CSR and Artificial Intelligence

CSR and Artificial Intelligence

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Artificial intelligence (AI) will change the economy and our lives. AI is on the “hype cycle” just before the breakthrough, and can be used increasingly profitable. Already several years ago, the DFGE began to explore the possibilities of machine learning, especially with neural networks.

With technology development and the power of IT-driven big data and data science processing, DFGE provides its customers a range of solutions for developing and optimizing enterprise-wide Corporate Social Responsibility (CSR) based on Artificial Intelligence.

“We have been dealing with fact-based corporate sustainability for a long time. Therefore, it makes sense to try out new methods here. Imagine the opportunities that arise when you collect a large amount of different CSR data in a Big Data approach, evaluate them with Machine Learning, and gain new insights for your CSR management!”
T. Fleissner
Dr.-Ing. Thomas Fleissner, Founder and CEODFGE

Further Information

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Business Cases of DFGEs AI-Solutions (Artificial Intelligence)

Special about the use of artificial intelligence are the numerous possible applications. You will find only a small selection here, as the questions as well as the results of the DFGE AI concept are always determined individually for your company. The DFGE Artificial Intelligence solutions will always help you to optimize your CSR strategy and improve your sustainability performance.

Improve CSR in your business

  • Reviewing your CSR data will increase the resilience of your CSR statements
  • Carbon Footprint Benchmarking
  • Carbon Footprint-Calculation
  • Competitor- und Market-Benchmarks
Expansion of your CSR activities:

  • SDG (Sustainable Development Goals) Matching. Gap analysis of your data and your SDG commitment
  • Scope 3-Calculation (Corporate and Product Carbon Footprint)
  • Applying AI insights to other CSR areas along your value chain, which have not yet been considered yet

Our approach to artificial intelligence in CSR

Data collection (Data)

In the CSR (Corporate Social Responsibility) three-pillar model, economy, ecology and social aspects are equal and equally important, both at the macroeconomic and political levels, and at global and entrepreneurial levels. In companies, vast amounts of data are generated in all these areas. These own data as well as validated external data of third parties and also other data, which are (at least at first glance) not directly related to CSR, are collected in the AI concept of DFGE (R).

Data collection

  • Own company-wide data
  • Databases of DFGE
  • Competitor / Market Data
  • External validated databases
  • Further data, also from non-thematic areas
AI-Concepts of DFGE (R):

  • Expertise in the selection of usable data
  • Validation of internal and external data
  • Own database of DFGE from carbon footprint and CSR projects over the last 20 years

Machine Learning (Processing)

This step requires a sound understanding of how machine learning works and what methods are appropriate for solving a specific problem. After selecting suitable models, they are further optimized in an iterative process until the result satisfies the quality criteria defined before for the application.

Processing

  • Mathematical regression
  • Selection of an appropriate AI algorithm for the problem
  • Training the AI model
  • Iterative improvements of the model hyperparameters
AI Concept of DFGE (R):

  • Many years of experience in regression analysis
  • Broad portfolio of AI algorithms

Results and Actions (Action)

The Artificial Intelligence concept of the DFGE is a hybrid and sequential approach. Therefore, this last step is not just about presenting results, but also about identifying and implementing measures to improve CSR across all three pillars of sustainability.

Action

  • Quantified results
  • Identification of strengths and weaknesses in the company and / or market
  • Derivation of measures: “Action” instead of “Results”
AI-Concepts of DFGE (R):

  • Derivation of measures from the results
  • Methodological extension of DFGE Sustainability Intelligence
  • Support and advice regarding implementation
More about Artificial Intelligence

Why artificial intelligence?

  • Keep a clear view even with large data sets (big data)
  • Detection of new patterns and derivation of actions
  • Connection of very different data sources