How does Japan’s technological landscape influence its legal frameworks, and how do those frameworks differ from or align with Western counterparts?

In this episode, host Mauricio Figueroa is joined by two leading legal experts from Japan, Kaori Ishii and Hajime Idei, to explore this complex and often overlooked jurisdiction in the global conversation on tech law. Tune in for an interesting conversation on the intersection of culture, technology, and law in one of the world’s most innovative nations. Listen to the episode here https://bit.ly/3XHA9kf

The Panel:

Mauricio Figueroa is a Mexican legal scholar based in the United Kingdom. His area of expertise is Law and Digital Technologies, and has international experience in legal research, teaching, and public policy. He is the host of the SCL podcast “Privacy and Technology Laws Around the World”.

Kaiori Ishii is a Professor in Chuo University, Faculty of Global Informatics from April 2019.
Before joining Chuo University, her professional experience included roles as a lawyer, in-house counsel, and academic positions at the Institute of Information Security and the University of Tsukuba.
Her research focuses on legal issues related to data protection and privacy across different countries. Her work includes comparative analyses of data protection legislation, exploring insights into the right to privacy, and examining how to harmonize current technological advancements with privacy protections. Recently, she has been interested in the intersection of privacy and data protection with competition law and consumer protection law.
She has been involved in various expert committees under the Cabinet Secretariat, ministries, and local governments in Japan, where she has provided input based on her expertise.

Haime Idei is Japanese lawyer, graduated from Waseda University School of Law. After working at Anderson Mori & Tomotsune, he joined Kotto Dori Law Office (https://www.kottolaw.com/en/). His specialty is entertainment law. He mainly advises the anime, game, AI, and VR industries.
From 2020 to 2023, he was involved in Japan’s Intellectual Property policy as an assistant counsellor at the Intellectual Property Strategy Headquarters of the Cabinet Office.
Since 2022, he has also served as an auditor of the Japanese Animation Society, researching development of Japanese anime culture.
His publications include “The Current State of the Debate on the Copyrightability of AI Creations and Future Legal Practice” (July 2024).

About the podcast

Join host Mauricio Figueroa and guests on a tour of tech law from across the globe. Previous episodes have focused on the use of ‘robot judges’ in several jurisdictions and developments in India and the USA. Future episodes will look at South America, Africa and Europe.

Where to listen

• podcasts.scl.org
• Spotify
• Apple
• Amazon

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Introducing Disruption
Imagine someone, sitting anxiously in a waiting room at a solicitor’s office they hastily found online. They clutch a notice of eviction in their hands, confused as to how their landlord can remove them from their home of ten years. Somewhere else, a woman waits on hold with a personal injury helpline after months of excruciating pain from slipping on an uneven paving stone, leaving her unable to work. Another man is on their commute home from work for the last time, frantically googling employment law, after being unexpectedly dismissed; he does not know what he can do or where next month’s rent will come from. Despite their differences, they all share one common question:

“Am I going to win?”

It is an age-old query coming in different forms; can I stay in my home; how much will I get in damages? Ultimately, they want to know about the outcome.

When considering how AI can increase access to justice, we must start there—with the outcome. Too often, solutions to the crisis of access to justice are framed within the constraints of existing systems, which rely on the current process being supported by technology (sustaining technologies). While these efforts are valuable, artificial intelligence (“AI”) offers an opportunity to work outside of these constraints and construct new routes to the same just outcomes (disruptive technologies).¹ This account will consider how access to justice has already benefitted from AI and what could be done next.

Organising Disruption: Efficiency
For AI-driven disruption to meaningfully increase access to justice, its implementation must be guided by clear principles with efficiency as the cornerstone. In this context, efficiency encompasses both expediency and accuracy in results. Timely justice has been a consistent guiding ethos, with the Magna Carta stating, “To no one will we sell, to no one deny or delay right or justice.”² This remains pertinent today, as explained by Zuckerman, the passage of time can diminish the value and enforceability of rights, making speed an essential element not just in procedure but in the dispensing of justice.³
Guided by efficiency and a willingness to move beyond traditional processes, AI can increase access to justice by providing faster, and still accurate, resolution that would otherwise take months of litigation.

