Top AI Ethics Jobs in Boston, MA

Lead enterprise strategy and operating model for information management, data governance, and AI governance. Establish federated governance, metadata, lineage, semantic models, and AI-ready data practices. Partner with legal, privacy, cybersecurity, quality, and business teams to create practical guardrails, intake and risk-tiering processes, monitoring, and executive reporting to enable trusted, ethical, and regulatory-compliant use of data and AI across the organization.

Lead firmwide AI enablement by setting strategy and roadmap, designing multi-level training, running an AI champion community, producing adoption analytics, shaping governance, evaluating tools and vendors, and managing stakeholders and a small team to drive responsible AI adoption and measurable business impact.

The Director of CX AI will develop and execute the AI roadmap for customer experience, evaluate tools, modernize workflows, and ensure responsible AI use.

The Principal TRI AI Governance Specialist manages DTCC's AI governance framework, ensuring compliance and risk management while promoting responsible AI use across the organization.

The Senior AI Risk Advisor will lead AI risk assessments, develop governance frameworks, evaluate AI vendors, and engage with stakeholders on AI adoption.

The AI and Digital Innovation intern will support AI initiative coordination, experimentation, staff training, and documentation of responsible AI practices within Oxfam.

The Head of Data & AI Governance and Risk oversees governance, compliance, and quality of Data and AI capabilities, defining policies, engaging regulators, and ensuring adherence to standards across various business lines.

The AI Enablement Lead oversees AI-enabled client productivity tools at Harvard, ensuring secure access and operational oversight while promoting responsible tool use across the university.

The AI Quality Associate evaluates AI outputs for quality, develops benchmarks and rubrics, validates workflows, and collaborates with finance experts to ensure high-quality AI-assisted outputs.

Support design, implementation, and operation of enterprise AI governance: maintain AI use-case inventory, execute risk intake and tiering, support governance reviews, pre-deployment testing and post-deployment monitoring, maintain risk registers and controls, and embed Responsible AI practices across functions.