AI applications in government range from low-risk chatbots to high-risk systems for predictive analytics, fraud detection, and automated decision-making.
A three-pronged approach to building trust involves training teams on ethics, adhering to a code of conduct, and embedding ethics enablers in projects.
OpenAI uses a 'teach, test, and share' framework that includes curating training data, red teaming models, and incorporating user feedback to ensure responsible development.
Case studies from Austria and the Netherlands reveal how biased AI systems in government led to discrimination and injustice in job placement and child benefits.
Implementing features like transparent automation notifications and opt-in voice bots gives users autonomy and control, which helps build trust in AI systems.
True transparency in AI involves not just explaining decisions but also clarifying the system's core purpose and using tools like model cards to document its behavior.
While regulations like the EU AI Act may introduce process overhead, they provide a necessary risk-based framework for ensuring AI is developed responsibly.
Developers should embrace their societal responsibility, adopt an iterative 'build, ship, learn' mindset, and always center the specific use case and affected stakeholders.
Everyone can contribute to more trustworthy AI by educating themselves, actively using the tools, providing feedback, and engaging in public discourse about its use.
29:35Steffen Bosse, Mina Saidze, Ray Eitel-Porter & Björn Bringmann
28:05Toju Duke
30:28Alexandra Wudel, Madalina Florean & Laura Moritz
31:15Thomas Wollmann, Gerrit Einhoff, Kara Sprague & Alexandra Wudel
28:07Vincent Slot & Jaap Kersten
28:54Torsten Stiller
28:19Jeff Hausmann, Jasmin Kaiser, Bernd Datler & Sonja Alvarez
28:27PJ Hagerty
Jobs that call for the skills explored in this talk.
TRUSTEQ GmbH
Serviceware SE
Government of The United Kingdom