Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

Data Carbon Scorecard



Data Carbon Scorecard

Purpose: The tool is an enhanced iteration of the Data Carbon Ladder, designed to facilitate insightful discussions surrounding the conceptualization of new data projects and their potential environmental ramifications.

How to use it: The first step is to identify the specific project you wish to undertake, such as assessing customer satisfaction. Next, carefully consider each of the nine questions and critically evaluate your answers. For instance, reflect on whether you truly require all of the available data sets, the necessity of real-time data, optimal data storage options, and the minimum analytical requirements necessary to generate a report that offers sufficient information for decision-making purposes.

Who should use it: The tool caters to all stakeholders engaged in the early stages of project initiation, ranging from the problem owner to the dedicated team responsible for constructing the final report.

Where should I start: Access the complimentary data carbon scorecard and begin by addressing each of the 9 questions. Based on your responses, each question will receive a red, amber, or green score. Keep track of these scores and once you have completed all 9 questions, enter them in the table provided. Then, utilize the straightforward calculation outlined in the document to obtain an overall score for your project.

  • Green – has some impact on the environment but the project is okay to proceed
  • Amber – has a medium CO2 impact and although the project can proceed, steps to identify greener alternatives should be considered.
  • Red – has a large CO2 footprint and questions should be asked to whether a greener dataset can be found or if the problem statement can be altered e.g. is real-time analytics required.

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.