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Licens.io Responsible Data Science Policy

Modular building blocks for a responsible, data science policy that can be mix and matched into a "made-to-measure" policy that suits an organization's needs.

Tool Category:

Documents

Summary

The Licens.io Data Science Framework is comprised of modular sub-procedures or textual building blocks that can be combined to tailor a data science use policy that is best-suited to the needs of a given company and specific regulatory regimes like CCPA, GDPR or SOC2/TSC. The text can be downloaded via Licens.io's GitHub repository or directly from their website.

Thanks to this approach, the framework is flexible and adaptable. That is, it can both cover a wide range of organizations and it can change with those organizations over time. In order to accomplish this, the policy is designed around a parent procedure that routes specific use cases or projects through two types of sub-procedures. Prescriptive sub-procedures give data science teams guardrails and a path for low-friction compliance. Adjudicative sub-procedures centralize decision-making with an individual or group like a risk committee. Organizations can mix and match prescriptive and adjudicative sub-procedures to meet their needs.

The framework contains the following files:

  1. Help and Reference Material
  2. Design and Implementation Guide
  3. Responsible Data Science Policy and Procedure Templates
    1. Responsible Data Science Policy
    2. Concepts and Techniques Inventory
    3. Parent Procedure
    4. Adjudicative Sub-Procedure Template
    5. Prescriptive Sub-Procedure Template
  4. Sample Artifacts
    1. Data Science Proposal Form
    2. Data Science Review Form
    3. Data Science Release Form
    4. Proposal Review Log
    5. Policy Exception Log
  5. Diagrams
    1. Responsible Data Science Policy – Conceptual Design (PNG, SVG)
    2. Responsible Data Science Policy – Example Procedure Flow (PNG, SVG)

Stats

Open Source:Yes
Paid Support:No
API:N/A

License(s)

CC-BY

Tech Stack

MarkdownDocx

Detailed Review

According to Licens.io:

FICO recently surveyed over 100 of the world’s largest, most sophisticated organizations; not even half of them reported having governance procedures related to “AI ethics” in place. When you realize that every single one of these organizations has a C-suite role dedicated to data, like a Chief Data Officer or Chief Analytics Officer, this statistic is even more remarkable.

Meanwhile, regulators and investors around the world are increasingly vocal and explicit about what they expect – and, in some cases, require – from organizations. Lawmakers and regulators, most notably in the US and EU, are drafting or enacting rules that will put in place new standards on data science. Not to be left out, investors wielding over $35T in assets are now including social and governance (ESG) considerations related to data science in their funding criteria. So, what’s an organization supposed to do? If even mature organizations have been slow to integrate data science into risk management and compliance frameworks, how are smaller or less mature organizations supposed to keep up? How can they ensure that their data science activities are conducted responsibly from a technical, legal, and ethical perspective? This data science compliance challenge is what motivated us to build a Responsible Data Science Policy framework – and what led us to open-source it. We want to help all organizations on their journey to build a better business, and, in turn, a better society.

The Licens.io Data Science Framework is comprised of modular sub-procedures or textual building blocks that can be combined to tailor a data science use policy that is best-suited to the needs of a given company and specific regulatory regimes like CCPA, GDPR or SOC2/TSC. The text can be downloaded via Licens.io's GitHub repository or directly from their website.