CHALLENGE OVERVIEW

Dataset Challenge: An Algorithm for Stories of Women
in History, Culture, Science and Technology​

​In this challenge, participants must develop an algorithm that can generate a character profile when provided
the name of a ​prominent female in history, science, technology, or culture including folklore and myth.
This algorithm is intended to scrape information from various sources off the internet and generate a character
​profile which includes a caption that is fewer than 100 words, and responses to metadata tags.
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EXPAND TO VIEW THE CHALLENGE PROCESS
​Challenge  From Program Team Perspective:
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Pre-Launch Steps 
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Launch the Challenge!
Post Launch
​Challenge  From Competing Team Perspective:
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Deliverables and tips for expanding your dataset:
In order to ensure the algorithm can be evaluated and considered complete the following deliverables are required:
  • Final version of the code in an accessible Git repository;
  • Reproducibility: we recommend you create a docker image which contains all dependencies and environments required for your algorithm to run; 
  • ReadMe file which contains description on how to run your algorithm;
  • Minimum system configuration required to run it;
  • Details of any Data used to train the model, outside of the dev set provided;
  • Missing any of the above will lead to disqualification.


Expanding your dataset:
We recommend that all teams expand the provided dev and test datasets and include data which fall into such categories as:
  1. Error conditions and Invalid Data – cannot be processed or are not content appropriate for this challenge.
  2. Data which cannot be evaluated – not enough information to produce enough metadata and tags.
  3. Good Datasets which are similar to provided datasets.
  4. Edge Cases: datasets which contain issues such as spelling errors, variations etc., making them difficult to process.
  5. Using Paid services to get additional data will lead to disqualification.
TIMELINE
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January - September: crowdfunding
March - July: pre-launch technical prep, dev set, tagging, annotation
September: Challenge is live
March 2021: Top 3 Prizes are announced

Meet Our Team of Advisors

​This dataset challenge will be lead by Davar Ardalan, founder of IVOW AI and Senior Advisor to AI Commons, together with Amir Banifatemi, 
​co-Founder of AI Commons and Global Innovation Lead at XPrize, senior software engineer Aprajita Mathur, Cyber Security and AI expert Nishan Chelvachandran, AI researcher and Kashyap Coimbatore Murali. In addition some 20 experts in data, cultural anthropology, software testing,
​AI & Ethics and engineering are also advising the project: ​
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EXPAND TO SEE LIST OF ADVISORS 
Adam Leon Smith: Adam is a technologist based in Barcelona, Spain. He is an expert on bias in artificial intelligence, and works with ISO and IEEE developing standards on bias. He is also the CTO of Dragonfly, a leading consultancy in AI and software quality.

Amir Banifatemi: Amir is General Manager, Innovation & Growth at XPRIZE Foundation and is managing partner of K5 Ventures. As the co-founder of AI Commons, Amir has 25 years of experience in development and growth of emerging and transformative technologies. 

Anna Royzman: Anna is the founder of A Quality Leadership Institute, a non-profit organization whose goal is to become the leading advocate for quality in technology through innovative educational programs. Anna serves as the Executive at Large on Association for Software Testing’s Board of Directors. 

Aprajita Mathur is an inventor, speaker, mentor and leader in space of software testing with an engineering background in Bioinformatics. She has 10+ years of software testing experience. She is also the chapter leader of “Women Who Test” in the Bay area and is working on growing this community. 

Archana Paladugu: Archana is a machine learning engineer, educator and researcher who received her PhD in Computer Science from Arizona State University in 2016 in Computer Vision and applied Machine Learning. She is passionate about personalized education, accessibility, and sustainability. 

Astrid Countee: Astrid is an anthropologist and technologist based in Houston, Texas. She is currently running operations at Cemvita Factory, an energy startup that harnesses synthetic biology for carbon dioxide utilization. Astrid seeks to combine her study of culture and knowledge with solutions based in digital technologies for social good. She is passionate about building inclusive AI systems and innovative technologies for a more diverse and sustainable future. 

Camille Eddy: Camille is a robotics engineer. She studies bias in AI and uses her international speaking platform to teach engineers, testers, and non-technical founders the use cases of de-biasing algorithms released today. Camille is currently an undergraduate student at the University of Idaho.

Davar Ardalan: Davar is the Founder and Storyteller in Chief of IVOW, specializing in AI-driven cultural content. She’s also an advisor to the non-profit AI Commons. Prior to this, she was a public media journalist for 20+ years, most of those at NPR News. In May 2014, she was the recipient of a United States Ellis Island Medal of Honor, for individual achievement and for promoting cultural unity. 