Implementing Disruption
One clear example of AI systems enhancing access to justice is the rise of chatbots, such as AccessAva, which streamline legal information for those who need it most.⁴ AccessAva, developed by Carers UK in partnership with Access Social Care, is an online tool designed specifically for unpaid carers in the UK. It empowers users by providing easy-to-understand legal information, along with templates and resources which reduce the need for professional legal assistance. Other models include DoNotPay, which offers a similar service aimed at consumers. These chatbots are examples of Large Language Models (“LLM”), which are trained on large amounts of text data, which, in turn, generate natural language responses to a wide range of inputs.⁵ What sets models like DoNotPay and AccessAva apart is their focus on supporting litigants directly. They disrupt by allowing computers to speak the language of lawyers, which was previously unachievable. Further, they increase efficiency by centralising the information needed by the individual and making justice more accessible to those who might otherwise struggle with traditional systems due to financial constraints or a lack of understanding. This exemplifies AI as a solution to problems highlighted by the 2023 Legal Needs Survey by the Law Society. A key finding was that, of people who faced a legal issue between 2019 and 2023, only 52% received professional help, with the rest either relying on family and friends or not seeking assistance at all.⁶ The survey highlights two key barriers: the cost of legal advice and a lack of understanding or confidence in engaging with the law. AI-driven platforms like AccessAva are precisely the kind of innovation that can overcome these obstacles, closing the gap and providing essential support to those who would otherwise struggle to access justice.

They can also be taken further. AI systems, particularly those using machine learning, can analyse patterns in large datasets to predict outcomes which has the potential to take chatbots beyond the provision of legal information and into the realm of advice.⁷ For instance, researchers have used AI to predict the outcomes of European Court of Human Rights cases with 79% accuracy.⁸ A predictive capability like this has the potential to disrupt as it would allow an individual not only to understand what the next steps are but to make a well-informed decision on whether to pursue at all. Users could ask whether they have a strong case, whether pursuing it is cost-effective, or what outcomes they might expect. Accessing legal advice is a key element of access to justice and an AI system which combines predictive outputs with user-friendly interfaces, like AccessAva and DoNotPay, has the potential to increase the number of people that such advice is available to.

Challenges
There are concerns which should be met head-on, the primary one being accuracy. If the architecture which underlies any predictive technology is wrong, the output will be too. Thus, any such model would have to be tightly regulated by humans (lawyers) with the knowledge of the underlying area and the ability to understand the dispute to ensure the algorithm does not result in litigants abandoning worthy claims. Legal minds will have a role at the point of data entry and in auditing the output. The changing role of the lawyer and the need for the legal sector to be reflexive with technological advancements is part of the disruption that access to justice solutions which use AI will bring around. The lawyer’s auditing role is also in identifying “hallucinations” by chatbots whereby responses generated are incorrect or fabricated. The risk is lower with bespoke systems using specialised legal data than with general-purpose chatbots like ChatGPT.⁹ Despite the reduced risk, verification by a qualified legal mind is still necessary to ensure accuracy, and therefore efficiency. With this safeguard, chatbots can help democratise legal assistance.
Another significant concern regarding the use of AI in legal practice is that it could stunt the growth of the common law. A classic iteration of this concern comes from considering Donoghue v Stevenson,¹⁰ a seemingly simple case where a woman drank from a bottle which, unbeknownst to her, had a decomposing snail inside. While the case involved a straightforward fact pattern, it went all the way to the House of Lords and ultimately established the “neighbour principle,” a key development in negligence law. If fed into an AI advice system before this principle was established, the outcome might have been different, possibly failing to recognise the broader legal implications of the case. This raises the concern that AI systems, by relying heavily on data from past decisions, might overlook the unique factors in a case that could lead to the establishment of new legal principles. If AI simply provides a binary answer—”good claim, pursue” or “no claim, tough luck” – it could ignore the nuanced, creative reasoning that legal professionals bring to the table. It must be acknowledged that whichever AI system is implemented, it has to be sophisticated enough to recognise and flag unique features of a case that may not align with past precedents. These features could prompt lawyers to consider how the case might develop or whether it warrants a new interpretation of the law.

Conclusion
Embracing AI has the potential to reduce delays and empower individuals with legal information and guidance. This disruption must be managed carefully with human oversight to address challenges in ensuring accuracy and preserving the flexibility of legal interpretation.
This account comes from a legal perspective, without the technical expertise to explore the underlying technology. That knowledge gap should not, however, remove lawyers from the conversation. They bring essential industry insights and knowledge that are key to the reflexive relationship between law and tech.
Ultimately, with such collaboration and safeguards, AI can bridge the justice gap, ensuring that more people can ask and answer the crucial question, “Am I going to win?”

