In this paper, I plan to argue that algorithms should be integrated into the board structure of data-driven enterprises because of the unmanageable volumes of data they generate. Thus, data governance frameworks are crucial for management but especially for board decision-making. How to include data governance in the boardroom? This article illustrates how data governance can be implemented in the boardroom by creating a hybrid model: an “algorithm” next to the human board composition. This boardroom model includes a legal entity (a Data Governance Firm—DGF) for designing an algorithmic board member consistent with the corporation’s purpose. The DGF’s role in developing an algorithmic seat is to transform the corporation and its constituencies into complex intersectional relationships based on data. Therefore, rather than being a nexus of contracts, modern corporations and their constituencies can be included in datasets showing a nexus of data.
Biotech companies have resorted to blockchain and unregulated vehicles, such as initial coin offerings, as a cheap way of monetizing DNA while funding the company. This paper explains how biotech blockchain companies have promised investors control over genetic data but obscured other dynamics within the company ownership structure. Not only do token holders lack voting rights, but they are in a new vulnerable position: the company has possession of their genetic data. At the same time, traditional shareholders surreptitiously gain more power and control. This paper’s analysis will shed light on the relationship between shareholders who invested capital and token holders whose interests depend on the human genetic capital shared with the company and the emerging implications from this scenario.
DeFi, or Decentralized Finance, is the technological distribution of financial services empowered by blockchain. This alternative market deals with the same processes of traditional finance involving the creation, management, and investment of money and financial assets. However, the fundamental difference in DeFi gravitates around the multiple financial intermediaries substituted with applications in permissionless systems that automatize cryptoassets trade (a kind of digital asset). This article explores the characteristics of DeFi and the relevant players, evidencing how decentralization and disintermediation are misnomers in innovation technology in Web 3.0, the transactions, and the legal issues (and regulatory gaps) from the US perspective.
This article illustrates how comparative law would benefit from the scientific method to bolster its reliability when comparing legal systems. It proposes to improve the method of comparison by creating a testable hypothesis that can help theorize and substantiate the functionalist or differentialist analysis. Empirical research would not obscure current comparative methodologies and tools but would enhance them by mapping a concrete interaction between theory and human activity. To this end, I introduce a comparative analysis example of traditional markets and cryptomarkets to uncover the elements of a successful cryptoassets sale. Finally, the paper focuses on different types of quantitative empirical legal research and methods used in legal studies and how they can be connected to comparative law. It concludes by identifying the limitations of this methodology as applied to comparative law and previewing a future of combined methods.
The technology around genomics has moved at a swift pace while reducing the entire sequencing costs. Biotech advancement increased the demand for gene sequencing services and data storage, creating privacy risks and making de-identification difficult. To mitigate those risks, genomic startups have turned to cryptographic technologies like Blockchain. However, Blockchain is not a panacea, and privacy issues in genomics are a concern that is hardly addressed from a bioethical or philosophical perspective. The most challenging obstacle plaintiffs face in privacy lawsuits is assessing the injury–the connection between privacy threats and concrete harms. This paper explains why genomic companies that use Blockchain expose their users to specific harms that fall outside agencies and regulations radars by providing some examples of injuries-in-fact.