Several articles written by prof Michal S. Gal are written about the Effects of Law on Data. 

Her academic articles about the subject are:

• Jorge Padilla, Michal Gal & Salvatore Piccolo, Optimal Legal Rules for the Assessment of Unilateral Conduct by Dominant Firms (Compass Lexecon, University of Haifa – Faculty of Law and University of Bergamo, 2025)

This article investigates what are the optimal legal rules for assessing unilateral conduct by dominant firms, maximizing long-term consumer welfare. Drawing on Decision Theory, it finds that the optimal rule is determined by the ratio of anti-competitive and pro-competitive plausibility and the relative costs of false positive and false negative errors. These optimal rules are often structured as rebuttable presumptions, suggesting technological changes may require revising both presumptions and the standard of proof.

• Michal Gal & Jorge Padilla, A General Framework for Analyzing the Effects of Algorithms on Optimal Competition Laws (Theoretical INquiries in Law, 2025) 

This article suggests a general framework for analyzing the effects of algorithms on optimal competition laws. It argues that the exponential growth of sophisticated algorithms challenges the economic presumptions embedded in existing laws. Applying old presumptions increases the instance of false negatives. The framework in the article employs Decision Theory to determine how laws must be optimally framed, resulting in a typology of six necessary legal effects, including the need for new prohibitions.

• Michal Gal & Daniel J. Hemel, Good Fences, and Big Data (University of Haifa – Faculty of Law and New York University School of Law, 2025)

This article reexamines the fencing costs theory of trade secrecy, which says that trade secret law substitutes for costly private fences. The rise of big data and AI challenges this premise by increasing the risk of malicious actors inflicting harm on third parties. Secret holders likely underinvest in private fencing relative to the social optimum. The idea of “good fences” turns the theory upside down. The good fences are secrecy precautions that protect, instead of harming, third parties.

• Tamar Giladi Shtub & Michal Gal, Data Without Borders: International Effects of Data Flow Regulation (University of Haifa – Faculty of Law and University of Haifa – Faculty of Law, 2024)

This article examines the complex challenges and implications of national regulation on data flows in an increasingly interconnected world. Local data flow regulation may create unforeseen externalities in other jurisdictions. Governments must recognize these externalities to balance economic growth, privacy, and national security. The findings underscore the urgent need for increased international cooperation on data governance frameworks.