Beth Gilmour is the winner of the SCL AI Group Junior Lawyer Article Competition. Beth is a BAR student and is currently a Judicial Assistant to High Court Judges in England and Wales.

1. Susskind RE, Tomorrow’s Lawyers an Introduction to Your Future, Chapter 6 “Disruptive Legal Technologies” (Third edition, Oxford University Press 2023), ↩︎
2. Magna Carta Clause 40 ↩︎
3. Professor Adrian Zuckerman, Zuckerman on Civil Procedure: Principles of Practice 4th Ed, Chapter 1 p.17 (4th Edition. Street & Maxwell, 2021) ↩︎
4. AccessAva, available at https://www.accesscharity.org.uk/accessava (accessed December 2024) ↩︎
5. Robin Allen KC and Dee Master, Judges, Lawyers, and litigation: Do they, should they, use AI? Paper for the Employment Law Bar Association (2024) p.17 ↩︎
6. The Law Society: Find out what your clients need, with the results of our Legal Needs Survey, available at https://www.lawsociety.org.uk/topics/research/find-out-what-your-clients-need-with-the-results-of-our-legal-needs-survey (accessed December 2024) ↩︎
7. Richard Susskind n1, Table 6.1 ↩︎
8. Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, Vasileios Lampos, Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective, (2016) PeerJ Computer Science 2:e93 ↩︎
9. Robin Allen KC and Dee Master n5, p.13 ↩︎
10. [1932] AC 562 ↩︎

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As the competition winner, Beth will receive £500 in training credits, and her article will be published on the SCL website and in the next issue of C&L Magazine.

The competition attracted a high calibre of entries, reflecting strong interest in the intersection of AI and law. In addition to the winning entry, four runners-up received a Highly Commended distinction for their outstanding submissions.

This was the first article competition organized by the SCL AI Group, open to trainees, junior lawyers, and students. Participants were invited to submit articles on one of the following topics:

• The most transformative changes in legal practice that will arise from the use of AI in the next 10 years
• The ability of AI to increase access to justice
• The liability of autonomous systems

The judging panel comprised members of the SCL AI Group Committee, alongside David Chaplin, Editor of Computers & Law. The final selection was made from a shortlist by SCL President, Professor Richard Susskind CBE KC (Hon).

Congratulations to Beth Gilmour and all participants for their exceptional contributions!

Read Beth’s article here: https://www.scl.org/the-ability-of-ai-to-increase-access-to-justice/

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The aim of the evaluation was to gather evidence on the functioning of the TTBER and of the accompanying Guidelines, for the Commission to decide if it should let the rules expire, prolong their duration or revise them. In view of its findings, the Commission will now launch an impact assessment to examine policy options for revising the rules.

The TTBER will expire on 30 April 2026, and of the accompanying Guidelines. The evaluation has shown the following:

• The TTBER and the Guidelines have been largely successful in ensuring the effective, efficient and uniform application of EU competition rules to technology transfer agreements. They have assisted companies in self-assessing the compliance of their technology transfer agreements with EU competition rules.
• The objectives of the TTBER and the Guidelines remain relevant to block-exempt only pro-competitive technology transfer agreements and to provide legal certainty for companies wishing to enter into such agreements.

The evaluation also shows that the TTBER and the Guidelines could be improved in certain areas to increase legal certainty and reflect recent market developments. These include the following:

• Some stakeholders identified practical difficulties in applying one of the two market share thresholds contained in the TTBER, namely the threshold for technology markets. Technology markets consist of the licensed technology right(s) and other technologies that are regarded as interchangeable by licensees.
• Stakeholders also suggested broadening the scope of the TTBER to cover the licensing of data or data rights, which have a growing importance in the digital economy, and/or providing guidance on this issue in the Guidelines.
• The safe harbour provided in the Guidelines for technology pools has generally worked well. It sets out the conditions that, if met, usually ensure that the pool does not breach EU competition rules. However, some stakeholders consider that these conditions do not always guarantee that only compliant pools benefit from the safe harbour.
• Some stakeholders consider that the Commission should provide guidance on the competition law assessment of licensing negotiation groups, namely groups of technology implementers who negotiate technology licences together.

The Commission will now launch the impact assessment phase of the review to investigate the issues identified during the evaluation with a view to having revised rules in place by April 2026, when the current rules expire.  The Commission intends to consult next month.

