Top AI Ethics Jobs in Chicago, IL

Co-lead a new 10-week AI safety replication program: select replication targets, mentor fellows, take technical ownership of replications, set publication standards, represent the program externally, and support fundraising and long-term planning. Work includes experimental design, review, writing, and coordination with XLab operations.

Leads enterprise Responsible AI governance, overseeing AI and Generative AI risk assessments, fairness testing, model evaluations, monitoring, controls, and lifecycle governance. Advises business, technical, legal, compliance, and risk stakeholders; develops scalable governance frameworks; and evaluates emerging technologies such as foundation models and agentic AI. Drives controls for monitoring, human oversight, intervention, containment, and system shutdown while balancing regulatory obligations, risk management, innovation, and business value.

Leads enterprise AI governance, portfolio strategy, intake, prioritization, roadmaps, and lifecycle management across UL organizations. Partners with business, technology, legal, security, risk, compliance, finance, HR, vendors, and external partners to operationalize responsible AI, evaluate delivery approaches, measure value, manage adoption, and drive AI literacy. Establishes operating frameworks, governance processes, performance metrics, change programs, and sustainable AI implementation strategies.

Lead enterprise AI strategy, governance, and adoption; partner with business and technical leaders to prioritize use cases, measure value and risk, and guide secure, responsible implementation of AI solutions while managing and developing AI talent.

Lead enterprise AI adoption by developing rollout plans, training programs, workflow playbooks, automation use cases, tool governance, acceptable-use policies, and risk controls. Measure adoption and business outcomes, manage an AI champions network, supervise a team, and partner with Security, Legal, Compliance, IT, Procurement, Product, and Engineering. The role focuses on safe, practical AI use across healthcare operations while addressing PHI, retention, vendor data use, HIPAA, and SOC 2 requirements.

Leads enterprise knowledge and AI transformation across Customer Success, Support, Enablement, Product, Operations, and Technology. Develops knowledge architecture, taxonomy, governance, metadata, content standards, ownership, and lifecycle processes. Builds AI-ready knowledge foundations for retrieval, self-service, copilots, and agentic workflows. Evaluates and implements AI opportunities, improves content and response quality using performance data, drives Knowledge-Centered Service practices, and leads cross-functional adoption and change without direct authority.

Advises enterprise executives on AI transformation for contact center and customer service operations. Develops AI strategies, roadmaps, business cases, operating models, and value realization frameworks. Leads executive workshops and defines use cases involving virtual agents, agent copilots, analytics, automation, and agentic AI. Evaluates platforms and technologies, provides vendor-neutral recommendations, and partners with clients, vendors, and delivery teams to guide successful implementation.

Lead end-to-end red-teaming campaigns against frontier AI models by defining threat models, designing attack scenarios and taxonomies, coordinating operators, and producing executive risk reports. Establish recurring testing cadences, track exploit families, measure severity and user harm, maintain dashboards, and partner with research, product, and operations teams to validate fixes and run regression tests.

Leads AI governance and assurance engagements, designing policies, controls, operating models, risk assessments, and compliance programs. Evaluates AI security, model governance, third-party solutions, and control effectiveness against frameworks including NIST AI RMF, ISO 42001, and the EU AI Act. Collaborates with security and engineering teams to test threats such as prompt injection, model manipulation, data poisoning, and leakage. Advises executives and clients, develops methodologies and proposals, researches emerging AI risks and regulations, and supports business development.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.