5.20 Establish responsible practices around AI and emerging or disruptive technologies
Business Strategy And Product Management
Implement policies, training, and auditing practices for AI and emerging or disruptive to ensure sustainability upon implementation, while accounting for associated legal issues.
Criteria
- AI and data collection:
Ensure all technologies that deploy or create large datasets use data that is appropriately scaled and stored, ethically sourced, screened, validated, and implemented in a non-discriminatory, responsible manner.- AI transparency framework
- All that glitters?! Intersectional Perspectives on AI
- Build AI sustainably
- Critical Thinking during the age of AI
- Ethical Foundations in Modern Software Development
- Frugal AI
- Frugal AI Challenge
- Frugal Machine Learning
- The human-centered AI manifesto
- LLMs Can Get Brain Rot!
- Misinformation by Omission: The Need for More Environmental Transparency in AI (PDF)
- Our contribution to a global environmental standard for AI
- The Wholegrain guide to ethical use of AI
- Thinking about using AI?
- United Nations SDGS – Goal 16 – Sustainable Society
- United Nations SDGS – Goal 17 – Global Partnership
- Use AI ethically and sustainably
- Business adaptation:
Show how members of your organization are supported in the process of adapting to the rise of new technologies that could disrupt the organization’s business model or operational norms.- AI Overviews Reduce Clicks by 34.5%
- Digital education: The unique learning ecosystem TechUcation
- Global Workforce Hopes and Fears Survey 2024
- The green transition requires an upskilled workforce. Here’s why
- Large Language Models, Small Labor Market Effects
- Turn off AI features by default (to reduce their climate impact)
- United Nations SDGS – Goal 1 – Poverty
- United Nations SDGS – Goal 10 – Inequality
- United Nations SDGS – Goal 16 – Sustainable Society
- United Nations SDGS – Goal 17 – Global Partnership
- Within Bounds: Limiting AI’s environmental impact
- Why We Need To Be UpSkilling The Current Workforce For The Green Economy
- Environmental responsibilities:
Audit and account for any environmental considerations associated with the promotion or adoption of AI or any emerging or disruptive technologies. This should include third-party choices, and the associated waste or emissions per use and those incurred as a consequence of deployment.- 3rd Global CryptoAsset Benchmarking Study (PDF)
- A Computer Scientist Breaks Down Generative AI’s Hefty Carbon Footprint
- A sustainable internet: Missing pieces to a healthy future
- AI and crypto mining are driving up data centers’ energy use
- AI could account for nearly half of datacentre power usage by ‘end of year’
- AI, data centers, and water
- AI emissions: What we know so far – and more importantly, what we don’t know
- AI Energy Score
- AI Environmental Equity
- AI has an environmental problem
- AI Is Fueling a Data-Center Energy Crisis. A New Architecture Can Ease the Pressure.
- AI is set to drive surging electricity demand from data centres while offering the potential to transform how the energy sector works
- AI power demand rapidly escalating
- AI Will Spew Gas Fumes for Years Before the Nuclear Revolution Takes Off
- AI’s Climate Impact Goes beyond Its Emissions
- AI’s Environmental Impact: Making an Informed Choice
- Are harvest now, decrypt later cyberattacks actually happening?
- Beyond Counting Carbon: AI Environmental Assessments Struggle to Inform Net Impact Decisions
- Big tech’s selective disclosure masks AI’s real climate impact
- Bitcoin Energy Consumption Index
- Carbon Emissions from AI and Crypto Are Surging and Tax Policy Can Help
- Carbon in Motion: Characterizing Open-Sora on the Sustainability of Generative AI for Video Generation (PDF)
- Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
- ChatGPT energy usage is 0.34 Wh per query
- Crypto and blockchain must accept they have a problem, then lead in sustainability
- Cryptocurrency’s Dirty Secret: Energy Consumption
- Data center energy and AI in 2025
- Datacenters to emit 3x more carbon dioxide because of generative AI
- Designing sustainable AI
- Digital aspects and the environment
- Dismantling the Quantum Threat
- EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training (PDF)
- From Efficiency Gains to Rebound Effects (PDF)
- Ecological Awareness for the Decentralized Web
- Energy and AI (PDF)
- The Energy and Environmental Footprint of AI (PDF)
- Evaluating the Energy-Efficiency of the Code Generated by LLMs (PDF)
- Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA
- Generating AI Images Uses as Much Energy as Charging Your Phone, Study Finds
- Generative AI is a climate disaster
- Generative AI’s environmental costs are soaring — and mostly secret
- Google’s still not giving us the full picture on AI energy use
- The GPT-OSS models are here… and they’re energy-efficient!
- How AI and automation make data centers greener and more sustainable
- How Much Energy Does AI Use? The People Who Know Aren’t Saying
- How much energy does Google’s AI use? We did the math
- How off-grid solar microgrids can power the AI race
- How useful is GPU manufacturer TDP for estimating AI workload energy?
- Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI (PDF)
- Improving Carbon Emissions of Federated Large Language Model Inference through Classification of Task-Specificity (PDF)
- In battle against climate crisis, don’t overlook the blockchain
- Introducing a new AI metric to drive sustainability
- Jevons’ Paradox is good sometimes
- Learning a Data Center Model for Efficient Demand Response (PDF)
- Ledger of Harms
- Let’s talk about AI and end-to-end encryption
- Measure environmental Impact of your AI Implementations
- Measuring the environmental impact of delivering AI at Google Scale (PDF)
- More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU (PDF)
- New Method Forecasts Computation, Energy Costs for Sustainable AI Models
- Offline Energy-Optimal LLM Serving: (PDF)
- Optimize AI Model Training and Inference
- Overestimating AI’s water footprint
- Prioritize Sustainable AI Design
- Refine Architecture and Assess Latest Trend Impacts
- Sustainable Ux in VR (PPT)
- Sustainability of Bitcoin and its Impact on the Environment
- The carbon emissions of writing and illustrating are lower for AI than for humans
- The cyber-consciousness of environmental assessment
- The Environmental Impacts of AI
- The Environmental Impact of ChatGPT
- The growing energy footprint of artificial intelligence
- The Real Story on AI’s Water Use-and How to Tackle It
- The role of artificial intelligence in achieving the Sustainable Development Goals
- Too Hot to Compute: The Water Crisis Behind Southeast Asia’s Data Centre Boom
- Towards Carbon-efficient LLM Life Cycle (PDF)
- Towards Sustainable Large Language Model Serving (PDF)
- Ultra-efficient AI won’t solve data centers’ climate problem. This might
- UK Government urged to promote, prioritise and invest in sustainable AI to become global leader in AI frugality and efficiency
- Understanding the environmental impact of generative AI services (PDF)
- United Nations SDGS – Goal 1 – Poverty
- United Nations SDGS – Goal 10 – Inequality
- United Nations SDGS – Goal 16 – Sustainable Society
- United Nations SDGS – Goal 17 – Global Partnership
- Unveiling Environmental Impacts of Large Language Model Serving (PDF)
- Water use in AI and Data Centres (PDF)
- Watts That Matter (PDF)
- We did the math on AI’s energy footprint. Here’s the story you haven’t heard
- We need to talk more about AI’s environmental impact
- Web3 and Sustainability
- Web3 and sustainability: Benefits and risks
- What is the environmental impact of LLM use on the customer’s side?
- Why Blockchain, NFTs, And Web3 Have A Sustainability Problem
- Automated tooling:
Ensure all automated tooling, scrapers, spiders, bots, artificial intelligence, and other forms of machine-assisted data gathering abides by requests to opt out at the host, server, or website level. Providers must declare themselves as non-human within the user-agent/HTTP header. Providers must also publish impact reports relating to their gathering activities.- A short history of web bots and bot detection techniques
- Adapting to AI: what 6 months of website analytics tells us about the future
- AI crawlers need to be more respectful
- Block the Bots that Feed AI Models by Scraping Your Website
- Blockin bots
- Blocking AI Bots
- Bot traffic: What it is and why you should care about it
- Distribution of bot and human web traffic worldwide from 2014 to 2021
- Go ahead and block AI web crawlers
- How and Why To Prevent Bots From Crawling Your Site
- How to Combat AI Bot Traffic on Your Website
- How to Eliminate Bots From Your Website
- Introducing pay per crawl
- No bots allowed?
- Please stop externalizing your costs directly into my face
- Thousands of creatives join forces to combat AI data scraping
- United Nations SDGS – Goal 16 – Sustainable Society
- United Nations SDGS – Goal 17 – Global Partnership
- Web User Agents
- Quantum resilience:
Do not roll out post-quantum encryption for high-traffic services that do not need resilience against harvest now, decrypt later attacks, where attackers steal encrypted data, anticipating that future quantum computers will be powerful enough to break the encryption and make the data readable at a later date.- China breaks RSA encryption with a quantum computer, threatening global data security
- GPF – General Policy Framework (PDF) – 1.7 – Strategy (Encryption)
- GPF – General Policy Framework (PDF) – 7.4 – Back-End (Consensus Mechanism)
- GPF – General Policy Framework (PDF) – 9.1-7 – Algorithms (Complete Chapter)
- GR491 – 2-6010 – Is sensitive user data secure?
- Mitigating Quantum Threats Beyond PQC
- Post-quantum cryptography
- Post Quantum Cryptography (PQC): You May Already Be Using It!
- What Is the Future of Quantum-Proof Encryption?
Benefits
- Economic
Establishing clear policies related to digital disruption and emerging technologies makes organizations more resilient and better able to pivot quickly, and face less risk from various threats, including legal action. - Operations
Prioritizing ongoing learning and continuous improvement builds stronger teams that can adapt more quickly.
GRI
- Materials: High
- Energy: High
- Water: High
- Emissions: High