What is happening to devops engineers
- Impact
AI tools are autonomously monitoring systems, optimizing deployments, automating routine operations, and enhancing incident resolution. This compels DevOps Engineers to radically pivot towards high-level strategic automation, complex system architecture, ethical AI governance, and fostering irreplaceable human-AI collaboration for highly resilient and efficient systems.
- Risk
Radical role overhaul; pervasive automation leading to significant workflow redefinition and specialized human focus.
The DevOps Engineer role is undergoing a profound and accelerating redefinition by AI. AI will assume command of vast routine monitoring, performance tuning, and much of the administrative burden in infrastructure and CI/CD pipelines. DevOps Engineers must immediately pivot to becoming experts in leveraging AI for hyper-efficiency and enhanced strategic insights, intensely validating AI outputs for accuracy and reliability, and dedicating their expertise to the irreplaceable human elements of the role: profound architectural design for cloud-native systems, nuanced troubleshooting for ambiguous issues, and critical ethical decision-making regarding system security, scalability, and data privacy.
- Sector readiness
Rapid & Transformative Integration
The DevOps and Cloud Native computing sectors are aggressively integrating AI, driven by overwhelming demand for efficiency, scalability, and resilience in modern software delivery. AI is rapidly moving beyond pilot stages to widespread adoption for AIOps, automated CI/CD, and intelligent infrastructure management, fundamentally altering traditional workflows and competitive dynamics.
Where you stand
The DevOps Engineer role is undergoing a profound and accelerating transformation, with AI fundamentally restructuring infrastructure management, CI/CD, and incident response.
AI will autonomously manage vast routine tasks, optimize deployments, and streamline incident resolution, compelling engineers to pivot to indispensable strategic automation and profound architectural design.
Survival and impact will hinge on DevOps Engineers mastering AI tools, critically validating AI outputs for reliability and ethics, championing ethical AI, and providing irreplaceable human judgment at the heart of resilient, secure, and hyper-efficient software delivery.
What this means for you
Concrete changes to how the work gets done, in the order you are likely to meet them.
- 01
AI-Driven Autonomous Monitoring & AIOps. DevOps Engineers will command AI-powered AIOps platforms that autonomously monitor entire IT landscapes – from networks to applications – to predict outages, detect subtle anomalies, and identify root causes in real-time. This radically frees engineers from alert fatigue, demanding verification of AI insights and strategic oversight.
- 02
AI-Optimized CI/CD Pipelines. DevOps Engineers will leverage AI tools that autonomously optimize Continuous Integration/Continuous Delivery (CI/CD) pipelines. AI will predict build failures, optimize test execution order, and automatically roll back faulty deployments, ensuring faster, more reliable, and secure software releases.
- 03
Intelligent Root Cause Analysis (RCA) & Remediation. When an incident occurs, AI tools will autonomously analyze vast amounts of logs, metrics, and traces from distributed systems. The AI will rapidly pinpoint the root cause and even suggest or execute autonomous remediation steps, dramatically reducing Mean Time To Resolution (MTTR).
- 04
Predictive Infrastructure Scaling. DevOps Engineers will utilize AI models that autonomously analyze historical usage patterns, application performance, and anticipated load changes to predict future resource needs. This enables proactive, autonomous scaling of cloud infrastructure to ensure optimal performance and cost-efficiency.
- 05
Generative AI for Infrastructure-as-Code (IaC) & Scripts. AI will autonomously draft initial versions of Infrastructure-as-Code (IaC) templates (e.g., Terraform, CloudFormation), automation scripts, and configuration files. DevOps Engineers will rigorously review and approve these AI outputs for security, efficiency, and adherence to best practices.
- 06
Focus on Strategic Automation & System Resilience. As AI assumes command of routine operational tasks, the paramount value of DevOps Engineers will be their irreplaceable human ability to design complex, highly resilient, and fully automated infrastructure architectures. This shifts focus to ensuring "self-healing" and "self-optimizing" systems.
- 07
AI-Driven Security & Compliance Automation. DevOps Engineers will deploy AI-powered security solutions within CI/CD pipelines that autonomously scan code, containers, and infrastructure for vulnerabilities and compliance deviations. AI will automatically remediate common issues or flag critical ones for human intervention, embedding security from design.
- 08
Ethical AI in Automation & Operational Bias. DevOps Engineers will bear profound responsibility for auditing AI systems for algorithmic bias (e.g., in scheduling deployments, resource allocation), ensuring data privacy in logs, and upholding ethical standards for fair and equitable system automation.
