Ethics, Governance, Risk, and Future Practice

Introduction

Content distribution is not only a technical or promotional activity. It is also an exercise of organisational power. A marketer decides whose attention is sought, what information is collected, which claims are amplified, how audiences are categorised, and which messages are made visible or invisible. These decisions affect privacy, autonomy, equality, reputation, public health, and trust.

Ethical content distribution therefore asks more than whether a campaign achieves reach or conversions. It asks whether the campaign is lawful, honest, inclusive, proportionate, accountable, and socially responsible. Governance converts these principles into repeatable responsibilities, controls, records, and escalation procedures. Risk management identifies what could go wrong, estimates its significance, and reduces or responds to potential harm.

The digital environment makes these issues especially important. Platforms change their rules, algorithms influence visibility, third-party cookies are declining, artificial intelligence can generate persuasive but inaccurate material, and emerging environments such as virtual worlds and decentralised networks create uncertain responsibilities. A professional content marketer must be able to promote content effectively without treating audiences merely as data points or platforms merely as neutral distribution pipes.

Learning Objectives

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

1. Explain the ethical foundations of responsible content distribution.
2. Apply principles of privacy, consent, copyright, accessibility, advertising disclosure, and brand safety.
3. Describe how platform policies and governance systems shape distribution decisions.
4. Identify risks involving misinformation, algorithmic bias, artificial intelligence, channel dependence, and measurement.
5. Recommend practical controls for compliant and trustworthy campaigns.
6. Evaluate emerging cookieless, decentralised, and immersive channels critically.

1. Ethics as a Distribution Responsibility

Ethics concerns the justified treatment of people and communities. In content marketing, ethical practice is commonly associated with four principles:

• Respect for autonomy: people should understand when and why they are being addressed, tracked, persuaded, or profiled.
• Beneficence: communication should create legitimate value and avoid foreseeable harm.
• Non-maleficence: marketers should not knowingly cause or facilitate harm through deception, exclusion, unsafe claims, or irresponsible targeting.
• Justice: benefits, burdens, opportunities, and visibility should not be allocated unfairly.

These principles complement, but do not replace, law. A practice may be legally permissible yet ethically questionable. For example, a highly complex consent interface may technically record a choice while making refusal difficult. Similarly, an advertisement may comply with disclosure rules but exploit anxiety or target people at a particularly vulnerable moment.

A useful professional test is the transparency and reversibility test:

1. Could the audience reasonably understand what is happening?
2. Could the organisation explain the decision publicly to an affected person or regulator?
3. Can the person withdraw, correct, or challenge the decision where appropriate?
4. Would the organisation accept the same practice if it were applied to its own employees or executives?

These questions do not produce an automatic legal answer, but they expose hidden power imbalances and weak governance.

2. Privacy, Data Protection, and Consent

Privacy is concerned with appropriate control over personal information and personal space. Personal data may include names and email addresses, but also identifiers, device information, location, browsing behaviour, inferred interests, purchase history, and combinations of data that make a person identifiable. Definitions and obligations vary by jurisdiction; marketers must consult the law applicable to their organisation, audience, and processing activity.

2.1 Core privacy principles

Responsible data practice usually includes the following principles:

Consent is one possible legal and ethical basis for processing, but it is not a universal solution. Meaningful consent should be informed, specific, freely given, unambiguous, and capable of withdrawal where the relevant law requires these conditions. Bundling consent to promotional tracking with access to an unrelated service can undermine voluntariness. Pre-ticked boxes, confusing language, and interfaces that make acceptance prominent while concealing refusal are poor practice.

Marketers should distinguish first-party data, collected directly through a relationship with an audience, from second-party data, received from a partner, and third-party data, obtained through external providers. First-party data is not automatically ethical: it still requires a clear purpose, appropriate notice, security, and lawful processing.

A practical consent record should document the wording presented, date and time, channel, purpose, identity or pseudonymous identifier where appropriate, and withdrawal status. The record should not contain more personal information than necessary.

2.2 Privacy impact thinking

Before launching a campaign, a team can conduct a lightweight privacy impact review:

1. What data is collected or inferred?
2. Why is each data element needed?
3. Who receives it, and across which jurisdictions?
4. What could happen if it were inaccurate, exposed, or misused?
5. How long will it be retained?
6. How can individuals exercise their rights?
7. What lower-risk alternative could achieve a similar marketing objective?

