AI Battle Workshop
Follow-up

- Resources and Actions -

You participated in an AI Battle workshop with us,

where we exchanged a wealth of information!

This page aims to provide you with further inputs to support your sustainable AI journey.

After a first section covering definitions, key concepts, and overall impacts, the resources are organized around the core challenges of AI — from the environmental one to questions linked to creativity.

The page concludes with an "Actions" section, featuring the action cards discussed during the workshop, designed to help translate reflection into concrete action.

Feel free to revisit this page, as we will update it regularly.

We also welcome any feedback or suggestions you might have!

Definitions, Core AI Concepts & Overall Impacts

The basis for the AI Battle workshop is this whitepaper by Data for Good.
The International standard ISO/IEC 22989:2022 provides formal definitions and terminology for artificial intelligence systems, including scope, outputs, and human-defined objectives.
The latest study by the Green IT Association from 2025 analyses the environmental footprint of digital technologies, including emissions across production, use, and end-of-life.
The paper "The Climate & Sustainability Implications of Generative AI (2024)" published at MIT explores rebound effects, resource consumption, and systemic sustainability risks linked to generative AI.
ArtificialAnalysis.ai is an independent benchmarking platform comparing AI models, performance, and providers.
Learn more about AI in the free online courses provided by "Stifterverband für Künstliche Intelligenz".

Environmental Impacts of AI

The International Energy Agency (IEA) provides an a uthoritative global assessment of current and projected energy demand from AI and data centers, including efficiency scenarios.
The academic paper “Making AI Less Thirsty” analyses the hidden water footprint of AI models and proposes mitigation strategies.
Research by Cornell shows the projected water and energy consumption of AI-driven data center expansion in the US, which you can read about in the article "Environmental Impact of AI Data Center Boom".
Learn more about how cloud infrastructure contributes to water stress and material corrosion in this article by the American Geographical Society.
The research by Alex de Vries-Gao looks on water and energy trade-offs in large-scale AI infrastructure.
The Environmental and Energy Study Institute (EESI) provides a policy-oriented overview of water consumption linked to data centers.
This article by the Lincoln Institute of Land Policy investigates the land and water impacts from data center development.
Mistral's life-cycle analysis of AI systems covers hardware manufacturing, training, inference, energy use, and water consumption.

Societal Impacts of AI

The Future of Jobs Report provided by the World Economic Forum gives a global assessment of skill shifts, automation, and AI-driven changes in employment.
The article “When Using AI Leads to Brain Fry” in Harvard Business Review discusses cognitive overload, supervision fatigue, and productivity limits when managing multiple AI tools.
This survey-based study by BCG explores AI tool usage and productivity saturation effects.
In this experimental study scientists used EEG to measure cognitive engagement of participants when writing with and without AI assistance.
This peer-reviewed research analyses AI’s impact on democratic processes and information ecosystems.

Bias & AI

This publication by the German Federal Office for Information Security is intended to give developers, providers and operators of AI systems a first introduction to the bias topic.
This study by the university of Washington investigates the impact of bias in AI on hiring decisions.
Read more about how instead of increasing the level of transparency and fairness in the hiring process, AI might just continue existing biases - and how to avoid falling into this trap in this article.

Creativity & AI

This BBC article covers GenAI entering the art world and debates around authorship and authenticity.
The paper "Generative AI enhances individual creativity but reduces the collective diversity of novel content" evaluates LLM performance on creativity tests.
Learn more about AI’s role in the 2023 Hollywood labor strikes in this article.
Find out more about copyright in the legal case GEMA vs OpenAI and in this article.
This BBC News report discusses legal and ethical issues related to voice cloning.

Realiability of AI

The German platform "Lernende Systeme" provides explanations on the risks of deepfakes for democracy.
A report of the security industry shows a rise of deepfake-based attacks.

Actions

This consulting report by KPMG how organizations move from aspirational Green IT goals to operational AI sustainability practices.

- I Question the Value Added by a Generative AI Service in Light of Its Impacts - I Ensure That No Existing Solution Already Meets the Need — and I ReQuestion the Need if Necessary - I Adapt the Level of Performance (Quality, Speed) to My Actual Needs - I Choose Transparent AI Models - I Measure Environmental Impact (Before, During, and After) - I Acknowledge the Influence of My Biases and Those of AI Models - I Make Sure I Understand How the AI Model I Use Was Built and Trained - I Ensure That AIPowered Essential Services Run on Existing Equipment - I Adapt Data Processing According to the Level of Data Sensitivity - I Make Sure I Do Not Adopt AI Services Due to Hype or Personal Interest - I Share Good Practices Related to AI - I Exercise Critical Thinking Toward AIGenerated Information - I Understand My Rights Regarding My Personal Data - I Am Transparent About My Use of AI and Explicitly Disclose It - I Acknowledge the Risk of Dependency and Ensure Resilience in a Context of Resource and Energy Constraints - I Recognize Our Limitations in Identifying AIGenerated Content and the Risks of Malicious Use - I Ensure Regulatory Compliance (AI Act, GDPR) and Apply Ethical Judgment to My AI Use

AI Battle Workshop Follow-up