- 09
Human-AI Teaming for Incident Management. DevOps Engineers will operate in seamless human-AI teams during incidents. AI will provide real-time diagnostic insights, generate possible solutions, and perform autonomous containment. The human engineer will lead complex troubleshooting, apply nuanced judgment, and make critical decisions for recovery.
- 10
AI for Cloud Cost Optimization. AI will autonomously analyze cloud spend across multiple services and accounts, identifying underutilized resources, cost inefficiencies, and suggesting optimal pricing models. DevOps Engineers will leverage these insights to aggressively optimize cloud expenditure.
- 11
Continuous Learning & Cloud-Native AI Literacy. The exponential pace of AI integration in DevOps and Cloud Native computing demands that DevOps Engineers commit to continuous, aggressive learning of new AI-powered tools, advanced cloud services, and their profound capabilities and ethical implications, as a foundational competency.
- 12
Specialization in MLOps (Machine Learning Operations). The field will see a significant rise in DevOps Engineers specializing in MLOps, focusing on designing, implementing, and managing robust, scalable CI/CD pipelines specifically for machine learning models, ensuring their continuous integration, delivery, and monitoring.
- 13
AI-Powered Performance Optimization for Applications. DevOps Engineers will collaborate with AI tools that autonomously analyze application performance in real-time, identifying bottlenecks at the infrastructure, code, or database layers. AI will suggest and potentially implement optimizations to improve application responsiveness.
- 14
Leadership in Platform Engineering & Developer Experience. DevOps Engineers in leadership roles will play a crucial role in guiding their organizations through the pervasive adoption of AI, advocating for highly automated platform engineering, and fundamentally reshaping the future of developer experience and software delivery.
- 15
Strategic Alignment of Infrastructure with Business Goals. As AI streamlines operational tasks, DevOps Engineers will dedicate more time to high-level strategic infrastructure planning, designing resilient and scalable systems that directly align with and contribute to the organization's overarching business strategy.
What is pushing this change
- 01
Explosive Growth of Observability Data (Logs, Metrics, Traces). Modern cloud-native and microservices architectures generate petabytes of telemetry data, overwhelming manual analysis and demanding AI.
- 02
Unprecedented Complexity of Cloud-Native Architectures. Intricate, distributed systems across multi-cloud and hybrid environments make manual management untenable, forcing pervasive AI adoption.
- 03
Urgent Demand for Hyper-Speed Software Delivery. Businesses require continuous delivery of high-quality software at unprecedented speeds, demanding AI-optimized pipelines.
- 04
Relentless Pressure for Reliability & Availability. Users and businesses demand 24/7, near-zero downtime for applications; AI predicts and prevents outages autonomously.
- 05
Critical Shortage of Highly Skilled DevOps Talent. The severe global shortage of DevOps and SRE (Site Reliability Engineering) professionals compels aggressive AI adoption to augment human capacity.
- 06
Pervasive Cyber Threats & Need for Automated Security. AI-powered attacks necessitate more sophisticated, AI-driven defenses and automated incident response in CI/CD.
- 07
Exponential Growth of AI/ML Workloads (MLOps). The rapid development and deployment of AI/ML models demand highly automated and scalable MLOps pipelines.
- 08
Demand for Aggressive Cost Optimization. Cloud computing costs are rising; AI optimizes resource utilization and spend across complex cloud environments.
- 09
Global Competition in Software Innovation. AI is a critical enabler for companies to gain a competitive edge in software innovation and delivery speed.
- 10
Focus on Developer Experience & Productivity. Automating routine tasks and optimizing workflows with AI radically improves developer productivity and satisfaction.
Impact by sector
The headline figure is an average. Where you work changes the picture.
- Site Reliability Engineers (SREs)
AI for autonomous observability, predictive incident management, and automated remediation. Focus on system reliability and uptime.
- Cloud Engineers
AI for autonomous cloud resource provisioning, cost optimization, and multi-cloud management. Focus on cloud efficiency and scalability.
- Automation Engineers
AI for autonomous infrastructure automation, CI/CD pipeline orchestration, and policy enforcement. Focus on automated delivery and compliance.
- Security Engineers (DevSecOps)
AI for autonomous vulnerability scanning, threat modeling in pipelines, and automated security policy enforcement. Focus on continuous security.
- Platform Engineers
AI for designing, building, and maintaining internal platforms that enable developers to deploy code autonomously. Focus on developer experience and efficiency.
Skills to build
The skills that keep the human part of this work valuable as the routine part is automated.
- 01
AIOps & Observability Mastery. Profound understanding and practical mastery of AI-powered operational platforms (AIOps) and comprehensive observability stacks.
- 02
Cloud-Native Architecture & Principles. Expertise in designing, deploying, and managing applications on cloud platforms (AWS, Azure, GCP) using microservices and containerization.