High-risk activities—such as sensitive-category targeting, location-based promotion, profiling children, or automated decisions with significant effects—normally require specialist legal and privacy review rather than informal approval.

3. Copyright, Licensing, and Content Ownership

Copyright generally protects original expression, subject to the law of the relevant jurisdiction. It may cover text, photography, illustrations, video, music, software, and distinctive creative works. Finding an image through a search engine does not establish permission to use it. Attribution alone may also be insufficient if a licence is required.

A distribution team should maintain an asset rights register containing:

• asset description and owner;
• licence type and permitted channels;
• territory and duration;
• commercial-use restrictions;
• required attribution;
• permitted edits and adaptations;
• expiry date and responsible owner.

Licences may be exclusive or non-exclusive, limited by territory, audience, medium, or time. Music licensing is often separate from image or video licensing. User-generated content requires particular care: a person’s posting of material publicly does not necessarily grant a brand permission to reproduce it in advertising.

A rights clearance workflow should occur before publication, not after content performs well. If rights cannot be verified, the safest choices are to obtain permission, replace the asset, or commission original work. Fair dealing, fair use, quotation, parody, and similar doctrines are jurisdiction-specific and fact-dependent; they should not be treated as blanket permissions for marketing reuse.

4. Accessibility and Inclusive Distribution

Accessibility means designing content so that people with differing sensory, cognitive, physical, and technological circumstances can perceive, understand, navigate, and act on it. It is both an ethical responsibility and, in many settings, a legal or procurement requirement.

Practical measures include:

• alternative text that conveys the purpose of meaningful images;
• captions and, where appropriate, transcripts for video and audio;
• sufficient colour contrast and information that does not rely on colour alone;
• readable typography and plain language;
• descriptive link text;
• keyboard-accessible forms and landing pages;
• warnings for flashing, distressing, or otherwise hazardous content;
• captions that identify relevant sound, not only spoken words.

Accessibility must cover the whole journey. An accessible social post leading to an inaccessible checkout page is not an accessible campaign. Testing should include automated tools, manual review, and, where possible, participation by people with relevant lived experience. Accessibility also improves searchability, comprehension, and usability for many people who do not identify as disabled.

Inclusive distribution extends beyond disability. Marketers should review cultural assumptions, language availability, representation, targeting exclusions, and the possibility that algorithmic optimisation will systematically underserve particular groups.

5. Advertising Disclosure and Persuasive Transparency

Audiences should be able to recognise commercial communication. Sponsored posts, influencer partnerships, affiliate links, native advertising, product placement, and employee advocacy can blur the boundary between editorial and promotional content. Clear labels such as “advertisement,” “sponsored,” or an equivalent locally understood disclosure are generally more transparent than vague terms or labels hidden after a “more” link.

Disclosure should be:

• prominent and easy to understand;
• presented in the same language as the content;
• visible before or at the point of endorsement;
• retained when content is reposted or reformatted;
• appropriate to the medium and audience.

An organisation is responsible not only for its own post but also for the instructions, contracts, and monitoring of agencies and influencers acting on its behalf. Influencers should not be encouraged to make claims they cannot substantiate or to conceal a material relationship.

Claims about health, finance, environmental performance, safety, performance superiority, or scarcity require evidence proportionate to their potential impact. “Green” or “ethical” claims are especially vulnerable to vagueness and selective presentation. A claim may be technically true while creating a misleading overall impression; review must therefore consider implication as well as literal wording.

6. Brand Safety and Platform Policy

Brand safety is the management of contexts in which a brand’s content appears or with which it becomes associated. Risks include extremist material, hate speech, sexual exploitation, graphic violence, misinformation, illegal activity, unsafe products, and content that conflicts with organisational values. Brand suitability is broader: content may be legal and permissible but still inappropriate for a particular brand or audience.

Platform policies establish rules concerning advertising, targeting, intellectual property, misinformation, political content, prohibited goods, and user safety. They are not identical across platforms and may change quickly. A campaign approved on one platform may be rejected or restricted on another.

Governance controls include:

1. a documented brand-safety taxonomy;
2. exclusion lists and category controls;
3. pre-publication review for high-risk topics;
4. monitoring of placement and comments;
5. escalation routes for harmful adjacency or policy violations;
6. records of takedown, correction, and appeal decisions;
7. periodic review of suppliers and platform settings.

