Ethics, Governance, Sustainability, and Regulation

Introduction

Strategic marketing creates value by identifying, communicating, and delivering offerings that satisfy human needs. However, marketing also influences beliefs, choices, identities, communities, markets, and the natural environment. The strategic question is therefore not only “How can the organisation increase demand?” but also “Should this demand be created, by what means, for whose benefit, and with what consequences?”

Ethical and responsible marketing concerns the legitimacy of marketing decisions and their effects on consumers, employees, suppliers, communities, investors, regulators, and the environment. It includes privacy and consent, data governance, consumer protection, inclusion and accessibility, truthful communication, algorithmic fairness, sustainability claims, and the governance systems that make responsible conduct routine rather than merely aspirational.

Law establishes minimum standards, but ethical marketing frequently requires more than legal compliance. A practice may be technically lawful yet manipulative, exclusionary, environmentally harmful, or inconsistent with consumer expectations. Strategic marketing managers must therefore combine legal knowledge, ethical reasoning, stakeholder analysis, evidence-based measurement, and organisational accountability.

Learning Objectives

By the end of this chapter, students should be able to:

1. Explain the relationship between marketing ethics, governance, regulation, and corporate responsibility.
2. Evaluate privacy, consent, data governance, and consumer-protection practices.
3. Analyse inclusion, accessibility, deceptive practices, and algorithmic bias.
4. Distinguish credible sustainability communication from greenwashing.
5. Assess stakeholder impacts and relevant environmental, social, and governance considerations.
6. Design practical controls for responsible marketing governance.
7. Apply ethical and regulatory reasoning to a contemporary marketing case.

1. Marketing Ethics and Responsible Value Creation

Marketing ethics is the systematic evaluation of marketing decisions according to principles such as honesty, fairness, respect for autonomy, non-maleficence, beneficence, justice, and accountability. These principles can conflict. For example, highly personalised advertising may improve relevance and convenience while reducing privacy or increasing the risk of manipulation.

A useful distinction is between descriptive and normative analysis. Descriptive analysis asks what organisations, consumers, or regulators actually do. Normative analysis asks what they ought to do. Strategic managers need both: empirical evidence reveals likely effects, while ethical reasoning evaluates whether those effects are acceptable.

Several ethical perspectives are relevant:

• Utilitarianism evaluates the overall balance of benefits and harms.
• Rights-based ethics protects claims such as privacy, informed choice, dignity, and freedom from discrimination.
• Justice theories ask whether benefits, burdens, opportunities, and risks are distributed fairly.
• Virtue ethics focuses on organisational character, including honesty, responsibility, courage, and practical wisdom.
• Stakeholder theory treats affected groups—not only shareholders—as legitimate participants in strategic decision-making.

No single perspective resolves every problem. A campaign that increases total sales may still violate rights or disproportionately harm a vulnerable group. A robust decision should therefore consider outcomes, rights, fairness, character, and stakeholder voice.

Ethical intensity

The ethical importance of a marketing decision tends to increase when it involves serious potential harm, vulnerable consumers, limited consumer choice, large numbers of affected people, irreversible consequences, or a high degree of managerial responsibility. Targeting children, using sensitive health data, promoting unsafe products, or deploying opaque credit-related algorithms therefore demands stronger scrutiny than an ordinary preference-based promotion.

2. Governance: Turning Principles into Practice

Marketing governance is the system through which marketing decisions are directed, monitored, documented, and corrected. It includes policies, roles, approval processes, training, controls, audits, escalation procedures, and remedies.

Ethical statements without governance are weak because responsibility remains vague. Effective governance answers five questions:

1. Who decides? Responsibility must be assigned to named roles, not to an abstract “marketing team.”
2. What standards apply? Policies should define prohibited practices, evidence requirements, and risk thresholds.
3. What evidence is required? Claims, consent records, audience definitions, accessibility tests, and model evaluations should be documented.
4. Who reviews risk? Legal, privacy, compliance, information security, customer experience, and affected-community representatives may need to participate.
5. What happens when something goes wrong? Organisations need incident response, correction, consumer notification, compensation where appropriate, and learning mechanisms.

