NY Tech Week 2025

Description:

This workshop explores the case study of the world's first multicultural and multilingual AI Safety Red Teaming Challenge focused on Asia-Pacific, conducted by Humane Intelligence and IMDA Singapore. You'll get hands-on experience using our no-code platform to perform actual red teaming exercises that identify cultural biases and linguistic vulnerabilities across diverse contexts, equipping you with practical skills to evaluate and improve AI systems through inclusive testing methodologies.

About the Presenter: Sarah Amos, a former journalist turned product manager, is dedicated to bridging people with information through responsible systems, currently serving as a Product Manager at Humane Intelligence. Previously, she was a Product Manager at Twitter focusing on Civic Integrity, building features to mitigate the harms of platform manipulation, misinformation and abuse during global election cycles, and before that founded and led the R&D Department at Dataminr, an AI-enabled platform uncovering early signals of high-impact events from publicly available data.

Description:

Prompting is a common first point of contact between people and large language models (LLMs), and the quality of the prompt heavily influences the output of the model, the successful maintenance of system guardrails, and the overall user experience. Prompt creation is often highly dependent on specialized knowledge and can be challenging for non-AI experts because it’s iterative, time consuming, and prompting practices are constantly evolving. To address these problems, Responsible Prompting is an LLM-agnostic, lightweight recommender system that aims to dynamically support users in crafting prompts that reflect responsible intentions and help avoid undesired or negative outputs. In this workshop, we will show how Responsible Prompting API works and how it can be customized to your needs.

About the Presenter: Dr. Vagner Santana is a Staff Research Scientist, a Master Inventor, and a member of the Responsible Tech Team at the IBM T.J. Watson Research Center. He holds a PhD and MSc in Computer Science from the University of Campinas (UNICAMP). He is passionate about user behavior analysis, researching how people interact with devices in real contexts of use (e.g. IT services, Finance, Health Insurance, Education, Oil & Gas, Agriculture, etc.) aiming at responsible and inclusive user interface personalization, and Web technologies. Dr. Santana has an extensive professional software development background that includes being a webmaster for the prominent Brazilian newspaper, Folha de São Paulo. Since 2006, his research has been focused on Human-Computer Interaction (HCI) and exploring the intersection of HCI, Machine Learning, and Data Science.

Description:

Docling is an easy-to-use, self-contained, MIT licensed, open-source toolkit for document conversion that can parse several types of popular document formats into a unified, richly structured representation. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and can be run on a laptop. With Docling, it’s easy to implement extensions, new features, models, and customizations. Docling has been already integrated in other popular open-source frameworks (e.g., LangChain, LlamaIndex, spaCy), making it a natural fit for the processing of documents and the development of high-end applications. The open-source community has fully engaged in using, promoting, and developing for Docling, which recently made it a #1 trending repository on Github.

About the Presenter: Ming Zhao is an open-source developer and Developer Advocate at IBM Research, where he helps IBM leverage open technologies while building impactful tools and growing vibrant open-source communities. He’s passionate about making open tech accessible to all and ensuring developers have the tools they need to succeed in the rapidly developing AI space. Ming now leads community efforts around Docling, IBM’s fastest-growing open-source project, recently welcomed into the LF AI & Data Foundation.

Description:

To date, AI has consumed nearly all publicly available data. This raises an urgent question: Where can the next wave of data available for AI come from? Non-public, sometimes sensitive, or private data could be made available for AI if the right governance structures were placed around the use of such data. In this workshop, OpenMined will showcase an open-source protocol that can underpin a unified marketplace for AI, where private and licensed data sources can be securely networked, strictly controlled, and appropriately attributed.

About the Presenter: Subha Ramkumar is a Technical Product Manager at OpenMined, where she works on building decentralized and network-source AI applications. She brings a strong background in networking technology and has led product development efforts at Cisco and Consumer Reports, with a focus on consumer privacy and security products. Subha holds a master’s degree in Human-Computer Interaction from Carnegie Mellon University and is passionate about advancing responsible, user-centered AI infrastructure. .

Description:

The ongoing AI revolution presents both opportunities and challenges, especially for regulated industries like finance that need data privacy, auditability, and transparency. The growing availability of high-performing AI models and software components as open source, with their improved transparency, offers a strong alternative to the traditional proprietary approach to developing business applications. Join leaders in finance, technology, and policy to discuss how regulated businesses can responsibly leverage open-source AI for innovation and compliance.

Speakers:

• Dr. Anthony Annunziata, Director, Open Source Strategy, IBM
• Dr. Yacine Jernite, Head of Machine Learning and Society at Hugging Face
• Prof. Serdar Kadioglu, Group VP of AI at Fidelity
• Rebecca Qian, Co-Founder & CTO, Patronus AI

Moderator: Emilia David, Senior AI Reporter, VentureBeat