Overly broad blocking can create its own ethical problem. For example, automatically excluding all content associated with a health condition, minority group, or social issue may suppress legitimate information and reinforce inequity. Controls should be proportionate and reviewed for unintended effects.

7. Misinformation, Disinformation, and Content Integrity

Misinformation is inaccurate information shared without necessarily intending to deceive. Disinformation is deliberately false or misleading information distributed to deceive or gain advantage. Malinformation uses genuine information in a harmful or misleading context.

Content marketers may contribute to these problems through careless amplification, sensational headlines, unverified statistics, manipulated images, fabricated testimonials, or false urgency. A responsible verification process should identify the source, date, evidence, context, and intended meaning of important claims. Subject-matter experts should review high-stakes topics.

When an error occurs, a correction should be timely, prominent, specific, and distributed through substantially the same channels as the original claim. Quietly editing a high-reach post may preserve a misleading impression. Organisations should preserve an incident record so that recurring causes can be addressed.

The pressure to publish quickly does not remove the duty to verify. A useful distinction is between speed-sensitive content, where delay may reduce relevance, and harm-sensitive content, where publication requires a higher evidence threshold. Public health, elections, financial advice, safety, and allegations about identifiable people warrant particular caution.

8. Algorithmic Bias and Audience Selection

Platforms and marketing systems use algorithms to rank, recommend, target, optimise, and measure content. Bias can arise from historical data, proxy variables, unequal representation, objective functions, feedback loops, or human choices embedded in system design.

A system may produce discriminatory outcomes even without using protected attributes directly. Location, language, device type, purchasing history, or inferred interests can act as proxies. Optimising for low cost per click may preferentially deliver advertisements to groups that are cheaper to reach rather than groups that should fairly receive the opportunity.

A practical bias review asks:

• Which groups are included or excluded?
• Are delivery and conversion rates materially different across groups?
• Could the objective reward harmful or discriminatory behaviour?
• Are proxy variables present?
• Can a human review or challenge the outcome?
• Is the audience category understandable to the people affected?

Where legally and ethically appropriate, teams can compare reach, frequency, engagement, conversion, rejection, and complaint patterns across relevant groups. Numerical parity is not always the correct objective, but unexplained disparities should trigger investigation. Human oversight must be meaningful: a reviewer needs authority, information, time, and a realistic ability to change the outcome.

9. Artificial Intelligence and Transparency

Generative AI can assist with ideation, drafting, translation, segmentation, image creation, optimisation, and customer interaction. It can also reproduce bias, invent facts, expose confidential information, infringe rights, imitate individuals, and produce content that audiences mistake for human testimony or documentary evidence.

A responsible AI governance policy should specify:

• approved and prohibited uses;
• information that must not be entered into external systems;
• human review requirements;
• fact-checking and source verification;
• copyright and likeness permissions;
• disclosure rules for synthetic or materially altered content;
• record-keeping and version control;
• incident reporting and model or vendor review.

Transparency should be proportionate to the audience’s need to understand the content. A minor spelling suggestion does not require the same disclosure as a synthetic news-style video depicting a real person. If an audience could reasonably believe that a fabricated person, event, endorsement, or experience is real, disclosure is particularly important.

AI-generated content should not be treated as evidence merely because it is fluent or visually convincing. Organisations should preserve provenance information where feasible and ensure that human accountability remains clear. “The model produced it” is not an adequate explanation for a harmful campaign.

10. Governance and Risk Management

Governance is the system by which decisions are directed, controlled, documented, and reviewed. It assigns accountability rather than assuming that everyone is responsible and therefore no one is responsible.

A practical content governance model includes four levels:

A RACI matrix can clarify who is Responsible, Accountable, Consulted, and Informed for tasks such as rights clearance, privacy review, crisis response, and final approval. High-risk campaigns should have a named accountable decision-maker and a documented rationale.

Risk can be prioritised qualitatively using likelihood and impact. A simple risk register records the risk, cause, affected parties, existing controls, owner, treatment, and review date. Numerical scoring may support discussion, but it should not create false precision. A low-frequency risk with catastrophic consequences may deserve more attention than a frequent minor error.

Typical treatments are to avoid the activity, reduce likelihood, reduce impact, transfer part of the risk through a contract or insurance, accept it with authority, or prepare a response. Monitoring should include complaints, policy warnings, unusual engagement, misinformation signals, data incidents, accessibility failures, and unexpected audience effects.