A practical governance cycle is:

Identify → Assess → Approve → Monitor → Remedy → Learn

This cycle should operate throughout a campaign or data project rather than only at launch. Governance is particularly important where marketing depends on agencies, platforms, data brokers, artificial intelligence, influencers, or international operations.

Three lines of responsibility

A useful organisational model distinguishes three lines:

• First line: marketing and product teams own day-to-day risks and controls.
• Second line: privacy, legal, compliance, risk, and information-security functions provide standards and challenge.
• Third line: internal audit or an equivalent independent function evaluates whether governance works in practice.

Boards and senior executives remain responsible for organisational culture and material risks. Accountability cannot be transferred entirely to an agency, software vendor, or algorithm.

3. Privacy, Consent, and Data Governance

Privacy as a strategic and ethical issue

Privacy concerns how information about individuals is collected, used, shared, retained, and deleted. It includes more than secrecy. Consumers may object to unexpected profiling, persistent tracking, inference of sensitive characteristics, or the use of data in ways that alter their opportunities.

Marketing data may include directly provided information, observed behavioural data, inferred attributes, and information obtained from third parties. Inferred data can be especially sensitive because people may not know that an organisation has made a prediction about them or may be unable to correct it.

Important privacy principles commonly found in data-protection regimes include:

• Purpose limitation: collect and use data for specified, legitimate purposes.
• Data minimisation: collect only what is reasonably necessary.
• Transparency: explain practices in clear, accessible language.
• Accuracy: maintain and correct relevant information.
• Storage limitation: retain information only as long as justified.
• Security: protect data against unauthorised access, loss, and misuse.
• Accountability: demonstrate compliance through records and controls.

Legal requirements differ across jurisdictions. Depending on location and context, organisations may need to address lawful bases for processing, consent standards, access and deletion rights, restrictions on sensitive data, children’s privacy, cross-border transfers, breach notification, and automated-decision rights. Managers must obtain current legal advice rather than assume that one national rule applies everywhere.

Meaningful consent

Consent is ethically meaningful when it is informed, specific, freely given, unambiguous, and capable of being withdrawn without unreasonable penalty. A pre-ticked box, confusing interface, bundled agreement, or “accept all” design that makes refusal difficult may produce a record of clicking without producing genuine autonomy.

A sound consent process should explain:

• what data will be collected;
• why it is needed;
• who will receive it;
• how long it will be retained;
• whether automated profiling is involved;
• which choices are optional; and
• how consent can be withdrawn.

Consent is not always the only legal basis for processing, and obtaining consent does not justify unfair or harmful use. Organisations should not use consent as a substitute for necessity, proportionality, security, or ethical judgment.

Data governance controls

A marketing data-governance programme should include a data inventory, ownership assignments, classification of sensitive information, retention schedules, access controls, vendor due diligence, data-quality procedures, incident response, and periodic review of whether the original purpose remains legitimate. Data protection should be built into campaign design—often called privacy by design—rather than added after implementation.

A useful risk assessment asks:

• What is the data source and is it reliable?
• Could the data reveal or predict a sensitive characteristic?
• What power imbalance exists between organisation and consumer?
• Could a breach or incorrect inference cause material harm?
• Can the objective be achieved with less data?
• Can consumers understand and challenge the outcome?

4. Consumer Protection and Deceptive Practices

Consumer-protection law generally seeks to prevent unfair, misleading, aggressive, or unsafe commercial practices. Rules vary, but the central principle is that marketing communications should not create a materially false or misleading overall impression.

