5.22 Promote and implement responsible emerging technology practices
Business Strategy And Product Management
Implement policies, training, and auditing practices for new or emerging technologies to ensure sustainability upon implementation, while accounting for associated legal issues.
Criteria
- Emerging technologies: Machine-testable
Establish public-facing policies for emerging technologies. Ensure all such technologies and their datasets are ethically sourced, screened, validated, and implemented in a non-discriminatory, responsible manner.- AI transparency framework
- Ethical Foundations in Modern Software Development
- Frugal AI
- Frugal AI Challenge
- Frugal Machine Learning
- The human-centered AI manifesto
- 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
- Disruptive technology: Human-testable
Show how workers are trained as new technologies and practices potentially disrupt an organizations business model.- 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
- Technology legislation: Human-testable
Support and comply with responsible legislation related to emerging technologies.- AI Act is Here
- EU AI Act Compliance Checker
- EU Artificial Intelligence Act
- Global AI Law and Policy Tracker
- Top 5 AI Disclaimer Examples To Keep Your Content Compliant
- 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
- Environmental responsibilities: Human-testable
Audit and account for any environmental considerations that may derive from the use of emerging technologies wishing to be promoted or implemented. This should include third-party choices, the expense in terms of waste or emissions of using the technology to create a desired result, and consequences that may arise from its 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 Energy Score
- AI Environmental Equity
- AI has an environmental problem
- AI is set to drive surging electricity demand from data centres while offering the potential to transform how the energy sector works
- 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?
- 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
- 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
- From Efficiency Gains to Rebound Effects (PDF)
- Ecological Awareness for the Decentralized Web
- Energy and AI (PDF)
- 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
- How AI and automation make data centers greener and more sustainable
- 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
- 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
- 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 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)
- 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: Human-testable
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.- 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
- 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
- Quantum resilience: Human-testable
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.- 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