11. Channel Dependence and Resilience

Reliance on one platform creates strategic and ethical risk. An organisation may lose reach through an algorithm change, account suspension, policy revision, acquisition, technical outage, or rising media costs. Dependence can also weaken the organisation’s direct relationship with its audience.

Resilience involves:

• maintaining owned channels such as a website, email list, or customer community;
• obtaining consent for direct communication rather than exporting audiences improperly;
• retaining content and audience records lawfully;
• using more than one suitable distribution route;
• documenting platform dependencies and exit plans;
• avoiding strategies that require opaque or unstable tactics.

Diversification does not mean using every channel. It means avoiding a critical single point of failure while preserving audience relevance and governance quality.

12. Cookieless Measurement and Responsible Attribution

The decline of third-party cookies and increasing privacy restrictions are changing measurement. Marketers are moving toward first-party data, consented analytics, contextual placement, aggregated reporting, clean rooms, platform experiments, and privacy-preserving technologies. These approaches have different strengths and limitations.

Attribution is not the same as causation. A platform may receive credit for a conversion because it recorded an exposure or click, while the conversion would have occurred anyway. Last-click attribution is easy to understand but can undervalue earlier influences and overvalue channels that appear late in the journey.

More responsible measurement combines several methods:

• direct measurement with clear consent and retention limits;
• controlled experiments or holdout groups where practical;
• incrementality analysis to estimate additional outcomes caused by a campaign;
• aggregated brand and sales indicators;
• qualitative evidence from customers and frontline teams.

Measurement quality should be judged not only by precision but also by privacy, transparency, representativeness, and decision usefulness. A highly detailed dashboard built on biased or non-consensual data is not a mark of analytical maturity.

13. Emerging Decentralised and Immersive Channels

Decentralised channels may include blockchain-based communities, token-gated experiences, decentralised social networks, and user-controlled identity systems. They may offer portability, community governance, or greater control over digital assets, but they can also introduce irreversible transactions, pseudonymity, scams, volatile incentives, unclear moderation, and difficult routes for deletion or redress.

Immersive channels include augmented reality, virtual reality, spatial computing, and virtual worlds. They may enable demonstrations, events, training, and participatory storytelling. New risks include biometric or behavioural data collection, intense emotional influence, harassment, identity impersonation, motion or sensory harms, and unclear boundaries between play, social interaction, and advertising.

Before entering an emerging channel, a marketer should ask:

1. What data is collected by the device, platform, and brand?
2. Who controls identity, assets, records, and moderation?
3. Can users understand and withdraw from the experience?
4. What safeguards protect minors and vulnerable users?
5. How are commercial messages disclosed in the environment?
6. What happens when the platform closes or the asset becomes inaccessible?

Novelty is not a sufficient strategic reason to participate. The channel should solve a real audience or business problem and pass the same standards of consent, safety, accessibility, truthfulness, and accountability as established media.

Applied Case: The “ClearPath” Wellness Campaign

This is an illustrative fictional case.

Background

ClearPath, a national wellness provider, planned a campaign promoting a mobile programme for stress management. The team intended to use short videos, influencer endorsements, location-based advertising, lookalike audiences, and an AI chatbot. The initial brief promised rapid growth through personalised targeting.

Problem

A review found that the proposed audience model inferred sensitive health interests from browsing behaviour. The influencer contract did not require clear sponsorship disclosure. Several video claims suggested that the programme could “prevent anxiety,” although the evidence supported only general wellbeing benefits. The chatbot generated unsupported advice, and the landing page lacked captions and keyboard support. The media agency also planned to place advertisements across a broad network with limited brand-safety controls.

Analysis

The campaign created interconnected risks rather than isolated compliance issues. Sensitive inference threatened privacy and could stigmatise people. Unsupported health claims created potential harm. Hidden sponsorship weakened trust. The AI system could provide unsafe advice. Inaccessible content excluded users, while broad placement created reputational risk. The growth objective had encouraged the team to optimise reach before validating the quality and safety of the experience.

Approach and solution

ClearPath replaced inferred health targeting with contextual placements and consented first-party segments based on explicit programme interactions. It commissioned an evidence review of every health claim and changed the message to describe supported benefits without implying treatment or prevention. Influencers received mandatory disclosure instructions and a monitoring process.

