Certified Ethical Emerging Technologist (CEET) Exam Voucher

This exam will certify that the successful candidate has the knowledge, skills, and abilities required to apply foundational ethical principles, follow industry-standard frameworks, identify and mitigate risks, and navigate ethical organizational governance in order to devise and maintain ethical, trusted, and inclusive data-driven technologies.
 

This certification exam is designed for individuals seeking to demonstrate a vendor neutral, cross- industry, and multidisciplinary understanding of applied technology ethics that will enable them to navigate the processes by which ethical integrity may be upheld within emerging data-driven technology fields (such as Artificial Intelligence (AI), Internet of Things (IoT), and Data Science).
 

While there are no formal prerequisites to register for and schedule an exam, we strongly recommend you first possess the following knowledge, skills, and abilities:
• Demonstrate an understanding of the fundamental/foundational concepts related to ethics in data-driven technologies.
• Identify common ethical principles and frameworks and select the appropriate framework to understand and/or address ethical issues.
• Identify regulations, standards, and best practices utilized in the industry and identify the ethical challenges that may conflict with or compromise their implementation.
• Identify and mitigate the myriad risks that arise within the development, utilization, and/or implementation of data-driven technologies.
• Communicate about ethical risks and ethical practices internally within the organization and externally to relevant third parties.
• Create, implement, and evaluate ethical policies and governance regarding data-driven technology throughout the organization.
You can obtain this level of skill and knowledge by taking the following courseware, which is available through training providers around the world, or by attending an equivalent third-party training program:
• Certified Ethical Emerging Technologist™ (CEET)

Domain 1.0 Fundamental Concepts for Data-Driven Technology Ethics Objective 1.1 - Identify and describe common terminology or concepts important to data-driven technology ethics

• AI-related concepts
• Data science-related concepts
• Legal-related concepts
• Privacy-related concepts
• Ethics
• Bias
• Evaluation metrics

Objective 1.2 - Identify and describe common ethical theories

• Moral philosophy
• Applied ethics

Objective 1.3 - Identify when it is appropriate to conduct an ethical risk review

• Ideation and innovation management activities
• What-if scenario planning sessions
• New product/service development
• All along the data science/AI development lifecycle, from ideation and design through deployment and maintenance
• Stage gates and other points as appropriate within an organization’s Ethics-byDesign practices
• When legal and/or regulatory non-compliance has occurred
• When an ethical violation or incident has occurred

Domain 2.0 Ethical Frameworks Objective 2.1 - Identify common ethical principles cited by major ethical frameworks

• Privacy
• Accountability
• Safety and Security
• Transparency and Explainability
• Fairness and Non-Discrimination
• Human Control of Technology
• Professional Responsibility
• Promotion of Human Values

Objective 2.2 - Given an ethically challenging dilemma, identify and select an ethical framework to understand the issue

• Initiatives

Objective 2.3 - Follow applicable regulations, standards, and best practices

• Regulations
• Standards/best practices

Objective 2.4 - Identify ethical challenges that may conflict or require compromise with regulatory and/or business constraints or demands

• Data minimization principle vs. need for data
• Performance vs. explainability
• Compliance vs. cost
• Transparency/explainability vs. intellectual property rights
• Company/stakeholder needs vs. ethical decision making
• Ethics washing
• Efficiency vs. the risk of collateral damage
• Proliferation of AI to unscrupulous actors vs. democratization of AI (e.g., open source)
• Efficiency of development vs. cultural/contextual sensitivity
• Availability of datasets for ML algorithms vs. privacy protection
• Big data generated through devices (cloud, IoT) vs. concentration of power in big tech
• Fair competition vs. corporate hegemony (data assets)
• Efficiency/streamlining experience vs. enabling human agency/autonomy
• Moral relativism vs. evidence-based policy

Domain 3.0 Risk Identification and Mitigation Objective 3.1 - Identify and mitigate privacy risks

• Source
• Methods of identification
• Mitigation strategies
• Tools for identification/mitigation

Objective 3.2 - Identify and mitigate accountability risks

• Source
• Methods of identification
• Mitigation strategies
• Tools for identification/mitigation

Objective 3.3 - Identify and mitigate transparency and explainability risks

• Source
• Methods of identification
• Mitigation strategies
• Tools for identification/mitigation

Objective 3.4 - Identify and mitigate fairness and non-discrimination (bias) risks

• Source
• Methods of identification
• Mitigation strategies
• Tools for identification/mitigation

Objective 3.5 - Identify and mitigate safety and security risks

• Source
• Methods of identification
• Mitigation strategies (Security-by-Design practices)
• Tools for identification/mitigation

Domain 4.0 Communication Objective 4.1 - Effectively communicate with key stakeholders and/or team members (internal communication)

• Identified ethical risks
• Business impacts
• Business incentives

Objective 4.2 - Effectively communicate about the ethical practices of the organization to outside parties (external communication)

• Marketing/Public Relations
• Brand awareness/value
• Media inquiries
• Corporate reporting
• Organizational philosophy
• Disclosure statements

Domain 5.0 Organizational Policy and Governance Objective 5.1 - Identify the elements that can help foster an ethical organizational culture

• Training
• Leadership championing
• Incentive structures
• Culture-building workshops
• Creation of an ethics board
• Organizational resourcing

Objective 5.2 - Identify and describe the ethical considerations that shape policies regarding the development, use, and governance of technology

• Fair competition
• Open data
• Privacy
• Intellectual property
• Fairness
• Non-discrimination
• Legal and regulatory requirements
• Human rights
• Accountability
• Transparency
• Animal rights/welfare
• Safety and reliability
• Environmental concerns
• Economic impacts
• Workforce impacts

Objective 5.3 - Follow recommended guidelines for developing a code of ethics

• Identify internal and external stakeholders who should review or contribute
• Determine your organization’s memberships in ecosystems, industry, and professional groups
• Collect codes of ethics from the above groups and aggregate their codes as a minimum baseline for your own code
• Consult with process owners to understand any factors which may frustrate or impede the adoption of more ethical practices
• Publish draft among stakeholders who will participate in a pilot for a determined length of time
• During the pilot period, interview stakeholders at predetermined intervals to understand impacts of change
• Upon completion of the pilot, update and ratify the code of ethics

Objective 5.4 - Follow recommended guidelines for developing an ethical organizational policy

• Identify the need for the policy
• Identify the owner(s) of the policy
• Gather information about the policy
• Draft the policy
• Consult appropriate stakeholders
• Approve and publish the policy
• Establish procedures to support the policy
• Implement the policy
• Monitor and refresh the policy at regular intervals

Objective 5.5 - Evaluate the effectiveness of internal and external ethical policies

• Sentiment analysis of public discussion
• Surveys and focus groups
• Periodic health check of ethical policies
• Number and severity of ethical violations
• Industry best practices/leading best practices