"Equality and Privacy by Design" A New Model of Artificial Intelligence Data Transparency via Auditing, Certification, and Safe Harbor Regimes
Artificial Intelligence and Machine Learning (AI) are often described as technological breakthroughs that will completely transform our society and economy. AI systems have been implemented everywhere, from medicine, transportation, finance, art, to legal and social spheres, and even in weapons development. In many sectors, AI systems have already started making decisions previously made by humans. Promising as AI systems may be, they also pose urgent challenges to our everyday life. While much attention has concerned AI's legal implications, the literature suffers from a lack of solutions that account for both legal and engineering practices and constraints. This leaves technology firms without Professor Shlomit Yanisky Ravid, Ph.D. Fordham Law School, Visiting Professor; Fordham Law Center on Law and Information Policy (CLIP) Head of AI IP and Blockchain Project; Yale Law School, Information Society Project (ISP) Fellow; Ono Law School, Israel, Senior Faculty, the Shalom Comparative Legal Research Institute, OAC, Founder and Academic Director. Sean K. Hallisey, Fordham Law, CLIP, AI IP Project, Fellow. We gratefully dedicate this Article to Joel Reidenberg, the founder and the head of Fordham Law Center of Law and Information Policy (CLIP) for his initiative, support, and encouragement, all of which tremendously contributed to the writing of this Article and the development of its ideas. We would also like to thank all the Fellows at the Fordham CLIP IP AI and Blockchain Project, Yale Law, ISP, as well as to the students of the course "Intellectual Property and the Challenges of Advanced Technology: AI and Blockchain," for their wonderful discussions, insights and comments. Finally, we thank Dean Matthew Diller, Fordham Law School, for promoting and stressing the challenges of advanced technology, data privacy, and intellectual property, and Linda Sugin, Associate Dean for Academic Affairs at Fordham Law, for her support. 2019] FORDHAM URB. L.J. 429 guidelines and increases the risk of societal harm. It also means that policymakers and judges operate without a regulatory regime to turn to when addressing these novel and unpredictable outcomes. This Article tries to fill the void by focusing on data rather than on the software and programmers. It suggests a new model that stems from a recognition of the significant role that the data plays in the development and functioning of AI systems. Data is the most important aspect of teaching AI systems to operate. AI algorithms begin with a massive preexisting dataset, which data providers use to train the system. But the data that AI systems "swallow" can be illegal, discriminatory, altered, unreliable, or simply incomplete. Thus, the more data fed to the AI systems, the higher the likelihood that they could produce biased, discriminatory decisions and violate privacy rights. The Article discusses how discrimination can arise, even inadvertently, from the operation of "trusted" and "objective" AI systems. To address this problem, this Article proposes a new AI Data Transparency Model that focuses on disclosure of data rather than, as some scholars argue, focusing on the initial software program and programmers. The Model includes an auditing regime and a certification program, run either by a governmental body or, in the absence of such entity, by private institutions. This Model will encourage the industry to take proactive steps to ensure and publicize that datasets are trustworthy. The suggested Model includes a safe harbor, which incentivizes firms to implement transparency recommendations even without massive regulatory oversight. From an engineering point of view, the Model recognizes data providers and big data as the most important components in the process of creating, training and operating AI systems. Even more importantly, the Model is technologically feasible because data can be easily absorbed and kept by a technological tool. Further, this Model is also practically feasible because it follows already existing legal frameworks of data transparency, such as the ones being implemented by the FDA and the SEC. Improving transparency in data systems would result in less harmful AI systems, better protect societal rights and norms, and produce improved outcomes in this emerging field, especially for minority communities that often lack resources or representation to challenge AI systems. Increased transparency of the data used while developing, training or operating AI systems would mitigate and reduce these harms. Additionally, to better identify the risks of faulty data, industry players must conduct critical evaluations and audits of the data used to train AI systems; one way to incentivize this is a 430 FORDHAM URB. L.J. [Vol. XLVI certification system to publicize good faith efforts to reduce the possibility of discriminatory outcomes and privacy violations in AI systems. This Article strives to incentivize the creation of new standards, which the industry could implement from the genesis of AI systems to mitigate the possibility of harm, rather than post hoc assignments of liability.