Executive Summary: The U.S. Department of State is releasing a “Risk Management Profile for Artificial Intelligence and Human Rights” (the “Profile”) as a practical guide for organizations—including governments, the private sector, and civil society—to design, develop, deploy, use, and govern AI in a manner consistent with respect for international human rights.[1] When used in a […]
A code of ethics is an aspirational document that sets principles and guides the moral and ethical decisions for an organization. In the case of ASIS International, our Code of Ethics details the ethical standard to which all security professionals who are members of ASIS must hold themselves.
A code of conduct is a set of rules and guidelines that specify the behaviors, practices, and actions that are required of members of an organization. In the case of ASIS International, our Code of Conduct mandates acceptable behavior of ASIS members, partners, and participants of any kind at all ASIS events, including chapter events, community meetings, and Global Security Exchange, and acceptable use of the ASIS brand.
The work of the EDSAFE centers around the SAFE Benchmarks Framework as we engage stakeholders to align equitable outcomes for all learners and improved working experiences for dedicated and innovative educators. We intend to clarify the urgency and specific areas of need to prevent failures in data management that compromise the potential for how responsible AI can be a lever for equity and innovation while protecting student privacy. Frameworks and benchmarks are important to innovation as a means of targeted guidance, focusing disparate efforts towards shared objectives and outcomes and ensuring the development of appropriate guidelines and guardrails.
In this article, Luciano Floridi argues that the development of AI in terms of successful agency without intelligence does not lead to any fanciful realisation of science fiction scenarios (Singularity), which are at best distracting and at worst irresponsible.
Marius Wehner und Lynn Schmodde von der Wirtschaftswissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf berichten von ihrer Forschung zu Learning Analytics. Im Verbundprojekt LADi haben sie Diskriminierungspotenziale und Bias in den Algorithmen untersucht sowie die Wahrnehmung der Lernenden von Beurteilungen durch Learning Analytics. Interviewer in Folge 11 des DINItus Podcasts ist Erik Reidt vom ZIM/Multimediazentrum der HHU Düsseldorf.
J. Whittlestone, R. Nyrup, A. Alexandrova, и S. Cave. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, стр. 195–200. New York, NY, USA, Association for Computing Machinery, (27.01.2019)