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A competition to hear ideas from students and prospective trainees on how the law can embrace sustainability principles.

The challenge we set: Which legal, environmental, social, or governance issue can be resolved with improved technology and how?

The Dragons
Our Dragons are experts in the field of technology, ESG, and the law. 

Neil Brown, Managing Director, decoded:legal
Lisa McClory, Digital Technologies Lead, D2 Legal Technology
Alessandro Galtieri, Deputy General Counsel, Colt Technology Service Group Ltd

We had three fantastic finalists for the competition who each submitted a video pitch with their suggested solution to the challenge. You can see their pitches here.

The judges were incredibly impressed with all submissions to the Green Dragon’s Den competition and greatly enjoyed hearing from all applicants. It has been fantastic to see such a broad range of ideas across different fields of ESG, and this has not made the judges’ task easy!

We would like to extend our thanks to all the entrants for putting together such interesting and thought-provoking presentations.

The chosen winner and detailed feedback to all finalists is below….

The Dragons are pleased to announce that the winner of this year’s Green Dragon’s Den competition is Vathmie Vanditha Widyalankara (Vandy) of Queen Mary University of London, who provided an engaging presentation on the opportunities to use AI-powered pro bono services to catalyse positive change in legal advice services.

Vandy provided a well-considered introduction to the current challenges in law centres and legal advice clinics, also setting out how AI might provide a solution, including by streamlining client triaging, providing AI-powered formfilling, using AI chatbots and AI legal assistants. Vandy dealt with some of the challenges and risks of leveraging AI technology, including privacy concerns around use of sensitive data, algorithmic bias and the crucial need for oversight and effective governance to combat potential fabrication by AI tools and protect consumers from risks.

Vandy demonstrated a good grasp of complex technologies, including differential privacy, and the judges were impressed with the way in which she was able to explain them.

Our two joint second-place finalists (in alphabetical order) are:

Diana Al Mejamai of the University of Newcastle
The Dragons were impressed with Diana’s explanation of the opportunity for AI and algorithmic technology to provide a tracking and early-detection solution to detect methane leaks across the national gas network. We found Diana’s explanation of the problem to be well presented and informative, with good coverage of the links to wider legal frameworks and economic factors associated with net zero. It was interesting to hear about the potential to use predictive technology to anticipate potential leaks.

Jared Higgins of the University of Sussex
The Dragons found Jared’s talk to be an interesting and informative review of end-to-end encryption technology, covering the difficult human rights and policy challenges in this area. Jared presented a solution focused on the use of encryption technology by Politically Exposed Persons. Jared dealt well with the balance of positive and potentially negative effects of introducing this use case for E2E technology, including the potential impacts on law enforcement and anti-corruption efforts.  We were impressed with the clarity of Jared’s thinking in terms of differentiating between general purpose chat applications, which we understood to be outside the scope of his talk, and specific applications to be used for official business by politically exposed persons.

The winning entrant has won a work experience placement at Colt and each finalist has won a free place at the SCL Annual AI Conference on 8 October in London.

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In his new role, Prof Susskind will be responsible for supporting the 56 Commonwealth countries in achieving Sustainable Development Goal (SDG) 16: ensuring equal access to justice for all by 2030, using artificial intelligence (AI).

He will bring to the role decades of experience in leveraging artificial intelligence responsibly to transform the legal sector, from courts and justice delivery to public legal education and the work of lawyers.

Speaking about the appointment, Prof Richard Susskind said:

“I am immensely honoured to be appointed Special Envoy for Justice and AI and so to help the 56 Commonwealth countries to ensure equal access to justice for all by 2030.

This comes at an ideal time – remarkable recent developments in AI will transform the way that people can understand and enforce their legal rights.”

You can read the full announcement here: https://thecommonwealth.org/news/commonwealth-secretary-general-appoints-prof-richard-susskind-special-envoy-justice-and-ai

Please join us in wishing our President many congratulations and all good wishes for this prestigious appointment.

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During its latest plenary, the EDPB adopted an Opinion on the notion of main establishment and on the criteria for the application of the One-Stop-Shop mechanism, following a request under Article 64(2) GDPR by the French data protection authority. The Opinion clarifies the notion of a controller’s “main establishment” in the EU, especially where decisions regarding the processing are taken outside the EU.

