Granular legal norms: big data and the personalization of private law

Busch, Christoph; Franceschi, Alberto De

doi:10.4337/9781788111300.00025

Cited by 26 publications

(3 citation statements)

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“…Specifically, big data bring in potential developments and challenges for inheritance law [ 35 , 36 ]. For example, with empirical research from big data, a personalized default rule can diversify the inheritance results [ 37 ]. Besides, with the development of astronautics, when people can travel in the whole universe at nearly the speed of light, the special theory of relativity is in charge, and it is essentially difficult to determine the time order for two events (e.g., the death of two persons) happen in far away locations [ 38 ].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Impossibility results about inheritance and order of death

Wang

2022

PLoS ONE

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If several relatives died with no will, the order of their deaths could affect the inheritance result. When the order of death is unknown, there are three approaches to determine the inheritance result in this simultaneous death situation: apply an inheritance method that is not affected by the order of death; artificially assign the order of death; stipulate that persons with unknown orders do not inherit each other. The last approach is adopted by the current French Civil Code (denoted as the French Approach). We prove that under some basic requirements, the French Approach is the only valid solution to the order of death problem. Therefore, the inheritance law of a country that does not adopt the French Approach either has unsolvable inheritance problems or violates basic requirements. In the appendix, we study the existence and uniqueness of inheritance methods that are invariant for different orders of death and only violate one requirement, such as gender equality.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Impossibility results about inheritance and order of death

Wang

2022

PLoS ONE

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“…In any case, future "personalized" information based on customer preferences, needs, capabilities, by way of analysis massive data stored by the manufacturer, can allow to "personalize" liability (Busch & De Franceschi, 2018;Busch, 2019) avoiding the one-size-fits-all rule (Ben Shahar & Porat, 2016). AI actually makes it possible (Schulte-Nölke et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Producer Liability for AI-Based Technologies in the European Union

Navas¹

2020

ILR

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The manufacturer's liability for defective products has remained almost unmodified since 1985 when Directive 85/374/EEC (=PLD) was enacted. Perhaps new technology based on artificial intelligence (=AI) could bring about a turning point in the regulation if concepts such as "product" and "defect" or aspects such as "grounds of liability", the so-called "development risks defense", and the "solidarity" are reconsidered. The Group of Experts on Liability and New Technologies (=NTF), in its “Liability for AI and other emerging digital technologies” Report, recommends, inter alia, the regulation of two different civil liability regimes: strict liability and fault-based liability. Thus, it will be necessary to determine precisely the cases to which these regimes apply and how to deal with “uncertain causation”. The alleviation of the victim’s burden of proof should be considered. From the various documents being published, it appears that the producer’s strict liability will remain as the main liability rule, but it will be combined -as the NFT suggests- in the case of the breach of a duty of care with a fault-based liability rule. This approach leaves some open questions, i.e., how to properly combine both grounds of liability in the domain of products that cause damages. In my view, the liability regime suggested by the NTF is far more complicated that the regime which distinguishes three types of defects that are often stressed: the defect of design, the defect of manufacturing, and the defect of information.

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“…18 Black (1996), at 27 speaks of 'individualised regulation' to refer to regulation tailored at the single, individual firm. More recently, see: Strahilevitz and Porat (2014); Busch (2016b); and Busch and De Franceschi (2018). 19 Algorithmic decision-making may also re-ontologize the world 'by understanding and conceptualizing it in new, unexpected ways, and triggering and motivating actions based on the insights it generates'.…”

Section: Regulatory Functions Of Digital Platforms Classifications and Issuesmentioning

confidence: 99%

Co-regulating algorithmic disclosure for digital platforms

Porto¹,

Zuppetta

2020

Policy and Society

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With digital platforms gaining dominant intermediating role and exerting regulatory functions vis-à-vis small and medium-sized enterprises (SMEs) through algorithms, EU institutions have started considering to rely on their analytical capacity to regulate the myriads of market transactions occurring within and through them (so-called platformto-business, or P2B transactions). Most of the time, the EU suggests recurring to light-tough disclosure duties. Hence, the European model falls short in rebalancing information asymmetry and unequal bargaining power plaguing the SMEs. In practice, the EU model consists either in pure delegation of self-regulatory powers (codes of conduct) or nonenforceable co-regulatory schemes (with technical standards established by the platforms themselves). Other models have been suggested that rely on the regulator's access to the platform's data (so called savvy and data-delegated options). These governance models present several limitations, making the platforms' role as regulatory intermediators little credible. In this scenario, the paper purports that a third option should be considered. In particular, to tackle the multifaceted risks associated with algorithmic decisions by digital platforms, while at the same time avoiding stifling innovation, it makes three suggestions: (1) also information disclosures should be done by an algorithm; (2) that is pre-tested in a co-regulatory process, that involves the regulator and stakeholders; and (3) enforced through legal and other empowerment tools, rather than sole fines.

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Granular legal norms: big data and the personalization of private law

Cited by 26 publications

References 0 publications

Impossibility results about inheritance and order of death

Impossibility results about inheritance and order of death

Producer Liability for AI-Based Technologies in the European Union

Co-regulating algorithmic disclosure for digital platforms

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