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 Previous Responsible AI Speakers
Kira Radinsky, PhD
Dr. Kira Radinsky is the CEO and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. In the past, she co-founded SalesPredict, acquired by eBay in 2016, and served as eBay director of data science and IL chief scientist. One of the up-and-coming voices in the data science community, she is pioneering the field of medical data mining. Dr. Radinsky gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics. In 2013, she was named to the MIT Technology Review’s 35 Young Innovators Under 35, in 2015 as Forbes 30 under 30 rising stars in enterprise technology, and in 2016 selected as “woman of the year” by Globes. She is a frequent presenter at global tech events, including TEDx, Wired, Strata Data Science, Techcrunch and academic conferences, and she publishes in the Harvard Business Review. Radinsky serves as a board member in: Israel Securities Authority, Maccabi Research Institute, and technology board of HSBC bank. Dr. Radinsky also serves as visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine.
Mosharaf Chowdhury, PhD
Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.
Ilana Golbin
Ilana is a Director in PwC Labs (Emerging Tech & AI), where she serves as one of the leads for Artificial Intelligence. Ilana specializes in applying machine learning and simulation modeling to address client needs across sectors regarding strategic deployment of new services, operational efficiencies, geospatial analytics, explainability and bias. Ilana is a Certified Ethical Emerging Technologist, is listed as one of 100 “Brilliant Women in AI Ethics” in 2020, and was recently recognized in Forbes as one of 15 leaders advancing Ethical AI. Since 2018, she has led PwC’s efforts globally in the development of cutting-edge approaches to build and deploy Responsible AI.
Emerging Approaches to AI Governance: Tech-Led vs Policy-Led(Talk)
Scott Zoldi, PhD
Scott Zoldi is chief analytics officer at FICO responsible for advancing the company's leadership in artificial intelligence (AI) and analytics in its product and technology solutions. At FICO Scott has authored more than 120 analytic patents, with 71 granted and 49 pending. Scott is actively involved in the development of analytics applications, Responsible AI technologies and AI governance frameworks, the latter including FICO's blockchain-based [SZ1] model development governance methodology. Scott is a member of the Board of Advisors of FinRegLab, a Cybersecurity Advisory Board Member of the California Technology Council, and a Board Member of Tech San Diego and the San Diego Cyber Center of Excellence. He is also a member of the CNBC Technology Executive Council. Scott received his Ph.D. in theoretical and computational physics from Duke University.
Sadie St Lawrence
Sadie St Lawrence is the Founder and CEO of Women in Data, a community of 30,000+ data leaders, practitioners, and citizens whose mission is to increase diversity in data careers. Women in Data has been named a Top 50 Leading Company of The Year, and has been rated as the #1 community for Women in AI and Tech. Sadie has trained over 400,000 people in data science and has developed multiple programs in machine learning and career development. Sadie has been awarded, Top 30 Most Inspiring Women in AI, Top 10 Most Admired Businesswomen to Watch in 2021, Top 21 Influencer in Data, and is the recipient of the Outstanding Service Award from UC Davis. In addition, she serves on boards, and is the host of the Data Bytes podcast.
Creating An Ethical AI Environment (Business Talk)
Serg Masis
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. He’s an Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether concerning leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” with do-it-yourself projects for AI hobbyists and practitioners alike.
Facial Recognition from Scratch with Python and JS(Workshop)
Raluca Ada Popa, PhD
Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.
Confidential Data Computing and Collaboration for Data Scientists(Keynote)
Nadia Fawaz, PhD
Nadia Fawaz is a Senior Staff Applied Research Scientist and the Technical Lead for Inclusive AI at Pinterest. Her research and engineering interests include machine learning for personalization, AI fairness and data privacy, and her work aims at bridging theory and practice. She was named one of the 100 Brilliant Women in AI Ethics 2021, her work on Hair Pattern Search was recognized in the AI and Data category on Fast Company’s World Changing Ideas 2022 list with an honorable mention, and her work on inclusive AI was featured in many news outlets, including The Wall Street Journal, Fast Company, Vogue Business and CBS. She was a winner of the ACM RecSyS challenge on Context-Aware Movie Recommendations CAMRa2011 and her 2012 UAI paper was featured in an MIT TechReview article as “The Ultimate Challenge For Recommendation Engines”. Earlier, she was a Staff Software Engineer in Machine Learning and the Tech Lead for the job recommendation AI team at LinkedIn, a Principal Research Scientist at Technicolor Research lab, and a postdoctoral researcher at the Massachusetts Institute of Technology, Research Laboratory of Electronics. She received her Ph.D. in 2008 and her Diplome d’ingenieur (M.Sc.) in 2005 both in EECS from Telecom ParisTech and EURECOM, France. She is a member of the IEEE and of the ACM.
Cal Al-Dhubaib
Cal Al-Dhubaib is a data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin.
Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in numerous publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.
Jyotika Singh
Bio Coming Soon!
Veena Mendiratta, PhD
Bio Coming Soon!
Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems(Talk)
Alex Ratner, PhD
Alex Ratner is the co-founder and CEO at Snorkel AI, and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.
Operationalizing Organizational Knowledge with Data-Centric AI(Talk)
Meg Kurdziolek, PhD
Meg is currently the Lead UXR for Intrinsic.ai, where she focuses her work on making it easier for engineers to adopt and automate with industrial robotics. She is a “Xoogler”, and prior to Intrinsic worked on the Explainable AI services on Google Cloud. Meg has had a varied career working for start-ups and large corporations alike, and she has published on topics such as user research, information visualization, educational-technology design, voice user interface (VUI) design, explainable AI (XAI), and human-robot interaction (HRI). Meg is also a proud alumnus of Virginia Tech, where she received her Ph.D. in Human-Computer Interaction.
Priya Donti, PhD
Priya Donti is a Co-founder and Chair of Climate Change AI, a non-profit initiative to catalyze impactful work at the intersection of climate change and machine learning, which she is currently running through the Cornell Tech Runway Startup Postdoc Program. She will also join MIT EECS as an Assistant Professor in Fall 2023. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her work explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University, and is a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.
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
ODSC WEST 2023 - Oct 30th – Nov 2nd
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