Misleading conduct can arise through:

• false factual claims;
• omission of material limitations or conditions;
• misleading price comparisons;
• fabricated scarcity or urgency;
• hidden fees or subscription renewal terms;
• fake reviews or undisclosed paid endorsements;
• unsupported health, performance, or environmental claims;
• dark patterns that steer consumers into choices they did not reasonably intend.

The assessment often concerns the likely understanding of the relevant audience, not merely the literal truth of one sentence. A technically accurate headline can still mislead if important qualifications are hidden, unreadable, or presented too late.

Evidence and claim substantiation

Before publication, the organisation should identify the precise claim, define its scope, specify the evidence needed, and ensure that evidence is current and representative. Comparative claims require a fair comparison, consistent measures, and a clear basis. Testimonials should reflect genuine experience and should not imply typical results unless typicality is supported or clearly qualified.

Influencer marketing creates additional disclosure obligations and ethical duties. The commercial relationship should be prominent, understandable, and placed where audiences will see it before relying on the recommendation. Organisations should also monitor influencer conduct rather than treat disclosure as a one-time contractual formality.

5. Inclusion, Accessibility, and Fairness

Inclusive marketing recognises differences in culture, language, gender, age, disability, income, geography, religion, and identity without reducing people to stereotypes. It concerns both representation and access to products, information, prices, services, and complaint mechanisms.

Accessibility is the extent to which people with different abilities can perceive, understand, navigate, and interact with marketing and the resulting service. Practical requirements may include captions, transcripts, image descriptions, keyboard navigation, readable contrast, plain language, flexible contact channels, accessible physical environments, and compatibility with assistive technologies.

Inclusion requires more than diverse imagery. A campaign may depict diversity while excluding people through unaffordable pricing, inaccessible design, biased targeting, or inadequate customer support. Organisations should involve affected users in research and testing, compensate community expertise appropriately, and avoid assuming that one person represents an entire group.

Fairness and segmentation

Segmentation can create value, but it can also produce unequal treatment. A manager should distinguish:

• Differential treatment: different offers or treatment are deliberately provided to different groups.
• Disparate impact: a seemingly neutral rule produces significantly worse outcomes for a protected or vulnerable group.
• Exclusion error: eligible people are denied an opportunity because a model or rule fails to recognise them.
• Allocation error: benefits or opportunities are distributed in a systematically unfair manner.

Not every difference is discriminatory; some differences may be relevant and beneficial, such as providing a disability-related accommodation. The key questions are whether the distinction is relevant, proportionate, explainable, contestable, and consistent with law and organisational values.

6. Algorithmic Marketing and Bias

Artificial intelligence and automated analytics support recommendation, pricing, audience selection, fraud detection, content generation, and customer service. They can improve relevance and efficiency, but they can also amplify historical inequality, infer sensitive attributes, discriminate through proxies, and make responsibility difficult to locate.

Bias can enter through:

1. Data bias: the training data are incomplete, unrepresentative, or historically discriminatory.
2. Measurement bias: the chosen outcome is a poor proxy for the organisation’s real objective.
3. Design bias: labels, features, thresholds, or optimisation criteria favour some groups.
4. Deployment bias: a system is used in a context different from the one for which it was evaluated.
5. Feedback loops: automated decisions change behaviour and generate new data that reinforce the original pattern.

Responsible practice requires documenting the purpose and limits of a system, assessing data provenance, testing performance across relevant groups, monitoring drift, providing human oversight, and offering explanations and appeals where decisions materially affect people. Fairness cannot be reduced to one universal statistical test; different fairness criteria may conflict, and the appropriate evaluation depends on context, law, and the harm being considered.

Generative AI introduces further issues: fabricated claims, copyright concerns, undisclosed synthetic endorsements, privacy leakage, stereotyping, and a lack of accountability for generated content. Human review should be proportionate to risk, and the organisation should retain clear records of prompts, sources, approvals, and corrections.

7. Sustainability, ESG, and Greenwashing

Sustainability in marketing concerns the environmental and social consequences of products, services, communications, and consumption systems. Environmental, social, and governance (ESG) considerations provide a broad framework for evaluating organisational impacts and oversight.