In its Opinion, the EDPB considers that a controller’s “place of central administration” in the EU can only be considered as a main establishment under Article 4(16)(a) GDPR if it makes the decisions on the purposes and means of the processing of personal data and if it has the power to have such decisions implemented. The EDPB further explains that the One-Stop-Shop mechanism can only apply if there is evidence that one of the controller’s establishments in the EU takes decisions on the purposes and means for the relevant processing operations and has the power to have these decisions implemented. This means that, when the decisions on the purposes and means of the processing are taken outside the EU, there is no main establishment of the controller in the EU, and therefore the One-Stop-Shop should not apply.

This Opinion follows the EDPB’s Vienna Statement on cross-border enforcement, aiming to streamline enforcement and cooperation among data protection authorities.

In addition, the EDPB adopted a Statement on the legislative developments regarding the Proposal for a Regulation laying down rules to prevent and combat child sexual abuse. The Statement follows the EDPB-EDPS Joint Opinion on the European Commission’s Proposal for a Regulation and focuses on the latest legislative developments, in particular the position of the European Parliament of November 2023.

The EDPB welcomes the many improvements proposed by the Parliament, such as exempting end-to-end encrypted communications from detection orders. However, the EDPB says that the updated text does not fully resolve important issues flagged by the EDPB and the EDPS related to general and indiscriminate monitoring of private communications, especially regarding detection orders.

The EDPB stresses the importance of further limiting the risk that those orders could affect individuals who are unlikely to be involved in child sexual abuse-related crimes. Furthermore, the EDPB says that detection orders are not limited to child sexual abuse materials (CSAM) that are already known to authorities, despite the fact that the technologies used to detect new CSAM have proven in the past to have significant error rates. During the plenary, the EDPB also discussed the scope of the guidance related to the Consent or Pay model. In addition to the upcoming Article 64 (2) Opinion, which will address the Consent or Pay model in the context of large online platforms, it was agreed that there is a need to consecutively develop Guidelines with a broader scope.

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The UK government has published its response to its AI White Paper, which was published last year. It set out initial proposals to develop a “pro-innovation regulatory framework” for AI. The proposed framework outlined five cross-sectoral principles for the UK’s regulators to interpret and apply within their remits. The government also proposed a new central function to bring coherence to the regime and address regulatory gaps. 

The five principles were:

• Safety, security and robustness.
• Appropriate transparency and explainability.
• Fairness.
• Accountability and governance.
• Contestability and redress.

The government says there was strong support for these principles. It says that it remains “committed to a context-based approach that avoids unnecessary blanket rules that apply to all AI technologies, regardless of how they are used. This is the best way to ensure an agile approach that stands the test of time.” 

Since the publication of the White Paper, the CMA has published a review of foundation models to understand the opportunities and risks for competition and consumer protection and the ICO updated its guidance on how data protection laws apply to AI systems to include fairness.

The government has written to several regulators affected by AI to ask them to publish an update outlining their strategic approach to AI by 30 April. It is encouraging regulators to include:

• An outline of the steps they are taking in line with the expectations set out in the white paper.
• Analysis of AI-related risks in the sectors and activities they regulate and the actions they are taking to address these.
• An explanation of their current capability to address AI as compared with their assessment of requirements, and the actions they are taking to ensure they have the right structures and skills in place.
• A forward look of plans and activities over the coming 12 months.

The government also proposed an AI central function. It says it has started developing the central function to support effective risk monitoring, regulator coordination, and knowledge exchange. It has also published guidance to support regulators to implement the principles effectively.

The government highlights three broad categories of AI risk: societal harms; misuse risks; and autonomy risks.  

Societal harms

• Preparing UK workers for an AI enabled economy – there will be guidance on the use of AI in HR and recruitment. In addition, it will publish a skills framework later this year, as well as funding AI-related courses.
• Enabling AI innovation and protecting intellectual property – creative industries and media organisations have particular concerns regarding copyright protections in the era of generative AI. The Intellectual Property Office convened a working group made up of rights holders and AI developers on the interaction between copyright and AI. However, it is now clear that the working group will not be able to agree an effective voluntary code. The government intends to do further research and engagement in this area.
• Protecting UK citizens from AI-related bias and discrimination – regulators such as the ICO have updated guidance.
• Reforming data protection law to support innovation and privacy – the Data Protection and Digital Information Bill will expand the lawful bases on which solely automated decisions that have significant effects on individuals can take place.
• Ensuring AI driven digital markets are competitive – the CMA has carried out an initial study and the Digital Markets, Competition and Consumers Bill aims to give it the tools it needs to regulate digital markets.
• Ensuring AI best practice in the public sector.

