Explore Tasks
Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
The accountable business owner receives a periodic oversight record for an automated or AI system in use.
12jobs do it
37jobs help with it
35skills it needs
- Accounts Payable Specialist
- Recruiter
- Learning & Development Specialist
- Talent Acquisition Lead
- Learning & Development Lead
- Lead Customer Insight Analyst
- Marketing Operations Lead
- Pricing Analyst
- Operations Coordinator
- Customer Service Team Lead
- Customer Success Lead
- Credit Controller
- Data Protection Officer
- Engineering Manager
- Accounts Payable Team Lead
- Financial Controller
- Head of Finance
- Head of Procurement
- Finance Director
- Procurement Director
- Chief Procurement Officer
- Talent Acquisition Manager
- Learning & Development Manager
- Head of HR
- HR Director
- Chief People Officer
- Customer Insight Analyst
- Communications Officer
- Communications Lead
- Insight Manager
- Communications Manager
- Marketing Operations Manager
- Chief Marketing Officer
- AI Product Manager
- Pricing Lead
- Head of Commercial
- Customer Success Manager
- Operations Manager
- Customer Service Manager
- Head of Operations
- Operations Director
- Chief Operating Officer
- Head of Insight
- Head of Marketing Operations
- AI Governance Lead
- Credit Manager
- Head of Communications
- Head of Customer Success
- Demand Planning Manager
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Assessing data-service continuity and recoverability
- Classifying AI uses against regulatory obligations
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Analysing error patterns and failure modes in models and AI systems
- Monitoring model fitness and drift
- Evaluating responsible AI use risks
- Recognising personal data and privacy duties
- Assessing privacy risk through data protection impact assessments
- Recognising cybersecurity threats to data work
- Recognising and containing suspected personal data breaches
- Assessing data-driven fairness and bias
- Assessing supplier dependency, data-handling and exit risk
- Explaining control types, dependencies and compensating controls
- Rating risks against appetite and choosing proportionate treatment
- Exercising human oversight of automated and AI outputs
- Specifying AI transparency and explanation duties to affected people
- Investigating anomalies
- Making accountable decisions under uncertainty
- Applying data classification and handling rules
- Running incident command: severity, timeline and status updates
- Recognising statutory incident reporting duties and deadlines
- Preserving incident evidence and timeline
Paths that teach the skills it needs
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Responsible AI Governance and Assurance
Govern and assure AI across its lifecycle including third-party AI and redress.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonApplied AI at Work