Developing Artificial Intelligence systems require access to masses of data. This notion is common knowledge for computer engineers and data analysts. Data regulation is essential to ensure our safety, privacy, and ownership rights. Regulating the amount of data, the quality, and the priority with which organizations can access data are paramount. However, as with other areas of law, regulation could result in unwarranted results. One impact of data regulation is incentivizing the usage of low-quality data that often demonstrates bias. Public domain data, like books or movies from the early 1930s, could reflect early Western society's prejudice against blacks, women, and LGBTQ. A program using such data would lack a basic understanding of modern phrases and apply different meanings to day-to-day terms. A more excluded impact of data regulation might be associated with the GDPR "right to explanation" for machine-learning algorithms requirement, which is somewhat dubious in wording. Programmers expressed concern about these obligations, claiming that explaining the process is a significant challenge. For the past decade, legal scholars have grappled with the right balance between access to data (for innovation and AI development) and privacy and intellectual property rights. In my talk, I outline the legal barriers to data, explain the current development and exception for using data and argue for data AI emancipation.
Aviv Gaon is a senior lecturer at the Harry Radzyner Law School. He holds a Ph.D. from Osgoode Hall Law School, York University, and LL.B (Cum Laude) and LL.M from Reichman University. His work in the area of intellectual property and technology earned him the IP Osgoode David Vaver Medal for Excellence in IP Law. Dr. Gaon's scholarship focuses on Intellectual Property, Law & Technology and Competition Law. His publications address the legal implications of emerging technologies, intellectual property, and artificial intelligence.