Misuse risks

• Safeguarding democracy from electoral interference – among other things, the Online Safety Act 2023 will capture specific activity aimed at disrupting elections where it is a criminal offence in scope of the regulatory framework. 
• Preventing the misuse of AI technologies – the NCSC published guidelines for secure AI system development in November 2023. The Online Safety Act and the Product Security and Telecommunications Infrastructure Act also aim to provide regulation in this area.

Autonomy risks

• The government has examined the case for new responsibilities for developers of highly capable general-purpose AI system. It says that while voluntary measures are a useful tool to address risks today, it anticipates that all jurisdictions will, in time, want to place targeted mandatory interventions on the design, development, and deployment of such systems to ensure risks are adequately addressed.  
• It is also working with international partners on AI governance.

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The business for 7 February stated:

“Up to 90 minutes after the commencement of proceedings on the Business of the House (Today) motion (if that motion is agreed to)…(Secretary Michelle Donalan) That the period on the expiry of which proceedings on the Data Protection and Digital Information Bill shall lapse in pursuance of paragraph (13) of Standing Order No. 80A shall be extended by 280 days until 12 December 2024.”

The Bill is currently due to go to Committees stage in the Lords, but this development suggests that the government is running out of parliamentary time before the next election. It remains to be seen whether the Bill is hurriedly passed in a pre-election wash-up, or the issues are left for a new government to resolve.

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For nearly a decade, iRobot has deployed Amazon Web Services as the foundational layer underpinning its ‘smart home’ product and cited the cloud service as a key factor in enabling it to scale. Specifically, by reducing technical and financial barriers to expansion, investment in Amazon’s advanced infrastructure has allowed the firm to focus on the development of new product innovations, while significantly increasing the speed of product testing to deliver an improved customer experience. Indeed, this is a relationship that exemplifies how the tech giant’s decision to license-out its propriety IT infrastructure has increased the size of the economic pie: a firm such as iRobot simply could not have developed comparable internal capabilities within a realistic time frame. Despite instances in which that well-worn phrase “standing on the shoulder of giants” may act as a veil for inter alia inequality of bargaining power and market dominance, it may be entirely appropriate in this instance.

While iRobot has been honing its niche product offering, Amazon has been building-up strategic positions across the nascent ‘smart home’ sector and placed Alexa squarely at the heart of a broader variety of – predominantly third-party – devices. Given iRobot’s strong market position and brand value and Amazon’s unparalleled capacity to increase ongoing technical efficiency, scale and diffusion, it appears likely that customers would reap some additional benefits from the consecration of this corporate marriage. These benefits stand to extend far beyond the narrow robot vacuum cleaner market, given that Amazon currently offers a diverse portfolio of products from the well-known Echo and Halo Rise to a flying Ring camera and Astro robot. Whether the ‘smart home’ embodies welfare enhancing sci-fi come true or, as cynics prefer to portray it, a voyage into the dystopian is ultimately a question that consumers must collectively answer. Setting aside the lively discussion over how data protection and privacy law should be optimised to protect those consumers who actively invite myriad connected devices equipped with cameras, microphones and other sensors into their homes, two primary competition law questions arise.

The first question is whether Amazon would have the ability to foreclose rivals to iRobot. In its SO, the EC has stated that Amazon may have such ability since its online marketplace represents a “particularly important channel” for the sales of robot vacuum cleaners in France, Germany, Italy and Spain and that customers in those member states “particularly rely on Amazon both in terms of product discovery as well as for their final purchasing decision.” Foreclosure, it is claimed, could manifest in the delisting or reduction in availability of rival robot vacuum cleaners, with the latter possible in both organic and paid results. Moreover, Amazon could limit access to product recommendation “widgets” or “commercially-attractive product labels” such as “Works with Alexa”. Finally, the EC finds that Amazon may have the ability to directly or indirectly raise rivals’ cost to advertise and sell on its marketplace. Notably, this part of the story accords with the conclusion of the CMA over the summer, which found that Amazon has the ability to “use its position as a major retailer to disadvantage rival robot vacuum cleaner manufacturers”.

