European Union Alexandra Kamerling Partner

T: +44 (0)20 7796 6490

alexandra.kamerling@dlapiper.com

www.dlapiper.com 23

The collection and sharing of data:

– can be pro-competitive and beneficial for consumers (benchmarking or improved/

more targeted services & products)

– should not allow for coordination of competitive behavior -historical and aggregated

data and

– should not foreclose prevent access /significantly disadvantage third parties

(horizontal guidelines)

Some instances the use of a platform to collect and share data is simply a hub & spoke

illegal information exchange (e.g. Lithuanian online travel agents case)

Other instances an agreed price algorithm is just another way to fix prices (CMA and

DoJ online sales of licensed sports posters)

The biggest challenge is going to be finding ways to prevent tacit collusion between self-

learning algorithms, where certain inform sharing is an object based infringement and

e.g. UK has eliminated its dishonesty test for cartel behaviour.

– CMA investigation into use of data in the insurance sector concluded this was

beneficial for consumers and decided not to launch an in-depth market study.

European Union: Information sharing

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Joint French and German Paper examined how the possession of Big Data might be an important

source of market power, particularly when the data can be used as a barrier to entry

A feature of the digital economy is that often firms compete for the market rather than in the market,

leading to "winner takes it all" outcomes (cf Facebooks ability to displace Myspace as most popular

social network).

German report proposes additional criteria to evaluate whether in the context of platforms and

networks a market is contestable.

Data driven exclusionary and predatory behavior may limit competitors timely access to key data,

prevent others sharing, inhibit data portability or exclude rivals that threaten the data related

competitive advantage of the incumbent.

EU law prohibits discriminatory behavior - here access to data with the intention of providing one

company an unduly competitive advantage over other competitors

e.g. French case of Cegedim fined for refusing to sell data from its medical database to customers

who used a competitor's software or

– Interim measures imposed on GDF- Suez - ordering the gas supplier to grant its competitors

access to customer data, subject to consent by the individual consumers.

Under debate is the extent to which data may be considered an essential input - although for the EU

essential facilities doctrine to be applicable must also prove the data cannot be reasonably duplicated

by competitors

European Union: Abuse of Dominance

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Facebook/WhatApp not subject to EUMR due the target's lack of significant turnover even

though the consideration was US $ 19bn, but national filings and elected the "one stop shop"

of the EUMR.

Concerns resulted in an EU consultation and various Member States revising their merger

control thresholds to capture the potential value of a transaction, where one or both parties

had little revenues (e.g. Germany and Austria)

Google/Double click (2008) and Facebook/WhatApp (2014). If consider solely potential

effect on price - mergers will be cleared, rather need to consider the risk of monopolization of

data or the privacy costs on consumers (i.e. considered as one of the non-price competitive

considerations for consumers, alongside quality and innovation etc.)

In Microsoft /Skype (2011) EU noted that since "consumers communication services are

mainly provided for free, consumers pay more attention to other features", and "quality is

therefore a significant parameter of competition".

Microsoft /LinkedIn (12/2016)- EU required commitments for 5 years to protect competition

between professional networks ensuring that PC manufacturers and distributors will be able

to choose whether to install LinkedIn on Windows, and competing networks will continue to

benefit from the current levels of interoperability with Microsoft products.

European Union: EU Merger Control

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EU Data Protection Directive 95/46/EC ensures that personal data may only be collected for specific,

explicit and legitimate repurposes and not further processed in a way incompatible with these purposes.

Unambiguous consent should be given to the processing of personal data.

EU independent supervisory authority (the EU Data Protection Supervisor) has published papers

focussing on the role of competition in data protection issues - advocating a more holistic approach to

enforcement.

– E.g. German FCO investigation as to whether Facebook's potential breach of data privacy

protection also amounts to an abuse of a dominance position

EU reform on privacy embodies in the General Data Protection Regulation which allows consumers

the right to download their personal data from any online service and to have that data transferred to

any third party without hindrance (Art. 20).

– while this ensures data portability, it has been criticised for increasing SME's costs which may

affect their ability to compete.

Increased recognition of the value of government data and the need to ensure competitive neutrality

while protecting national and consumer interests - while not disadvantaging or distorting competition.

– E.g. UK CMA Report on the Commercial Use of Consumer Data (2015) and recommendations on

the use of public sector information to foster competition in the supply of value added products

and services.

European Union: Other legal issues

Asia

Nathan Bush Partner, Head of

Investigations, Asia

Head of Antitrust and

Competition, Asia

T: +65 6512 6065

nathan.bush@dlapiper.com

www.dlapiper.com 28

Asia: Diverse Markets & Evolving Antitrust Laws

Pop. (Mil)

GDP

Per Cap.

