Machine Representation of Data Analyses¶

Challenges of data-intensive social good¶

Applying AI and data science to social good is exciting,
but there are special challenges:

• Not a "closed world"
• Collaboration is essential:
  • AI researchers
  • Data scientists
  • Subject-matter experts
  • Policymakers
  • Philanthropists
  • ...

Case study: Accelerated Cure Project

• Mission: Stimulate MS research through open-access data repository
• Challenge: How to share and summarize data analyses?

Similar challenges in any social good enterprise involving complex, data-driven questions

A new kind of data science platform?¶

Our motivation: create a cloud platform for collaborative data science

Equipped with AI features such as:

• Recommend relevant data analyses and datasets
• Identify similar work on a given dataset
• Organize analyses in a given domain
• Evaluate analyses using appropriate metrics

Machine representation of data analysis¶

These features require a rich, machine-interpretable representation of platform content

Our contribution in one line:

Automatically extract a dataflow representation of a data analysis, which is interpretable by machines

Example: Exploratory data analysis for ACP¶

Example: Dataflow graph¶

Methodology¶

Our system is based on dynamic program analysis. At highest level, three steps:

1. Execute and trace program, getting a DAG of function calls
2. Annotate function calls and objects using an annotation database for statistical software
3. Align graph with knowledge base of data analysis concepts

Machine-assisted social good¶

A dream: a knowledge ecosystem that is fully

• open
• online
• ontologically integrated

If realized, could initiate a paradigm shift for social good, fundamentally changing the way that stakeholders collaborate and share knowledge.