Ten Recommendations for Global AI RegulationOctober 4, 2023
Drawing on CIPL;'s years of experience as a thought leader and our extensive engagement with private sector leaders developing and deploying AI technologies, policymakers, and regulators, CIPL offers in this paper ten recommendations to guide AI policymaking and regulation to enable accountable, responsible, and trustworthy AI. These ten recommendations encapsulate CIPL’s view on a layered or three-tiered approach to AI regulation:
- Principle- and outcome -based rules, - Demonstrable organizational accountability, and - Robust and smart regulatory oversight. |
CIPL Recommendations on Adopting a Risk-Based Approach to Regulating AI in the EUMarch 22, 2021
Building on its prior work, CIPL has been working with experts in the EU and multinational companies who are leaders in AI to collect best practices and emerging trends in AI accountability. CIPL’s objective is to inform the current EU discussions on the development of rules to regulate AI. This paper summarizes CIPL’s vision on how to implement a risk-based approach to AI regulation and compliance. It is based on the premise that AI regulation must remain agile, just like the technology uses that it seeks to regulate. Hence, it should not aim for a one-size-fits-all approach or elimination of all risks. It must allow for the evaluation of contextual risks and benefits, mitigation of risks, honest error and constant improvement.
In this paper, CIPL recommends a risk-based approach to regulating AI applications comprised of: (1) A regulatory framework focusing only on AI applications that are “high risk”; (2) A risk-based organizational accountability framework that calibrates AI requirements and compliance to the specific risks at hand; and (3) Smart and risk-based oversight. |
Artificial Intelligence and Data Protection: How the GDPR Regulates AIMarch 12, 2020
The COVID-19 crisis is imposing a wide range of immediate and likely long-term impacts on organizations, governments, regulators, people and society at large. Many of them are likely to stay with us beyond the immediate crisis and change the way we all live, work and interact going forward. These impacts likely will also be felt in data privacy – from how we perceive this right in light of other rights to how we behave and what we expect. They will also change the way organizations and governments collect, use and share data (not just in the COVID-19 context, but generally).
In this paper, we examine some of these trends from the point of view of organizations, DPAs and society. We also try to anticipate how these trends may force all of these actors to adapt and change in the post-COVID-19 world, including and particularly DPAs. We also suggest that organizational accountability – championed, encouraged and enforced by DPAs – will be essential to mitigating any challenges that such changes might pose for data protection. |
Hard Issues and Practical SolutionsFebruary 27, 2020
The rise and rapid expansion of Artificial Intelligence technology is one of the main features of the Fourth Industrial Revolution. Its transformational potential for our digital society and ability to drive benefits for citizens, governments and organizations is unparalleled. To realize this potential and ensure its sustainability, we must build AI on a foundation of trust and respect for our human values, rights and data privacy laws.
This second report from the Centre for Information Policy Leadership (CIPL) in our project on Artificial Intelligence and Data Protection aims to provide insights into emerging solutions for delivering trusted and responsible AI. |
Artificial Intelligence and Data Protection in TensionOctober 29, 2018
This report will introduce artificial intelligence and some of the technologies enabled by it, as well as some of the challenges and tensions between artificial intelligence and existing data protection laws and principles. The challenges to data protection presented by AI are frequently remarked on but are often addressed only at a surface level. There is an urgent need for a more nuanced, detailed understanding of the opportunities and the issues presented by AI and of practical ways of addressing these challenges, in terms of both legal compliance and ethical issues that AI raises.
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