(US$)

Annual

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(%)

GINI

Index

Internet

Users (Mil)

Internet

Penetration Competition

Law

Effective

20th

Centu

ry Japan 126 $ 41,200 0.7% 37.9 117.8 93% 1947

South Korea 51 $ 37,700 3% 30.2 45.5 89% 1981

Taiwan 23 $ 48,100 1.9% 33.6 20.6 90% 1992

Gia

nts

/

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ennia

ls China 1,379 $ 15,400 6.5% 46.5 731 53% 2008

India 1,281 $ 6,700 7.4% 35.2 462 36% 2003

Indonesia 260,5 $ 3,578 5.3% 36.8 132.7 51% 1999

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all

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ies

Hong Kong 7 $ 58,400 2.7% 53.7 6.3 90% 2015

Singapore 6 $ 87,800 2.6% 45.8 4.7 78% 2004

Rest of A

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Vietnam 96 $ 2,111 6.3% 37.6 50 52% {2005}

Philippines 104 $ 2,997 6.7% 46 60 58% 2017*

Thailand 68 $ 5,744 3.5% 48.4 46 68% {2018}

Malaysia 31 $ 9,765 4.8% 46.2 22 71% 2012

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New research reports signal regulators' recognition of complexities

– Japan Fair Trade Commission (JFTC): Report of the Study Group on Data

and Competition Policy (June 2017)

– Competition Commission of Singapore (CCS): Handbook on Competition

and E-Commerce in ASEAN (August 2017)

– Incorporates input from competition authorities of Brunei, Cambodia,

Indonesia, Laos, Malaysia, Myanmar, Philippines, Thailand, and Vietnam.

– Indonesia's Komisi Pengawas Persaingan Usaha (KPPU): The Digital

Economy in Indonesia (December 2017)

Public comments from leaders of Korea Fair Trade Commission (KFTC),

Taiwan Fair Trade Commission (TFTC), and China's National Development

and Reform Commission (NDRC) highlight concerns about bid data and

algorithms and reluctance to deem current competition tools insufficient to

address new challenges.

Asia: Competition Authorities Prepare to Respond

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JFTC Report: If such digital cartels serve to substantially restrain competition, they will

probably need to be strictly dealt with in the same way as other traditional cartels.

Therefore, it would be desirable to keep a close eye on the situation, and if necessary, to

discuss on issues with “liaison of intention” in the interpretation of the “unreasonable

restraint of trade.”

CCS Handbook: "Greater price transparency, and the development of advanced price

setting algorithms have made establishing and enforcing price coordination easier for

firms in some markets. Evidence from cases in the US and the UK have shown that

existing competition policy and law are largely sufficient to deal with the challenges . .

.As such technologies become more sophisticated, they may self-learn that coordination

among competitors is optimal. Were such developments to occur, legal clarity would be

required, for instance explaining where the liability falls."

KPPU Report: "The growing use of algorithmic pricing for digital services, such as taxi-

hailing apps, erodes consumer surplus – the difference between higher prices

consumers are willing to pay and lower prices they are required to pay. The analytical

challenge posed by competition is that, like collusion, it produces similar prices for

similar products and services in the marketplace. Digitalised OTT services and the use

of algorithms make these distinctions more difficult for regulators to judge."

Asia: Competition Authorities' Views on Algorithms

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Substantial momentum for convergence with Europe and North America

– Influence of foreign principles, policies, procedures and precedents

– Collaboration with peer regulators on policy and enforcement

But pressures for divergent outcomes are growing

– Local context: Institutional goals & antitrust as political economy

– Diagnosing divergence: innovation, error, or politicized outcome?

Big Data issues implicate cybersecurity, privacy, consumer protection

industrial policy, domestic security and national defence issues.

Distinctive domestic market structures and commercial conditions may

drive divergence– or yield different outcomes from similar approaches.

– Example: Antitrust enforcement behind China's "Great Firewall"

Flexibility of new statutes unconstrained by precedent

Asia: Convergence or Divergence?

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Questions? Please contact us to learn more about Antitrust Aspects of Big Data and Algorithms or visit www.dlapiper.com.

Kevin Wright Partner, Chair, Canadian

International Trade,

Regulatory and

Government Affairs Group

T: +1 604 643 6461

kevin.wright@dlapiper.com

Lesli C. Esposito Partner, Co-Chair, US

Antitrust and Trade

Regulation Group

T: +1 215 656 2432

lesli.esposito@dlapiper.com

Catherine Pawluch Partner

T: +1 416 369 5272

catherine.pawluch@dlapiper.

com

Alexandra Kamerling Partner

T: +44 (0)20 7796 6490

alexandra.kamerling@dlapip

er.com

Nathan Bush Partner, Head of

Investigations, Asia

Head of Antitrust and

Competition, Asia

T: +65 6512 6065

nathan.bush@dlapiper.

com

MODERATOR

United States

Canada

European Union

Asia

PANELISTS

Canada

www.dlapiper.com 33