Responsible AI Track
As data proliferates and becomes more freely available, the power of driving impact in social sector increases.
See the many ways organizations are applying their data science infrastructure in the name of making the world a better place.
Learn through stories of success and failures, and core practices that are implemented by change makers in the social sector that can differ from industry and academia.
Get exposed to data science & Machine learning workflows and models being utilized steered towards causes like climate change, agriculture, socio-economic impacts, disaster management etc.
Some of Our Past Responsible AI Speakers
Alessandro Romano
Alessandro is a highly experienced data scientist with a Bachelor’s degree in computer science and a Master’s in data science. He has collaborated with a variety of companies and organizations and currently holds the role of senior data scientist at logistics giant Kuehne+Nagel. Alessandro is particularly passionate about statistics and digital experimentation and has a strong track record of applying these skills to solve complex problems. He shares his knowledge regularly, speaking at events like the Data Innovation Summit and DataMass Gdansk Summit.
The Crucial Role of Digital Experimentation and A/B Testing in the AI Landscape(Talk)
Parul Pandey
Parul Pandey has a background in Electrical Engineering and currently works as a Principal Data Scientist at H2O.ai. Prior to this, she was working as a Machine Learning Engineer at Weights & Biases. Parul is one of the co-authors of Machine Learning for High-Risk Applications book, which focuses on the responsible implementation of AI. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin’s Top Voices in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
Machine Learning for High-Risk Applications – Techniques for Responsible AI(Tutorial)
Walid S. Saba
Walid Saba is a Senior Research Scientist at the Institute for Experiential AI at Northeastern University. Prior to joining the institute in 2023, he worked at two Silicon Valley startups, focusing on conversational AI. This work included high-level roles as the principal AI scientist for telecommunications company Astound and CTO of software company Klangoo, where he helped develop its state-of-the-art digital content semantic engine (Magnet).
Saba’s career to date has seen him hold various positions in both the private sector and academia. His resume includes entities such as the American Institutes for Research, AT&T Bell Labs, IBM and Cognos, while he has also spent a cumulative seven years teaching computer science at the University of Ottawa, the New Jersey Institute of Technology (NJIT), the University of Windsor (a public research university in Ontario, Canada), and the American University of Beirut (AUB).
Walid is frequent invited for interviews and as a keynote speaker on AI and NLP has published over 45 technical articles, including an award-winning paper that he presented at the German Artificial Intelligence Conference (KI-2008). Walid received his BSc and MSc in Computer Science from the University of Windsor, and a Ph.D in Computer Science from Carleton University in 1999.
Nils Reimers
Nils Reimers is an NLP / Deep Learning researcher with extensive experience on representing text in dense vector spaces and how to use them for various applications. During his research career, he created sentence-transformers that were the foundation for many today’s semantic search applications.
In 2022, Nils joined Cohere.com to lead the team on smarter semantic search technologies and how to connect LLMs to enterprise data. Here, his teams develop new foundation models that can understand and reason over complex data.
Connecting Large Language Models – Common Pitfalls & Challenges(Talk)
Bob Foreman
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
Data for Social Good – Find Your Paradise!(Workshop)
HPCC Systems – The Kit and Kaboodle for Big Data and Data Science(Solution Showcase)
Abstract:
Learn why the truly open source HPCC Systems platform is better at Big Data and offers an end-to-end solution for Developers and Data Scientists. Learn how ECL can empower you to build powerful data queries with ease. HPCC Systems, a comprehensive and dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow. Topics include HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security and more.
Krishnaram Kenthapadi
Krishnaram Kenthapadi is the Chief AI Officer & Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the senior program committees of FAccT, KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 7000+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, model monitoring, responsible AI, and generative AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, given several invited industry talks, and instructed a course on responsible AI at Stanford.
Deploying Trustworthy Generative AI(Tutorial)
What You'll Learn
Talks + Workshops + Special Events on these topics:
Responsible AI: From Principles to Practice
Using AI to Overcome Bias & Make Hiring More Equitable
Artificial Intelligence for Conservation and Sustainability: From the Local to the Global
Machine Learning and Robotics in Healthcare Devices and Rehabilitation
Advances and Frontiers in Auto AI and Machine Learning
Federated Learning for User Privacy
Explainable AI: human in the loop
Reproducability
AI Risk to Companies
Incident Response in a World of Evolving Threats
and more…
Why Attend?
Accelerate and broaden your knowledge of key areas in Responsible AI
With numerous introductory level workshops, get hands-on experience to quickly build up your skills
Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today
Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels
Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms
Connect with peers and top industry professionals at our many networking events to discover your next job, service, product, or startup.
Who should attend
The AI for Social Good Track is where industry’s top creative minds gather to discuss and shape the most challenging social problems. Whether you are an expert, or just starting your journey, this is the conference for you.
Data scientists looking to build an understanding of ethical intelligent machines
Data scientists seeking to investigate and define potential adverse biases and effects, mitigation strategies, fairness objectives and validation of fairness
Anyone interested in understanding areas such as fairness, safety, privacy and transparency in artificial intelligence and data
Business professionals and industry experts looking to understand data science ethics in practice
Software engineers and technologists who need to develop algorithms to solve fundamental algorithmic fairness problems
CTO, CDS, and other managerial roles that require a bigger picture view of data science
Technologists in the field of AI Fairness and others looking to learn mitigation strategies, algorithmic advances, fairness objectives, and validation of fairness
Students and academics looking for more practical applied training in data science tools and techniques
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