Of course, ability is nothing without incentive, and the second question is whether Amazon would find such foreclosure economically profitable. According to the CMA, the benefits of a foreclosure strategy to Amazon would be limited, taking into consideration both the small size and low expected growth of the UK robot vacuum cleaner market. The keynote is that any such strategy would necessarily involve significant losses for the acquiring firm, not least in the shape of lost sales commission and diminished advertising revenues. Moreover, the CMA found that the market for robot vacuum cleaners holds “limited strategic importance” and concluded that the acquisition would not disadvantage ‘smart home’ platforms that rival Amazon since robot vacuum cleaners – and the data gathered thereby – are “generally not considered to be an important input to the emerging ‘smart home’ market in the UK.” For sake of completeness, the CMA noted that rival ‘smart home’ offerings could benefit from integrating iRobot rivals with “similar capabilities”, of which several already exist. On this point, significant divergence between the authorities emerges: apparently, the story across the channel in continental Europe is rather different.

According to the EC’s SO, losses from fewer sales of rival and related products on Amazon’s marketplace would be outweighed by the potential gains from additional iRobot sales. Such gains would not be limited to the financial value of transactions and include data-driven benefits given the additional data gathered by Amazon from iRobot users. The tentative theory of harm articulated by the EU has strong echoes of Google Shopping and pivots about the fulcrum of ‘self-preferencing’. While the hotly anticipated judgment of the CJEU in Google Shopping is set to draw the final curtain on a saga whose origins  are nearly 15 years old, the case has led not only to the evolution of ‘self-preferencing’ into a stand-alone abuse under Article 102 TFEU but crucially spawned Article 6 (5) of Europe’s recently enacted Digital Markets Act (DMA). This ex ante obligation prohibits ‘gatekeeper’ firms from treating their own products and services more favourably in ranking than similar third-party products and services and mandates those ‘gatekeepers’ to apply transparency, fair and non-discriminatory conditions to such ranking. The SO also clearly echoes recent investigations of both the EC and the CMA into Amazon’s marketplace practices, which added to ‘self-preferencing’ the concern that Amazon had been harnessing non-public seller data derived from its marketplace to calibrate its own retail offering to the detriment of other marketplace sellers. The saliency of such concerns can be seen in the construction of Article 6 (2) of the DMA, which prohibits ‘gatekeepers’ from engaging in precisely this form of anticompetitive data-driven conduct.

Significantly, both the EC and the CMA’s investigations have concluded with the acceptance of similar sets of commitments tabled by Amazon. Indeed, in stark contrast to Google, it would appear that iRobot’s acquirer is prepared for compliance with the DMA and, presumably, any similar ex ante rules that emerge from the UK Digital Markets, Competition and Consumers Bill. Moreover, the DMA obliges ‘gatekeepers’ under Article 6 (10) to provide business users with free of charge, real-time access to and use of data, including end user data, that is provided for or generated through the use of its platform. Sculpted, seemingly, in response to concerns raised by Spotify in relation to Apple’s App Store practices, this ex ante obligation would be applicable to an apparently compliance-ready Amazon.

Against this backdrop, a final and crucial question arises: how can the EC credibly challenge Amazon’s acquisition of iRobot on the basis of concerns that Amazon may have the ability and incentive to engage in certain potentially harmful conduct that is already, to a not insignificant extent, prohibited ex ante under its flagship DMA? While in some quarters there is an increasing sense in which allowing Amazon to continue to attain strategic market positions through acquisitions such as iRobot is tantamount to the resignation of competition in digital markets to a ‘death by a thousand cuts’, a clear theory of harm may be some way off. The emergence of ecosystem theory, which seeks to sift the focus away from narrowly defined markets and towards the broader network of complementary products and services offered by a firm, could provide some answers. Ultimately, if the EC is to transform its SO into a meaningful obstacle to Amazon/iRobot, it will need to undertake substantial additional legwork and overcome its apparent ‘echo’ problem by clearly reasoning why competition law and new digital market regulation are insufficient to prohibit or deter any potentially anticompetitive conduct.

At the end of those long, tiring days of entertaining and feasting – and for many a little light family mediation – it seems entirely reasonable to allow the consumer to choose between picking up the dustpan and brush and uttering the command: ‘Alexa, vacuum those pesky Christmas tree needles!’ Now just imagine the potential post-merger benefits.

Ben Evans is a Postgraduate Researcher at the School of Law and Centre for Competition Policy, University of East Anglia.

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