- 03
Automation Scripting & IaC. Mastery of scripting languages (e.g., Python, Bash) and Infrastructure-as-Code (IaC) tools (e.g., Terraform, Ansible) for autonomous automation.
- 04
CI/CD Pipeline Design & Optimization. Deep knowledge of designing, implementing, and optimizing continuous integration and continuous delivery pipelines for rapid software deployment.
- 05
Ethical AI & Operational Bias. Profound understanding and application of ethical AI principles in automation, ensuring algorithmic transparency, mitigating biases, and rigorously protecting data privacy.
- 06
Problem-Solving & Root Cause Analysis (Distributed Systems). The ability to rapidly diagnose and resolve complex issues in highly distributed, cloud-native systems, leveraging AI for hyper-fast root cause analysis.
- 07
DevSecOps & Security Automation. Expertise in embedding security best practices throughout the entire software development lifecycle (SDLC) using AI-powered tools and automation.
- 08
Adaptability & Continuous Learning. A relentless commitment to continuously learning new AI technologies, adapting DevOps methodologies, and radically transforming software delivery processes.
Tools in use
Kinds of tool worth knowing
- 01
AIOps Platforms. Platforms that use AI/ML to autonomously automate IT operations, detect anomalies, predict outages, and perform root cause analysis.
- 02
Cloud Management Platforms (CMP) with AI. Platforms that provide centralized management and autonomous optimization of cloud resources, leveraging AI for aggressive cost control, security, and compliance.
- 03
Security Orchestration, Automation, & Response (SOAR). Software that autonomously automates security workflows, orchestrates security tools, and executes incident response playbooks with minimal human intervention.
- 04
Generative AI for IaC & Scripts. Large Language Models (LLMs) used to autonomously draft initial versions of Infrastructure-as-Code templates, automation scripts, and configuration files.
- 05
AI-Powered CI/CD Tools. CI/CD platforms that integrate AI for autonomous build optimization, test orchestration, and intelligent deployment decisions.
- 06
AI for Observability & Distributed Tracing. Tools that leverage AI to analyze performance data and traces across distributed systems for anomaly detection and root cause analysis.
Named tools already in use
Datadog (AIOps, DBM)
VisitLeading observability platforms that leverage AI for anomaly detection, root cause analysis, and performance optimization across distributed systems.
Palo Alto Networks Prisma Cloud
VisitLeading cloud-native security platforms that leverage AI for continuous, autonomous monitoring, radical risk assessment, and compliance enforcement across cloud environments.
Cortex XSOAR
VisitComprehensive platforms that autonomously automate and orchestrate security workflows, enabling hyper-fast and consistent incident response with minimal human oversight.
ChatGPT / Google Gemini (for IaC/scripts)
VisitGenerative AI models that can autonomously draft complex IaC templates, automation scripts, and configuration files for rigorous human review.
GitLab (AI features) / CircleCI (AI insights)
VisitCI/CD platforms that are integrating AI for autonomous build optimization, test orchestration, and intelligent deployment decisions.
Honeycomb (Observability) / Lightstep (Observability)
VisitObservability platforms that use AI for real-time analysis of traces and metrics from distributed systems, enabling faster troubleshooting and performance optimization.
In practice
Ways people in this role are already using AI, and what they get from it.
- Automate Incident Root Cause AnalysisExample 1
- How
DevOps Engineers will deploy an AI-powered AIOps platform. The AI will autonomously analyze vast logs, metrics, and traces from distributed systems, rapidly pinpointing the root cause of an incident (e.g., microservice dependency failure, database bottleneck) and suggesting remediation steps.
GainDrastically reduces Mean Time To Resolution (MTTR), ensures faster incident recovery, and frees engineers for proactive system design.
- Optimize CI/CD Pipeline PerformanceExample 2
- How
DevOps Engineers will leverage an AI tool integrated into their CI/CD pipeline. The AI will autonomously analyze code changes, predict build failures, optimize test execution order, and automatically roll back faulty deployments to ensure fast, reliable releases.
GainAccelerates software delivery, improves release reliability, and reduces manual intervention in CI/CD pipelines.
- Predict Infrastructure FailuresExample 3
- How
DevOps Engineers will utilize an AI model that autonomously analyzes historical server performance, network traffic, and resource utilization. The AI will predict potential infrastructure failures (e.g., disk full, CPU overload) hours or days in advance, triggering autonomous scaling or maintenance.
GainMinimizes costly downtime, prevents service disruptions, and shifts IT operations from reactive firefighting to proactive, predictive management.
