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DEBIZ

Data Ethics for Business Professionals (DEBIZ) is designed so business professionals can identify, explain, and document how their organization identifies ethical principles and sources of risk, and creates sustainable ethical decision-making processes.

DEBIZ

Data Ethics for Business Professionals (DEBIZ) is designed so business professionals can identify, explain, and document how their organization identifies ethical principles and sources of risk, and creates sustainable ethical decision-making processes.

DEBIZ Jobs

DEBIZ is intended for business leaders and stakeholders, product and project managers, and all else who are seeking to deliver ethical data-driven solutions.

DEBIZ Exam Details

A Data Ethics Business Professional is an individual who has demonstrated foundational knowledge of the ethical use of data in emerging technologies in a business context consistent with the DEBIZ Job Task Analysis. A credential holder is able to identify, explain, and document how organizations identify ethical principles and considerations, understand sources of risk, and explain how businesses can create sustainable, ethical decision-making processes.

TARGET CANDIDATE

The Data Ethics for Business Professionals (DEBIZ) exam is designed for individuals who are seeking to demonstrate an understanding of ethical uses of data in business settings. The exam is open to new and established professionals seeking to learn more about data ethics in the modern business world.

EXAM CODES

DEB-110

LAUNCH DATE

October 2021

SUNSET DATE

TBD

EXAM DURATION

Estimated 20-45 minutes, candidates may retake as many times as desired

PASSING SCORE

80% (20 of 25 Items)

NUMBER OF ITEMS

25

ITEM FORMATS

Multiple Choice/Multiple Response/True-False

EXAM OPTIONS

Online via the CHOICE LMS

DEBIZ makes ethical principles accessible, and risk mitigation possible for all your data-driven initiatives.

TRUST, A MUST

Industry and governments are enacting more regulation to ensure trustworthy tech. DEBIZ can help you define how to develop processes to stay abreast of these dynamic requirements.

LEAD ETHICAL DATA INITIATIVES

Verify that your data stakeholders have the requisite knowledge to design and deliver ethically aligned projects.

Data Ethics for Business Professionals Training

To ethically implement data projects, you require fundamental knowledge to drive results. CertNexus DEBIZ training covers foundational terminology, technical concepts, and business use cases so you can actively participate or lead your next data project, ethically.

Course Details

1. Ethical Principles

Identify the key features of data privacy.

Privacy models

Comprehensive
Sectoral
Co-regulatory
Self-regulatory

Privacy legislation and regulations

GDPR
HIPAA

The ways that consent can be given
Variability of proper consent based on location
Issues/shortcomings of individual self-managed consent paradigm
Digital footprints
Privacy by Design (PbD) foundational principles

Understand the key elements of accountability.

Data governance frameworks
Documentation of best practices
Accuracy & integrity
Individual and collective responsibility for maintaining existing values
Audit types
Risk assessments
Data Protection Impact Assessments (DPIAs)
Contractual best practices
Understand broader professional responsibility in the collection and use of data
ACM Code of Professional Ethics
Ethical design of technology
Whistleblower policy
Consideration for long term effects

Understand the essential features and approaches to transparency and explainability.

Data transparency
Notice about interaction with AI and decisions of AI
Choice to ignore AI-based outcomes/be excluded from automated decisions
Secondary uses of data
Explainability
Auditability
Right to information data interpretation and data labels
Challenges of transparency and explainability

Demonstrate an understanding of human-centered values and fairness.

Data models that prioritize human values
Protected classes (variable by jurisdiction)
Community involvement in decision-making
Types of fairness
Procedural
Outcome
Correlation vs. Causation
Unmeasurable impacts that fall outside of metrics
Social norms
Group versus individual
Statistical accuracy
False positives and negatives
Testing prior to deployment

Identify important features of inclusive growth, Sustainable Development

Tech for good
Human agency
Need for competence
Human rights as grounds for data stewardship
Indigenous Data Governance
Intercultural Digital Ethics
Global inclusivity (of non-western based values and principles)
Human-Computer Interaction
Critical Data Studies
Impact on natural environments
Impact on labore

Understand the methodology for identifying and assessing trade-offs.

Trade-off analysis
Consideration of all potential models
How decisions can lead to systems that incorrectly prioritize one criterion as lover another more important criteria
Senior management approval of the model selected can lead to models aslbeing developed that are unsuitable or pose a risk to personal data
Conducting continuous review of a data system leads to new trade-offs not aslbeing considered or approved

2. Business Considerations

Identify the types of laws applicable to data.

Product liability laws
Directors’ duties
Corporate structures

Partnerships
LLCs
Charities

Anti-discrimination laws
Legal basis

GDPR

Notice

GDPR
BIPA

Children’s Code
Alignment to AI principles of equity
Key anti-discrimination legislation

Fair Credit Reporting Act
Title VII of Civil Rights Act
Human Rights Declaration

Understand the key implications of social and behavioural effects.

Disinformation and misinformation
Nudging/Social Proofing
Decision-making
Social rating systems
User profiling and matching algorithms

Responsible business practices that reinforce trustworthiness

Stakeholder management and communication strategies
Types of stakeholders

Internal
External

Data hygiene best practices
Credibility and reliability

How the use of data can have a positive or negative effect on business reputation.

Talent attraction and retention
Consumer loyalty and Net Promoter Score
Improved investor and business partner relationships through ethics
Brand enhancement through commitment to continuous data curation
Reputation impact of data ethics

How organizational values are embedded along the data value chain.

Identify key values in a data-centric culture
Understand how organizational values align to data ethics
Decision-making in line with organizational values
Applying ethical principles in business
Ethical supply chain management

Business case for ethically driven business data models.

Benefits and harms of granular tracking and microtargeting
Commercial justifications and advantages of acting ethically
Value alignment
The pace at which legislation lags technology innovation

Understanding of the connection between ethics to business operations.

Complex AI/data supply chains
The use of data and AI internally
Accountability
Roles
Responsibilities
Automation and productivity
Ethics training

3. Sources of Risk

Understanding of bias and how it can be embedded in emerging tech.

Statistical definition of bias
Bias reflected within datasets

Historical
Structural
Sampling
Availability bias

Technocentrism and techno-solutionism
Bias mitigation techniques

AI can lead to discrimination, both directly and indirectly.

Disparate impact
Unintentional data discrimination
Account for biases in data models
Fairness above analytics

Identify the key aspects of safety and security in using data.

Data protection principles
Data systems
Cyber Security
Impact on outputs
Risk of harm
Unauthorized disclosure/access
Risk mitigation
Theft of proprietary information and data
Disinformation campaigns
Adversarial machine learning

AI and data science can lead to abnormal, unintended, or unintelligible outputs.

Concept drift
Human in the loop
Detecting abnormal behaviors

Important concepts of data surveillance and its broadening scope.

Online behavior tracking
Internet-of-things and embedded computing
Smart and mobile devices
Biometric data
Facial recognition software
Smart home/smart city
Hidden data exchange markets
First-party, second-party, and third-party data
Predictive analytics
Employee surveillance

Video Packages
Online Live Class

Fees Structure :	22500 INR / 270 USD
Total No of Class :	54 Video Class
Class Duration :	37 Working Hours
Download Feature :	Download Avalable
Technical Support :	Call / Whatsapp : +91 8680961847
Working Hours :	Monday to Firday 9 AM to 6 PM
Payment Mode :	Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay)

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Fees Structure :	30500 INR / 365 USD
Class Duration :	60 Days
Class Recording :	Live Class Recording available
Class Time :	Monday to Firday 1.5 hours per day / Weekend 3 Hours per day
Technical Support :	Call / Whatsapp : +91 8680961847
Working Hours :	Monday to Firday 9 AM to 6 PM
Payment Mode :	Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay)

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