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However, the protection granted by the Directive is not absolute. Article 1(2)(b), bolstered by Recital 11, concedes that secrecy will take a back seat if the ‘Union or national rules require trade secret holders to disclose, for reasons of public interest, information, including trade secrets, to the public or to administrative or judicial authorities for the performance of the duties of those authorities’. 

With regard to trade secrets in general, in the Microsoft case, the CJEU held that a refusal by Microsoft to share interoperability information with a competitor constituted a breach of Article 102 TFEU.

Although trade secrets remained protected from the public and competitors, Google had to disclose Page Rank parameters to the Commission as the administrative authority for the performance of its investigative duties. It is possible that a similar examination will take place in the recently launched probe in Amazon’s treatment of third-party sellers. 

For instance, in February 2020, the District Court of the Hague held that the System Risk Indication algorithm that the Dutch government used to detect fraud in areas such as benefits, allowances, and taxes, violated the right to privacy (Article 8 ECHR), inter alia, because it was not transparent enough, i.e. the government has neither publicized the risk model and indicators that make up the risk model, nor submitted them to the Court (para 6 (49)).

Article 22 still remains one of the most unenforceable provisions of the GDPR. Some scholars (see, e.g. Wachter) question the existence of such a right to explanation altogether claiming that if the right does not withstand the balancing against trade secrets, it is of little value.

In 2019, to ensure competition in the platform economy, the European Parliament and the Council adopted Platform-to-Business (P2B) Regulation. To create a level playing field between businesses, the Regulation for the first time mandates the platforms to disclose to the businesses the main parameters of the ranking systems they employ, i.e. ‘algorithmic sequencing, rating or review mechanisms, visual highlights, or other saliency tools’ while recognising the protection of algorithms by the Trade Secrets Directive (Article 1(5)).

The recent Guidelines on ranking transparency by the European Commission interpret the ‘main parameters’ to mean ‘what drove the design of the algorithm in the first place’ (para 41).

The German Interstate Media Law that entered into force in October 2020, transposes the revised Audio-Visual Services Directive, but also goes well beyond the Directive in tackling automated decision-making that leads to prioritization and recommendation of content.

This obligation to ‘explain the algorithm’ makes it the first national law that, in ensuring fairness for all journalistic and editorial offers, also aims more generally at diversity of opinion and information in the digital space – a distinct human rights dimension. If the provision proves enforceable, it might serve as an example for other Member States to emulate. 

Lastly, the draft DSA grants the newly introduced Digital Service Coordinators, the Commission, as well as vetted researchers (under conditions to be specified) the powers of data access to ensure compliance with the DSA. The core of this right, however, is undermined in Article 31(6), which effectively allows the platforms to refuse such access based on trade secrecy concerns. 

This shows that although addressing algorithms in a horizontal instrument is a move in the right direction, to make it enforceable, the final DSA, as well as any ensuing guidelines, should differentiate between three tiers of disclosure: 1) full disclosure – granting supervisory bodies the right of access, which may not be refused by the IP owners, to all confidential information; 2) limited disclosure – granting vetted researchers the right of access limited in time and scope, with legal guarantees for protection of trade secrecy; and 3) explanation of main parameters – granting individuals information in accessible language without prejudice to trade secrets. 

re are notable differences in scop

inauthentic behaviour, including the suppression of millions of fake accounts and the implementation of safeguards against malicious automated activities.

more granular information is needed to better assess malicious behaviour specifically targeting the EU and the progress achieved by the platforms to counter such behaviour.”

several tools have been developed to help consumers evaluate the reliability of information sources, and to open up access to platform data for researchers.

platforms have not demonstrated much progress in developing and implementing trustworthiness indicators in collaboration with the news ecosystem”, and “some consumer empowerment tools are still not available in most EU Member States.”

The implications of cloud platforms such as Salesforce, Microsoft, Apple, Amazon and others differ again. A business built on a technology platform with a track record of well-developed data and model governance, audit capability, responsible product development practices and a culture and track record of transparency will likely reduce some risks related to biased data and model transparency, while encouraging (and even enforcing) adoption of those same practices and norms throughout its ecosystem.

policies that govern their internal practices for responsible technology development; guidance, tools and educational resources for their customers’ responsible use of their technologies; and policies (enforced in terms of service) that govern the acceptable use of not only their platforms but also specific technologies, such as face recognition or gait detection.

At the same time, overreliance on a small, well-funded, global group of technology vendors to set the agenda for responsible and ethical use of AI may create a novel set of risks.

Audit is another area that, while promising, is also fraught with potential conflict. Companies such as O’Neil Risk Consulting and Algorithmic Auditing, founded by the author of Weapons of Math Destruction, Cathy O’Neil, provide algorithmic audit and other services intended to help companies better understand and remediate data and model issues related to discriminatory outcomes. Unlike, for example, audits of financial statements, algorithmic audit services are as yet entirely voluntary, lack oversight by any type of governing board, and do not carry disclosure requirements or penalties. As a result, no matter how thorough the analysis or comprehensive the results, these types of services are vulnerable to manipulation or exploitation by their customers for “ethics-washing” purposes.

, we must broaden our understanding of platforms beyond social media sites to other types of business platforms, examine those risks in context, and approach governance in a way that accounts not only for the technologies themselves, but also for the disparate impacts among industries and business models.

This is a time-sensitive issue

arge technology companies — for a range of reasons — are trying to fill the policy void, creating the potential for a kind of demilitarized zone for AI, one in which neither established laws nor corporate policy hold sway.