The chatbot was restricted to navigation, programme information, and signposting to qualified support. It could not diagnose or provide personalised clinical advice. Human reviewers tested its responses using predefined safety scenarios. The campaign team added captions, transcripts, accessible forms, and a non-video information route. Media buying used exclusion categories, placement monitoring, and an escalation procedure.

A governance group approved the revised campaign after reviewing the privacy assessment, rights register, claim substantiation, accessibility test, AI controls, and incident plan.

Outcome

The revised campaign launched more slowly and reached fewer people initially. However, complaint rates were lower, completion of the accessible sign-up journey improved, and the organisation avoided unsupported health claims. The campaign produced a reusable governance checklist for later work.

Learning points

1. Ethical controls should be designed at the briefing stage.
2. Personalisation is not automatically valuable if it relies on sensitive inference.
3. AI should be bounded by purpose, evidence, and human accountability.
4. Accessibility is part of campaign performance, not an optional final adjustment.
5. A slower, better-governed campaign can produce more durable trust and organisational resilience.

Challenges and Limitations

Ethical governance is difficult because laws differ, platforms are opaque, and evidence is incomplete. Excessive approval can slow legitimate communication, while weak approval can expose audiences and organisations to serious harm. Privacy-enhancing measurement may reduce granular attribution, creating pressure to return to intrusive tracking. Automated brand-safety tools may over-block legitimate speech or miss coded harmful content. Diversity metrics can reveal disparities without explaining their causes.

The appropriate response is not to eliminate all uncertainty. It is to make uncertainty visible, allocate responsibility, test assumptions, involve affected perspectives, and revise decisions when evidence changes. Governance should be proportionate to risk and capable of learning rather than merely producing paperwork.

Best-Practice Checklist

Before distribution, confirm that the team has:

• a lawful and understandable data basis;
• necessary consent and a withdrawal route where applicable;
• verified rights for every asset;
• accessible content and destination experiences;
• clear advertising and sponsorship disclosure;
• substantiated material claims;
• appropriate targeting and bias checks;
• platform and brand-safety controls;
• human review for high-risk AI outputs;
• a correction, takedown, and incident plan;
• a measurement approach that respects privacy;
• channel diversification and an exit plan;
• a named accountable owner and retained decision record.

Chapter Summary

Ethical content distribution treats audiences as people with rights and interests, not merely as sources of attention or data. Privacy requires purpose limitation, minimisation, security, and meaningful consent. Copyright and licensing controls protect creators and organisations from unauthorised reuse. Accessibility ensures that content and journeys can be used by people with differing abilities. Advertising disclosure and evidence-based claims preserve persuasive honesty.

Brand safety and platform governance reduce harmful adjacency and policy violations, while misinformation controls protect content integrity. Algorithmic bias requires attention to proxies, disparate outcomes, objectives, and meaningful human oversight. AI introduces opportunities for efficiency but requires limits, verification, provenance, and proportionate transparency. Channel diversification, privacy-conscious measurement, and careful evaluation of decentralised and immersive environments support resilience.

Effective governance assigns accountability, documents decisions, monitors outcomes, and enables correction. Responsible practice is therefore not a final compliance check; it is a design principle embedded throughout briefing, creation, distribution, measurement, and review.

Key Terms

Accessibility: Designing content and experiences so people with differing abilities can use them.

Algorithmic bias: Systematic unfairness arising from data, design, objectives, or implementation of an algorithmic system.

Brand safety: Measures that reduce harmful or inappropriate associations between a brand and surrounding content.

Consent: A clear, informed, and voluntary indication of agreement to a specified activity where required.

Copyright: Legal protection for qualifying original creative expression.

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

Disinformation: Deliberately false or misleading information distributed to deceive.

First-party data: Data collected directly by an organisation through its relationship with an audience.

Governance: Structures and processes for directing, controlling, documenting, and reviewing decisions.

Incrementality: The additional outcome attributable to an activity beyond what would otherwise have occurred.

Malinformation: Genuine information used or presented in a harmful or misleading context.

Misinformation: Inaccurate information shared without necessarily intending to deceive.

Personal data: Information relating to an identified or identifiable person, subject to applicable law.

Purpose limitation: Using data only for specified and legitimate purposes.

Risk register: A documented record of risks, causes, impacts, controls, owners, and treatments.

Sponsorship disclosure: A clear indication that content involves a commercial relationship.

Third-party data: Data obtained from an external organisation rather than directly from the audience.

Transparency: Making relevant practices, relationships, decisions, and limitations understandable to affected people.

