WhiteBox

WhiteBox · Impact & Risk Intelligence

A.I. for Impact

Identifying environmental and social risks to mitigate and discovering opportunities for positive impact.

WhiteBox is a transparent and accountable data and machine learning framework for identifying opportunities for positive environmental and social impact while measuring and managing risk. It is an R&D platform, and since 2016 it has built data and artificial intelligence (A.I.) approaches to that one problem.

Environmental, social, governance and financial dimensions are exceedingly complex on their own, and more so together. Multiply that by diverse stakeholders and biodiversity in an ecosystem and the complexity grows exponentially. WhiteBox parses that complexity with deep learning approaches, leveraging data frameworks and multilayered data sources, and provides transformations into actionable insight and sustainable ecosystems.

Build a Whitebox

A bounded volume of space and time holding a measured, inspectable record of impact and risk.

The four dimensions of a whitebox. Environmental, social and governance factors are the main factors deciding a company’s or an investment’s sustainability footprint and subsequent risks.

The box is white because it is open: every value carries its source, method and time of recording, every model shows which inputs drove its output, and the people described can look in, check and reply.

A boundary in space, drawn through time.
Four measures inside, each a long list of numbers.

The whitebox, explained in full

Open the box

Every whitebox holds four stakeholder pillars, each asking something different of the record.

  1. Finance

    Have a clear picture of environmental, social and governance risks to protect and create value.

  2. Environmental

    Understand the changing dynamics of the environment for advanced stewardship.

  3. Governance

    Anticipate and align with regulatory frameworks and optimise good governance strategies.

  4. Social

    Account for complex social forces for healthy and vibrant communities.

Each pillar explained.

It begins with the people affected

The people who live with impact and risk see them first, so they are the first layer of the record.

A community knows the water has changed before a satellite does; a workforce knows a site is unsafe before an auditor arrives. Every whitebox therefore begins with engagement: what the stakeholders a project touches know, and what they say back as things change. Public and private datasets, disclosures, news, satellite observation, sensors and market data come after, each checked against what the people on the ground said and never in place of it.

The sources a whitebox is built from

Deep Learning

WhiteBox leverages machine learning to discover insight from big data to assess environmental, social and governance impact and risk.

The data in a whitebox is large, mixed and always changing. No person can read all of it; machine learning can. The models read across the dimensions at once: the risk building in one place from signals in another, the direction a situation will take, the anomaly that means fraud or waste, the opportunity for positive impact that nobody has priced. Two rules hold. A model must show its working, since a black box inside a whitebox defeats the purpose, and its output is a reading to be checked by the people it describes, not a verdict.

Monitoring and reporting: how the A.I. is put to work

Why it matters

The programme’s founding finding is a cycle: positive impact within a stakeholder ecosystem lowers risk, and a risk found and mitigated raises positive impact. A whitebox is built to find that cycle in the data.

The cycle is hard to see because its halves live in different reports: a benefit to a community is a social reading, the permit it protects a governance one, the delay it prevents a financial one. Held together through time, the four dimensions let a model trace an impact to the risk it lowers, and a mitigated risk to the impact it raises.

Most sustainability reporting is a rating assigned once a year, from outside, after the fact. A whitebox reads from inside and in time: impact is measured where it lands, a risk to the environment shows as the social, governance and financial risk it also is, and a change is caught while it is still a trend. A company can manage impact and risk instead of reporting on them afterwards, an investor sees a project as it is, and a community can hold a project to its word with the project’s own record.

The journey

The work runs from a Toronto consultancy in 2010, to an R&D platform in 2016, to an operating system for land in 2021.

  1. 2010Flynn CoteImpact and risk between stakeholders
  2. 2016WhiteBoxThe R&D platform
  3. 2021Druid OSThe middle layer between nature and markets, by creating WhiteBoxes around farms and forests
  4. 2026AwendilThe company that builds and runs it
  1. 2010

    Flynn Cote

    Flynn Cote Brokerage House is founded in Toronto to work on impact and risk between the parties to a project. Ten years of that work produced the question WhiteBox was built to answer.

  2. 2016

    WhiteBox

    The R&D platform begins, bringing together subject-matter experts, engineers and academics. It works in partnership with IBM, is listed as a start-up by York University’s Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) programme, and sets down the four findings that still hold. The programme, in detail

  3. 2021

    Druid

    The box is drawn around a piece of farmland instead of a company. Every field gets a whitebox: what the land produces, read from public satellites; who may see it, decided by the farmer; every forecast graded against what the land then did. druid.green

  4. Now

    The think tank, and Druid OS

    WhiteBox continues as a think tank, exploring the whitebox across the environmental, social, governance and financial dimensions and handing what works to Druid OS, the operating system that runs it on land. Awendil, the company being formed to build and operate Druid OS, carries the work forward.

The people behind the programme

Flynn Cote Brokerage House brought together people in research, engineering, product, design and advisory roles.

  • Stephane Flynn CoteDirector
  • Jeffrey YipProject Coordinator
  • Jory GoldsteinProduct Manager
  • Marc DelormeDevelopment Manager
  • Tobin AnsongRisk Advisor
  • Oluseun ObikoyaImpact Advisor
  • Hassan MasumSystems & Strategy Advisor
  • Michaela CoteDesign
  • IBM
  • York University
  • IBMTechnology partner
  • YodiStakeholder A.I., now the first app of Druid
  • Sipher WhiteboxA.I. and distributed ledger building blocks
  • FCBH Inc.Flynn Cote Brokerage House Inc., which founded and runs the programme
  • York University, ADERSIMListed as a start-up company by the Advanced Disaster, Emergency and Rapid Response Simulation programme

Where it leads: Druid OS

Druid OS is the whitebox at work on land. One record per field holds what the land produces, read from public satellites every ten days, who may see it, decided by the farmer, and every forecast graded against what the land then did. The box is the same idea. The thing inside it changed.

Druid OS, at druid.green

Together we can improve the resilience and sustainability of our companies, ecosystems, communities, and the institutions that govern them. We welcome partnerships and collaborations with the private sector, government, non-governmental organisations and academia: hello@whiteboxplatform.com.