Typical environmental issues include greenhouse-gas emissions, energy and water use, resource extraction, packaging, waste, biodiversity, and product durability. Social issues include labour conditions, human rights, health and safety, community effects, inclusion, and consumer welfare. Governance includes board oversight, ethics, risk management, transparency, controls, and anti-corruption.

Marketing must avoid treating ESG as a communications exercise detached from operations. A credible sustainability claim is supported by evidence, has a defined boundary, uses an appropriate comparison or baseline, acknowledges material limitations, and is consistent with the organisation’s actual practices.

Greenwashing

Greenwashing occurs when environmental communication creates an unjustifiably favourable impression of environmental performance. Common forms include:

• vague terms such as “eco-friendly” without a defined basis;
• emphasis on a minor improvement while hiding major impacts;
• irrelevant claims, such as highlighting the absence of a substance that is already legally prohibited;
• labels or imagery that imply certification without independent verification;
• claims about future net-zero performance without a credible transition plan;
• presenting an offset as if it eliminates the product’s underlying emissions.

Managers should define the unit of analysis, establish the baseline, verify the evidence, communicate uncertainty, and ensure that the claim is understandable to the intended audience. Product-level improvements do not automatically justify claims about the entire organisation or value chain.

Measuring marketing’s sustainability effects

Useful indicators may include the proportion of claims independently verified, packaging intensity, return and disposal rates, emissions associated with campaigns, accessibility completion rates, complaint resolution, supplier compliance, privacy incidents, and outcomes across demographic groups. Metrics should not be selected merely because they are easy to improve. A dashboard that reports advertising reach but ignores complaints, exclusion, or material environmental effects is strategically incomplete.

8. Stakeholder Analysis and Responsible Decision-Making

Stakeholder analysis identifies parties affected by a decision and examines their interests, power, vulnerability, legitimacy, and ability to influence outcomes. Relevant stakeholders may include customers, non-customers, employees, suppliers, distributors, communities, regulators, investors, advocacy groups, and future generations.

A practical decision process is:

1. Define the marketing objective and the affected decision.
2. Map direct, indirect, and cumulative impacts.
3. Identify legal duties, ethical principles, and organisational commitments.
4. Consult affected stakeholders, especially vulnerable or underrepresented groups.
5. Compare alternatives, including a lower-data or lower-harm option.
6. Assess distribution of benefits, risks, and voice.
7. Document the rationale, evidence, uncertainty, and approval.
8. Monitor outcomes and provide remedy.

This process prevents the common error of treating consumers only as targets. People may be recipients of marketing, data subjects, workers in a supply chain, community members, or citizens affected by broader market outcomes.

9. An Integrated Responsible Marketing Framework

The following framework can be used for campaign planning, product launches, customer analytics, and AI-enabled marketing.

Stage 1: Purpose and materiality

State the legitimate business and consumer purpose. Identify why the activity matters and which impacts are material. Reject objectives that depend on deception, exploitation, unlawful discrimination, or unnecessary surveillance.

Stage 2: Stakeholder and rights assessment

Identify affected groups, vulnerabilities, rights, expectations, and possible harms. Consider people who are not customers but may experience externalities.

Stage 3: Evidence and design

Specify data sources, claim evidence, audience definitions, accessibility requirements, model limitations, sustainability boundaries, and alternatives. Build privacy, fairness, and accessibility into the design.

Stage 4: Review and approval

Use proportionate review. High-risk activities—such as sensitive profiling, children’s marketing, health claims, financial targeting, or automated eligibility decisions—require specialist and independent review.

Stage 5: Transparent execution

Communicate in clear language. Provide meaningful choices, disclosures, complaints channels, and explanations. Ensure that contractors and platforms follow equivalent standards.

