Description

The precautionary principle is a strategy for approaching issues of potential harm when extensive scientific knowledge on the matter is lacking. It emphasizes caution, pausing, and review before leaping into innovations that may prove disastrous [Read, O’Riordan], and is being used to inform policymaking related to technical systems that have to cope with the complexity and variability of the real world [Bar-Yam, Norman, Marcus, Taleb].

So far the conversation around AI trust issues has been centered mainly on social displacement factors (effects on job security, privacy, etc), and cybersecurity (is trust a vulnerability? if so, should we design systems that are not automatically trusted but always verified?) — it is only now that exposures arising from higher-order uncertainties such as quantum advantage and decentralized intelligent agents are being seriously considered, given the recent developments in the adoption and stage of maturity of those technologies.

Since 2019 the allocation of research budgets for Responsible and Trustable AI has increased dramatically across academia and the private sector. However, many open questions remain in regard to the attainability of tradeoffs between conflicting goals: uncertainties and risks around issues of fairness, transparency, privacy, robustness, explainability, security, and, data provenance. Moreover, is trustability even possible when one should assume that every component of the system can be hostile? This workshop intends to foster the discussion between scientists and practitioners, to explore open questions around trustworthy artificial intelligence that may benefit from the application of the Complex Systems Engineering approach.

This workshop aims at bringing together experts in Applied Mathematics, Computer Science, Philosophy, and, Risk Forecasting, to discuss topics such as Ethical Machine Learning standards and Practical Ethics in the field of AI. The discussion will focus on current research questions, novel developments (i.e from interconnected systems such as blockchain and quantum computing), and future applications of this emerging area of research in disciplines such as Law, Finance, Defense. The target audience is social and natural scientists, policymakers, and, investment professionals looking forward to understanding and make contributions to the body of knowledge and the narrative about AI Ethics.

Researchers and practitioners are invited to submit their contributed papers/ articles for inclusion in the workshop website repository, which will be used as reference materials for attendants.

Conference documents can be found at this link: