Jin Mi
Head of Downstream Process Development
Spark Therapeutics
Getting one or two AI models into production is very different to running an entire enterprise or product on AI, and as AI is scaled, problems can (and often do) scale too.
- Standardizing how you build and operationalize models
- Focusing teams where they’re strongest
- Introducing MLOps and establishing best practices and tools to facilitate rapid, safe, and efficient development and operationalization of AI
Raynor Ren
Principal Engineer AL/ML
Amazon
Pradeep Gupta
Senior Director- AI Industries, Solutions and Architecture
NVIDIA
Failure to adequately explain model development, working and outcome inherently invites both regulatory and customer scrutiny, especially when things go wrong.
- The extent to which customers need to know how and why a particular outcome has been reached
- Do you need to understand black box models and if so, why?
- Where is explainability a luxury and where is it absolute necessity
- Lessons learned from failures and how explainability could have helped
Agus Sudjianto
Executive Vice President, Head of Model Risk
Wells Fargo
Agus Sudjianto is an executive vice president, head of Model Risk and a member of the Management Committee at Wells Fargo, where he is responsible for enterprise model risk management. Prior to his current position, Agus was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom. Before joining Lloyds, he was an executive and head of Quantitative Risk at Bank of America. Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Company. Agus holds several U.S. patents in both finance and engineering. He has published numerous technical papers and is a co-author of Design and Modeling for Computer Experiments. His technical expertise and interests include quantitative risk, particularly credit risk modeling, machine learning and computational statistics. He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.
Even without intentionally prejudice data or development practices, AI can produce inequitable results. How can organizations ensure they are mitigating bias at all levels and reducing the risk of reputational, societal and regulatory harm?
Hooman Sedghamiz
Senior Director of AI & ML
Bayer
Hooman Sedghamiz is Director of AI & ML at Bayer. He has lead algorithm development and generated valuable insights to improve medical products ranging from implantable, wearable medical and imaging devices to bioinformatics and pharmaceutical products for a variety of multinational medical companies.
He has lead projects, data science teams and developed algorithms for closed loop active medical implants (e.g. Pacemakers, cochlear and retinal implants) as well as advanced computational biology to study the time evolution of cellular networks associated with cancer , depression and other illnesses.
His experience in healthcare also extends to image processing for Computer Tomography (CT), iX-Ray (Interventional X-Ray) as well as signal processing of physiological signals such as ECG, EMG, EEG and ACC.
Recently, his team has been working on cutting edge natural language processing and developed cutting edge models to address the healthcare challenges dealing with textual data.
All systems fail at some point, no matter how much time and rigor are put into their design and development. AI is not immune, susceptible to attacks, exploitation and unexpected failures. This session will be broken into two presentations to explore:
Design and build:
- Top tips for designing, building and ensuring robustness and resilience in AI
- Improving the robustness of AI components and systems
- Designing for security challenges and strategies for risk mitigation
Testing and evaluation:
- How to test, evaluate and analyze AI systems
- Adopting comprehensive test and evaluation approaches
- Which protocols can be applied and where new approaches are required
Aysha Machingara
Risk Strategy- Decision Science & Analytics
Uber
AI has the potential to unlock significant long-term value but trust needs to be felt both by the company deploying the AI and the customers that will experience it and its outcomes. This discussion will explore the success and failures organizations have faced with:
- Effectively building trust and conveying the net benefits for all parties
- Human circuit breaks as a safety mechanism
- Leveraging trust to create more desirable business outcomes
- The definition of trust in the context of AI – is it in the development process, the outcome or both?
Suhas Manangi
Group Product Manager- AI,ML Defense Platform
Airbnb
Suhas Manangi is the product head of the AI/ML Defense Platform team at Airbnb, where he leads work on accelerating the use of AI and machine learning in fighting fraud and abuse to ensure trust and safety on the online marketplace. Before joining Airbnb, he spent many years working with trust and safety teams at Amazon, Lyft, and Microsoft. Manangi is also active in the product management community, helping product managers transition to using AI/machine learning in their products.
An opportunity to hear first-hand insight and ask questions on how regulation is taking shape, what to expect and what you can do to prepare now.
Get ahead of the curve with an understanding of how different businesses are proactively preparing as the US and EU start to align on future AI regulation. This panel will explore how regulation is likely to take shape and what organizations should be doing now to ensure they are proactively prepared.
Shawn Rizvi
Integrity Risk Launches - Artificial Intelligence & Machine Learning
Meta
Shawn (CIPP, CDPSE) has over 10 years of experience in managing Data Risk across Regulatory, Privacy, Governance and Cybersecurity vectors. He started his career with IBM in 2012 and would go on to join Deloitte’s Canadian practice in 2015 and EY’s Canadian practice in 2018. In 2021 he would become the CPO at the Edtechn Unicorn – Applyboard where established and led the company’s first Privacy & Data Governance department, program and strategy. His strategic leadership was critical to Applyboard’s success in preventing major operational cost from rapidly evolving regulatory changes across its over 120 countries in which it operated in. He is now currently at Meta, where he works within the Integrity and Risk Launches organization and has successfully helped launch over 300 related AI and ML related projects. He has also helped drive EU Digital Service Act and Digital Markets Act preparedness across multiple Meta products.
Shawn has worked with market leaders across the globe to drive better business decisions based on enabling and scaling AI technologies through effective risk management and data governance techniques. This includes helping global brands navigate complex regulations and pioneering new frameworks that ensure cutting edge technology empowers society responsibly. He is also a subject matter expert on over 100 different regulations, framework and practices related to data risk. On his spare time, he provides mentorship to start up tech firms in Toronto and is passionate about helping start-ups succeed.
Karen Silverman
CEO and Founder
The Cantellus Group
Karen is a leading global expert in practical governance strategies for AI and other frontier technologies. As the CEO and Founder of The Cantellus Group, she advises Fortune 50 companies, startups, consortia, and the public sector on how to manage cutting-edge technologies in a rapidly changing policy environment. Her expertise is informed by more than 20 years of practice and management leadership at Latham & Watkins, LLP where she advised global businesses in complex antitrust matters, M&A, governance, ESG, and crisis management. Karen chairs the board of a public benefit corporation developing complex content moderation tools. She is an SME for the Business Roundtable's Responsible AI Initiative, and a World Economic Forum Global Innovator and Karen sits on its Global AI Council. She serves on the boards of Krunam, AI.EDU, Legal Momentum and Not For Sale.
Brandon Allgood
Chief AI Officer
Valo