Stage 6: Measurement and remedy

Monitor actual outcomes, not merely reach or conversion. Investigate complaints, disparities, security incidents, misleading claims, and unintended environmental effects. Correct public errors promptly and provide appropriate remedy.

Illustrative Case: Personalised Sustainability Marketing at TerraHome

Background

TerraHome, a fictional online retailer of household products, plans to use browsing behaviour, purchase history, location, and inferred household characteristics to promote “low-impact” products. It also proposes an AI system to rank customers by likelihood of purchasing sustainable goods.

Problem

The marketing team wants higher conversion and a stronger sustainability reputation. However, the data include inferred income and family status. The proposed model performs less accurately for rural customers and some minority-language users. Several products labelled “planet positive” have only partial lifecycle evidence, and the advertising agency intends to use a pre-selected “accept all” tracking banner.

Analysis

The project presents several risks. First, inferred attributes may be sensitive and unnecessary for the stated purpose. Second, the model may systematically provide fewer sustainable offers to groups it predicts will not buy them, reinforcing unequal access. Third, “planet positive” may imply a broad environmental benefit that the evidence does not support. Fourth, the consent interface may undermine meaningful choice. Finally, the organisation could be held responsible for agency conduct and model outcomes even if an external vendor built the system.

Approach and solution

TerraHome redesigns the project as follows:

• It defines a narrower purpose: recommending products based on expressed preferences and product characteristics, not inferred income or protected characteristics.
• It removes unnecessary variables and establishes a retention limit.
• It offers a clear opt-in for personalised sustainability recommendations and a usable non-personalised alternative.
• It tests recommendation rates, accuracy, and customer experience across relevant groups.
• It allows customers to inspect and correct preference information and to appeal unsuitable recommendations.
• It replaces “planet positive” with specific claims supported by lifecycle evidence, such as reduced packaging relative to a named baseline.
• It requires supplier evidence and independent review for material environmental claims.
• It adds captions, plain-language explanations, keyboard-accessible controls, and multilingual information.
• It creates an approval record involving marketing, privacy, legal, accessibility, sustainability, and customer-service functions.
• It monitors complaints, opt-out rates, recommendation disparities, claim challenges, and environmental outcomes.

Outcome

The redesign produces a lower initial conversion rate than the most aggressive proposal but improves trust, reduces data collection, increases accessibility, and avoids unsupported claims. Over time, clearer recommendations lead to fewer returns and complaints. The case illustrates that responsible marketing is not simply a restriction on growth: it can improve resilience, brand credibility, customer relationships, and decision quality.

Learning points

1. Personalisation should be justified by purpose and proportionality, not by data availability.
2. Fairness must be evaluated in actual outcomes across relevant groups.
3. Sustainability claims require defined evidence and boundaries.
4. Consent design is part of marketing design.
5. Vendors do not eliminate the organisation’s accountability.
6. Trust, accessibility, and remedy are measurable strategic assets.

Challenges and Limitations

Responsible marketing governance faces significant difficulties. Laws differ across jurisdictions and can change faster than organisational systems. Ethical principles may conflict: greater personalisation can improve relevance but require more data; strict fraud controls can inconvenience legitimate customers; sustainability claims may be difficult to communicate without oversimplification.

Measurement is also limited. Many harms are diffuse, delayed, or experienced by non-customers. A fairness metric may show equal error rates while masking a more fundamental problem: the decision should not have been automated at all. Similarly, a carbon reduction percentage may ignore absolute growth in total emissions. Managers should therefore combine quantitative indicators with qualitative research, complaints, audits, and stakeholder testimony.

There is also a risk of ethics washing: publishing principles without changing incentives, procurement, targets, or executive accountability. Responsible governance is credible only when employees can challenge a profitable but harmful practice and when performance systems reward long-term trust rather than short-term conversion alone.