David Danks: David is Department Head and L.L. Thurstone Professor of Philosophy and Psychology at Carnegie Mellon University. His research largely falls at the intersection of philosophy, cognitive science, and machine learning, using ideas and frameworks from each to inform the others.

Emele Ugavule: Emele is a Tokelauan (Te Kaiga o Fagatiale, Nukunonu, Te Kaiga o Koloi, Uea) Fijian (Kaideuba) multi-disciplinary storyteller working across live performance and film as a  performer, writer, director, photographer and videographer. Emele is the Creative Director of Talanoa.

Ioannis A. Kakadiaris: Ioannis is a Hugh Roy and Lillie Cranz Distinguished University Professor of Computer Science at the University of Houston. Ioannis is focused on bold, disruptive, and impactful research and development in AI for identity and health. He and his team have developed value-driven solutions resulting from extensive customer discovery. Ioannis is passionate for developing creative ideas and building innovative technologies that change lives. He is the founder of the Computational Biomedicine Lab(www.uh.edu/cbl) and founded and served as the Director of the Borders, Trade, and Immigration Institute, a Department of Homeland Security Center of Excellence led by the University of Houston. ​

Inzamam Malik: Inzamam is an IVOW consultant and AI Chatbot and VUI Developer, who loves to explore and work with cutting edge technologies. He is a Lead Voice Apps Developer in Panacloud, and has more than two years experience in chatbot design and development. He dedicates his weekends to teaching state of the art technologies in major cities of Pakistan in collaboration with tech companies, NGOs, and universities.

Juanisa McCoy: Juanisa is a Lead UI/UX Designer. Her expertise involves such organizations as NASA, Oracle, ViaSat, and Cox Communications and where she has had the opportunity to build end-to-end rewarding experiences and interactions that people use every day. Nisa’s passion is people and how to engage and “wow” them. She believes that design can bridge the gap between cultures.

Kashyap Coimbatore Murali: Kashyap is an AI and Deep Learning Developer and Researcher who has programmed and delivered 6 MLH hackathon winning projects and interned at Genpact and EduChat. He is currently the Dean of the Princeton School of AI where he strives to provide world-class AI education for all for free. 

Kee Malesky: Kee directs research at IVOW and edits our publications. Her 30-year career as an award-winning news librarian for National Public Radio prepared her for background research and fact-checking on deadline, which she continues to find fascinating and fulfilling work. 

Mariana Lin: Mariana is a poet and creative writer. She worked as a creative director at Apple, where she was the principal writer for the voice of Siri, overseeing global character development in different languages and countries. She consults as lead character designer for Hanson Robotics’ Sophia. She writes on creativity and AI for the Paris Review.

Marie Smith Morrow: Marie is the Chief Information Officer of Data 360, an AI and machine learning platform as a service that reduces customer and employee acquisition costs by up to 90%. She is the inventor of the proprietary modeling language and system integration for one of the first ubiquitous AI and machine learning platforms which can be used in nearly any industry or organization for massive savings and operational efficiencies.

Nicola McLay: Nikki is a creative director and designer based Aotearoa/NZ. Her background in fine art, design, and digital development has led her to AI where she is drawn to the possibilities that come from weaving tradition and heritage with future systems for social good. 

Nishan Chelvachandran is the Chief Research and Innovation Officer of Saidot. Nishan is also a cybersecurity adviser, strategist, published author and researcher, with years of experience built on the strong foundations of bespoke operational activity in the UK Public Sector. Nishan’s research interests include Big Data keyword and behavioural analytics, and ethical frameworks for mass and automated data surveillance, profiling and decision-making, IoT, AI, and Data Use and Privacy.

Rogerio Lourenco: Rogerio is an information ethnographer. He lives in Rio de Janeiro, and has professional, academic, and life experiences with youth community media, controlled vocabularies, digital culture, metadata, public education, and gamification.

Sharada  Mohanty: Sharada  is  the CEO  and Co-founder  of AICrowd, an open source platform for organizing machine learning challenges on a variety of real world problems. Mohanty is also an aspiring artist, and has been experimenting with using methods from his research in AI as a form of creative expression.  

Collaborate with us!

If you're interested in offering technical and or financial support as part this dataset challenge, we'd like to hear from you.