- Generate Infrastructure-as-CodeExample 4
- How
DevOps Engineers can instruct a generative AI tool to draft Infrastructure-as-Code (IaC) templates (e.g., for a new Kubernetes cluster configuration) based on desired architecture and cloud provider. The AI will autonomously generate the code for review.
GainSignificantly reduces manual IaC authoring time, ensures consistent and error-free infrastructure definitions, and accelerates environment provisioning.
- Automate Cloud Cost OptimizationExample 5
- How
DevOps Engineers will implement an AI-powered cloud cost optimization platform. The AI will autonomously analyze cloud spend across multiple services and accounts, identifying underutilized resources (e.g., idle VMs, unattached storage) and automatically recommending or applying rightsizing and deletion.
GainAchieves unprecedented cloud cost savings, optimizes resource utilization, and ensures cloud architectures are maximally efficient and adaptable.
How this role compares
Three neighbouring roles chosen to show the direction of travel, then the roles either side of yours on the exposure scale.
- System Administrators (Routine maintenance, monitoring) / QA Engineers (Regression testing)More exposed
- AI impact
Catastrophic (AI/RPA can autonomously perform routine maintenance; AI can autonomously execute regression tests.)
Work moves toImmediate need for radical re-skilling into AI oversight, troubleshooting complex infrastructure, or specializing in cloud-native architecture.
- Site Reliability Engineers (SREs) / MLOps EngineersDifferent skills, growing
- AI impact
Foundational (They define and build the AI-driven infrastructure and pipelines that DevOps engineers implement.)
Work moves toDeep expertise in AI/ML, distributed systems, software engineering for reliability, and building scalable MLOps platforms.
- Product Managers (Platform/Dev Tools) / Solution Architects (Enterprise)Complementary, less exposed · exposure 45
- AI impact
Low-Moderate Augmentation (AI assists in market research for PMs; AI helps with design patterns for architects), but core product vision, user empathy, and high-level architectural strategy remain paramount.
Work moves toDefining product vision, user needs, and strategic roadmap (Product Managers); Designing enterprise-wide systems and technology strategy (Solution Architects).
- 552–5 yrs
- 552–5 yrs
- 551–6 yrs
DevOps Engineers · this report
552–5 yrs- 601–4 yrs
- 602–5 yrs
Corporate Development Managers
602–5 yrs
Closing judgement
For DevOps Engineers, AI is not merely a tool but a radical force of transformation that will fundamentally redefine their role. It will autonomously handle the mundane, amplify automation capabilities, and streamline operations, compelling engineers to pivot to indispensable strategic architectural design, profound problem-solving, and ethical oversight. The future DevOps Engineer will be a visionary orchestrator of human-AI collaboration, providing irreplaceable judgment at the heart of resilient and hyper-efficient software delivery.
Evidence and revisions
What the exposure figure rests on, what changed when it was last revised, and the published work cited for this role.
50 → 55
Window2-5 years (unchanged)
The 4 October 2026 review moved the score up by 5 points.
Microsoft's AI applicability score for the matching occupations is 0.27, in the top quarter of 785 US occupations; Anthropic's observed exposure (the share of the occupation's tasks already being done with Claude) is 0.31, which is heavy by the standards of the 756 occupations measured; the US Bureau of Labor Statistics places it in the 'very high' AI-exposure tier; BLS projects employment to grow 3.0% over 2025–35. Blending our 2025 editorial figure (60%) with the 2026 evidence composite (40%) moves the score from 50 to 55.
US Bureau of Labor Statistics · Employment Projections 2025–35 and AI Exposure Categories
Official statistics · 27 August 2026AI-exposure tier: Very high. Projected employment change 2025–35: +3.0%. Matched to Network and computer systems administrators; Software developers.
Microsoft Research · Working with AI: Measuring the Applicability of Generative AI to Occupations
Working paper · 10 July 2025AI applicability score 0.27 (percentile 84 of 785 occupations) for SOC 15-1252, 15-1244.
Anthropic · Anthropic Economic Index report: Cadences
Report · 26 June 2026Observed exposure 0.31 for SOC 15-1252, 15-1244 (percentile 92 of 756 occupations).
Stanford Digital Economy Lab · Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence
Working paper · 12 August 2026Software development is one of the two occupations where the paper finds the clearest early-career hiring decline; experienced developers show no comparable gap.
World Economic Forum · The Future of Jobs Report 2025
Report · 7 January 2025Software and applications developers and AI/ML specialists sit on the WEF fastest-growing list: exposure here reads as transformation and demand, not decline.
UK Department for Science, Innovation and Technology · Assessment of AI capabilities and the impact on the UK labour market
Report · 28 January 2026UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings.