Contemporary Developments and Best Practices

Current marketing environments are shaped by platform regulation, restrictions on third-party tracking, expanding data rights, accessibility expectations, climate-disclosure requirements, supply-chain scrutiny, synthetic media, and automated decision systems. The exact legal obligations depend on jurisdiction and sector, but several broad practices are durable:

• maintain a current inventory of marketing data, vendors, claims, models, and risks;
• use privacy-preserving and data-minimising alternatives where feasible;
• make consent and refusal equally understandable and usable;
• test customer journeys with disabled and diverse users;
• substantiate claims before publication, not after challenge;
• record model purpose, data provenance, limitations, and review results;
• monitor outcomes by relevant customer groups;
• provide human escalation and meaningful correction mechanisms;
• align executive incentives with trust, safety, sustainability, and customer welfare;
• publish material errors and remedial actions transparently;
• train staff in law, ethics, accessibility, data protection, and responsible AI;
• audit high-risk campaigns independently.

The strategic objective is not zero risk, which is unrealistic, but informed, proportionate, transparent, and accountable risk management.

Chapter Summary

Ethical and responsible marketing evaluates not only whether an activity is effective or legal, but also whether it respects autonomy, rights, fairness, dignity, stakeholder interests, and environmental limits. Governance translates these principles into assigned responsibility, evidence requirements, review procedures, monitoring, and remedy.

Privacy requires purpose limitation, minimisation, transparency, security, retention controls, and meaningful choice. Consumer protection requires truthful overall impressions, substantiated claims, clear pricing, authentic endorsements, and avoidance of manipulative design. Inclusion and accessibility concern both representation and the ability to obtain, understand, and use marketing and services.

Algorithmic marketing can produce bias through data, measurement, design, deployment, and feedback loops. Sustainability communication must be specific, bounded, evidenced, and consistent with operational reality; otherwise it risks greenwashing. ESG analysis expands attention to environmental, social, and governance impacts, while stakeholder analysis identifies who bears benefits and harms.

A responsible marketing system follows a cycle of purpose definition, stakeholder assessment, evidence-based design, specialist review, transparent execution, monitoring, and remedy. Its success should be judged through long-term trust and stakeholder outcomes as well as commercial performance.

Key Terms

Marketing ethics: The evaluation of marketing conduct according to principles of right, fair, and responsible behaviour.

Marketing governance: Structures and processes used to direct, control, monitor, and correct marketing decisions.

Privacy: Control and appropriate treatment of information about individuals.

Consent: A voluntary, informed, specific, and unambiguous agreement to a defined activity.

Data minimisation: Collecting and using no more personal data than is reasonably necessary.

Purpose limitation: Restricting data use to specified and legitimate purposes.

Consumer protection: Legal and ethical safeguards against misleading, unfair, unsafe, or exploitative commercial conduct.

Dark pattern: An interface design that steers or pressures users into choices they may not otherwise make.

Accessibility: The ability of people with different abilities to perceive, understand, navigate, and use a product or communication.

Inclusion: Designing participation and access so that diverse groups are respected and not unfairly excluded.

Algorithmic bias: Systematic and unjustified disadvantage produced or amplified by an automated system.

Disparate impact: Unequal adverse effects produced by a seemingly neutral practice.

Greenwashing: Environmental communication that creates an unjustifiably favourable impression of environmental performance.

ESG: Environmental, social, and governance considerations used to evaluate organisational impacts and oversight.

Stakeholder: An individual or group affected by, or able to affect, an organisation’s decisions.

Claim substantiation: Evidence demonstrating that a marketing claim is accurate, relevant, and appropriately limited.

Privacy by design: Incorporating privacy protections into systems and processes from the beginning.

Remedy: Corrective action provided when people experience harm, error, or rights violations.

Review Questions

Short Answer Questions

1. What is the difference between legal compliance and ethical marketing?
2. Identify four principles of responsible data governance.
3. What makes consent meaningful?
4. Define greenwashing and provide one example.
5. Why is accessibility a strategic marketing concern?