Review Questions

Short Answer Questions

1. What is the difference between first-party and third-party data?
2. Why is attribution alone not always sufficient for copyright compliance?
3. Identify two features of meaningful consent.
4. What is brand safety?
5. Why should AI-generated marketing content receive human review?

Long Answer Questions

1. Explain how privacy, accessibility, and advertising disclosure contribute to audience trust.
2. Describe a governance structure for a high-risk content campaign.
3. Discuss how algorithmic bias can arise even when protected characteristics are not directly used.
4. Compare the advantages and limitations of platform-based measurement and privacy-conscious measurement.
5. Evaluate the opportunities and risks of immersive or decentralised distribution channels.

Analytical / Application Questions

1. A retailer wants to target people inferred to be experiencing financial difficulty with urgent credit advertisements. Analyse the ethical, privacy, and brand risks, and propose a safer alternative.
2. A video campaign receives high engagement after spreading an unsupported health claim. Design a response covering verification, correction, platform action, stakeholder communication, and governance learning.
3. An organisation depends on one social platform for 80% of its leads. Develop a channel-resilience plan that protects consent, measurement quality, and audience relationships.

Multiple Choice Questions with Answers

Question 1

Which practice best reflects data minimisation?

A. Collecting every available customer attribute for possible future use

B. Collecting only the data necessary for the stated campaign purpose

C. Purchasing a large external dataset to improve personalisation

D. Keeping all campaign data indefinitely

Correct Answer: B. Collecting only the data necessary for the stated campaign purpose

Question 2

Which statement about advertising disclosure is most accurate?

A. Disclosure is unnecessary when an influencer genuinely likes the product

B. A disclosure can be hidden after several paragraphs of content

C. A commercial relationship should be made clear and prominent to the audience

D. Disclosure is required only for television advertisements

Correct Answer: C. A commercial relationship should be made clear and prominent to the audience

Question 3

What is the main purpose of a rights register?

A. To forecast campaign conversion rates

B. To document ownership, permissions, restrictions, and expiry dates for assets

C. To rank influencers by popularity

D. To replace accessibility testing

Correct Answer: B. To document ownership, permissions, restrictions, and expiry dates for assets

Question 4

Which example is most clearly disinformation?

A. An honest mistake in a quickly written post

B. An outdated statistic repeated without checking

C. A deliberately fabricated testimonial created to deceive consumers

D. A customer misunderstanding a product description

Correct Answer: C. A deliberately fabricated testimonial created to deceive consumers

Question 5

Why can algorithmic bias occur without directly using protected characteristics?

A. Algorithms cannot process numerical data

B. Proxy variables and historical patterns can produce unequal outcomes

C. Human review always removes bias

D. Platforms distribute every advertisement equally

Correct Answer: B. Proxy variables and historical patterns can produce unequal outcomes

Question 6

Which is the strongest control for a high-risk AI chatbot in a wellness campaign?

A. Allowing it to diagnose users using persuasive language

B. Removing all human review to improve speed

C. Restricting its purpose, verifying outputs, and providing human escalation

D. Disclosing only that the campaign uses digital technology

Correct Answer: C. Restricting its purpose, verifying outputs, and providing human escalation

Question 7

What is a major risk of relying on a single social platform?

A. The organisation automatically owns the platform’s audience data

B. A policy change or account suspension can severely disrupt distribution

C. The organisation no longer needs an owned website

D. Content becomes immune to misinformation

Correct Answer: B. A policy change or account suspension can severely disrupt distribution

Question 8

Why is last-click attribution limited?

A. It cannot record any digital interactions

B. It may assign credit to the final interaction even when other influences contributed

C. It always requires third-party cookies

D. It measures only customer satisfaction

Correct Answer: B. It may assign credit to the final interaction even when other influences contributed

Question 9

Which action best supports accessibility in video content?

A. Using colour alone to distinguish speakers

B. Adding captions and providing an appropriate transcript

C. Reducing contrast to make the design subtle

D. Placing all important information in background music

Correct Answer: B. Adding captions and providing an appropriate transcript

Question 10

What should an organisation do when it discovers a widely distributed unsupported claim?

A. Delete it silently and publish a different message

B. Leave it online because engagement was high

C. Issue a timely, prominent correction and review the process that caused the error

D. Blame the platform and take no further action

Correct Answer: C. Issue a timely, prominent correction and review the process that caused the error