Anthropic · Anthropic Economic Index report: Learning curves
Report · 24 March 2026Coding tasks are migrating into automated API workflows where directive (delegated) use dominates, which raises real-world exposure beyond what chat-based usage shows.
Indeed Hiring Lab · AI at Work Report 2025: How GenAI is Rewiring the DNA of Jobs
Report · 23 September 2025Indeed rates software development the most exposed occupation (81% of typical skills hybrid), yet its 2026 follow-up finds software postings up almost 15% since early 2025, concentrated in senior and AI-titled roles.
Archived copies are served only where the licence permits; otherwise the link goes to the publisher. Full research library →
Readers' view
What people who do this work make of our reading: their own scores, their reasons, and the notes they left on each section.
Our report is one reading of the evidence. This section is the other dataset: what people who do or know this work make of it. Nobody has scored this role yet. Sign in to add yours.
—
—
55
No one has explained their score yet. A line or two about what you see in your own work is the most useful thing on this page.
No notes yet. Every section above has a “Readers' notes” line at the bottom; open one and say what you know.
Method and sources
Each report was written from a large body of published research and then, in October 2026, re-scored against occupation-level evidence: the US Bureau of Labor Statistics AI-exposure classification and 2025–35 projections, Microsoft Research’s AI applicability scores and Anthropic’s observed-exposure data, cross-checked against the reports listed in the Evidence section above. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.
Scores are revised by blending the previous editorial figure (60%) with a composite of the three occupation-level measures (40%), capped at fifteen points per revision and rounded to the nearest five. The window shifts one notch when a score moves ten points or more. Hand adjustments are recorded with their reason in the revision log.
Research library: every source, with dates, licences and archived copies →
- World Economic Forum
- The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).
- AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
- McKinsey Global Institute
- AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).
- Industry-specific reports — Financial services, healthcare, manufacturing and others.
- PwC
- Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).
- Upskilling Hopes and Fears survey — Employee perceptions and readiness.
- Microsoft Research and Anthropic
- Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
- Stanford Digital Economy Lab and Stanford HAI
- Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
- Deloitte
- Human Capital Trends series — Workforce, talent and HR technology trends.
- Tech Trends series — Emerging technologies and their business implications.
- Accenture
- Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.
- Fjord Trends — Design, innovation and human experience in a digital world.
- Boston Consulting Group
- AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
- EY
- AI and workforce reports — Adoption, talent strategy and ethics.
- IBM Institute for Business Value
- AI and automation studies — Business models, workforce evolution and leadership.
- OECD
- AI Policy Observatory — International data and policy on AI, labour markets and skills.
- Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
- International Labour Organization
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
- International Monetary Fund
- Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
- UK Department for Science, Innovation and Technology
- Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
- Brookings Institution
- AI and automation research — Economic and social implications, displacement and skills.
- Yale Budget Lab and Goldman Sachs Research
- Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
- Oxford University (Oxford Martin School)
- The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
- MIT Technology Review
- AI & Work — Reporting on AI research and its implications for industries and jobs.
- Gartner
- Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.
- Future of Work reports — Workplace models and talent strategy.
- U.S. Bureau of Labor Statistics
- Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
- Indeed Hiring Lab
- AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.
- OpenAI
- Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
- Google DeepMind
- Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
- Meta AI
- Research papers and blog — Large language models, computer vision, AI for social good.
- Hugging Face
- Transformers library and model hub — Open-source state-of-the-art NLP models.
- TensorFlow and PyTorch
- Documentation and community forums — Core frameworks illustrating practical capability.
- arXiv
- cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
- NeurIPS and ICML
- Conference proceedings — Top-tier academic research.
- ACM and IEEE
- Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
- Kaggle
- Datasets and competition solutions — Applied machine learning on real-world problems.
- The Alan Turing Institute
- Research and reports — Responsible and applied AI.
- NIST
- AI Risk Management Framework — Voluntary framework for managing AI risk.
- European Commission
- AI Act — Risk-tiered legal framework for AI.
- Ethics Guidelines for Trustworthy AI — Principles for responsible development.
- Partnership on AI
- Research and best practice — Responsible AI development.
- AI Now Institute
- Annual reports — Social implications: power, inequality, rights.
- ACM FAccT
- Proceedings — Fairness, accountability and transparency.
- Data & Society
- Publications — Social implications of data-centric technology.
- WIPO
- Conversation on IP and AI — Intellectual-property implications of AI.
- IEEE Global Initiative on Ethics of A/IS
- Ethically Aligned Design — Recommendations for ethical AI design.
- Center for AI and Digital Policy
- Policy briefs — Accountable AI policy.