Maintain Accountability and Oversight

AI risks and opportunities for society

Accountability for AI decisions

Oversight in AI decision-making

Maintaining Human Oversight

Implement Model governance

monitoring

Review points

sign-off

Define responsibility for models in production

Establish review processes

Defining Operating Constraints

Implement system constraints and documentation

Public Involvement

Engange the public in projects

Address public concerns

Governance and Accountability Structures

Ethics oversight committees

Clear accountability and redress processes

Project Planning

Ethics assessment

Public interest

Fairness

Data sourcing

Risk analysis

Engage stakeholders and domain experts

Define project governance

data security

handling

Data Management

Understand consents and legal data usage

Ensure data security and trained staff

Consider impacts on privacy, bias and error

Implement quality checks and bias mitigation

Analysis & Development

Apply ethical, professional and regulatory standards

Monitor and asses risks

harm

bias

error

privacy

Maintain analytical rigor, quality assurance and peer review

Simplify models and validate thoroughly

Explain outcomes and uncertainties

Implementation & Delivery

Transparency in decision delegation

Share methods, results and limitations

Best practices in anonymizing and data sharing

Regular models reviews with risks assessments

Communication & Oversight

Engage stakeholders and clarify issues

ensure transparency and objectivity

Leadership responsibilities

Engage ethical principles and policies

Engage ethical bodies, privacy groups and public stakeholders