Long Answer Questions

1. Explain how stakeholder theory can improve marketing decision-making.
2. Discuss the main sources of algorithmic bias in personalised marketing.
3. Evaluate the relationship between consumer protection and dark patterns.
4. Explain how ESG considerations should influence marketing strategy and measurement.
5. Describe an integrated governance cycle for a high-risk marketing campaign.

Analytical / Application Questions

1. A retailer uses inferred health and income data to target discounts. Analyse the privacy, fairness, consent, and governance issues and recommend a lower-risk alternative.
2. A beverage company claims that its product is “100% sustainable” because its packaging contains recycled material. Assess the claim and design a credible communication approach.
3. An AI recommendation system offers premium products mainly to affluent urban customers. Design an evaluation and remediation plan that addresses data quality, fairness, accessibility, transparency, and accountability.

Multiple Choice Questions with Answers

Question 1

Which principle requires an organisation to collect only the personal data reasonably necessary for a stated purpose?

A. Data minimisation

B. Purpose expansion

C. Algorithmic opacity

D. Scarcity pricing

Correct Answer: A. Data minimisation

Question 2

Which feature is most essential to meaningful consent?

A. A pre-selected acceptance box

B. A clear and voluntary choice that can be withdrawn

C. A long agreement written in technical language

D. A requirement to accept tracking to access every service

Correct Answer: B. A clear and voluntary choice that can be withdrawn

Question 3

A claim that highlights a small packaging improvement while concealing major environmental impacts is most likely an example of:

A. Inclusive design

B. Privacy by design

C. Greenwashing

D. Data minimisation

Correct Answer: C. Greenwashing

Question 4

Which statement best describes disparate impact?

A. A deliberate decision to use different colours in two advertisements

B. A neutral rule that produces significantly worse outcomes for a particular group

C. A consumer’s decision to reject an advertisement

D. A regulator’s requirement for claim evidence

Correct Answer: B. A neutral rule that produces significantly worse outcomes for a particular group

Question 5

Who primarily owns day-to-day marketing risks and controls in the three-lines model?

A. The first line, consisting of marketing and product teams

B. Only external auditors

C. Only the board of directors

D. The advertising platform

Correct Answer: A. The first line, consisting of marketing and product teams

Question 6

Which practice best supports accessibility in digital marketing?

A. Using images without descriptions

B. Making all information available only through audio

C. Providing captions, readable contrast, and keyboard navigation

D. Removing complaint channels to simplify the interface

Correct Answer: C. Providing captions, readable contrast, and keyboard navigation

Question 7

Why can inferred data create heightened ethical risk?

A. It is always more accurate than information supplied by consumers

B. Consumers may not know what characteristics have been predicted about them

C. It cannot be stored electronically

D. It automatically eliminates discrimination

Correct Answer: B. Consumers may not know what characteristics have been predicted about them

Question 8

What is the strongest basis for publishing a comparative environmental claim?

A. A vague slogan and attractive nature imagery

B. A competitor’s unverified social-media statement

C. Defined evidence, a clear baseline, and an appropriate comparison

D. The marketing team’s personal belief

Correct Answer: C. Defined evidence, a clear baseline, and an appropriate comparison

Question 9

Which action is most appropriate when an automated marketing system produces unequal outcomes across customer groups?

A. Ignore the difference if total sales increase

B. Delete all records of the system’s operation

C. Investigate data and design causes, monitor outcomes, and provide remediation

D. Transfer responsibility entirely to the software vendor

Correct Answer: C. Investigate data and design causes, monitor outcomes, and provide remediation

Question 10

What is the central purpose of stakeholder analysis in responsible marketing?

A. To focus exclusively on shareholders

B. To identify affected groups and assess the distribution of benefits, risks, and voice

C. To maximise advertising frequency regardless of impact

D. To replace all commercial objectives with charitable objectives

Correct Answer: B. To identify affected groups and assess the distribution of benefits, risks, and voice