Best Practices

Ethics & Responsible Use

Ethical considerations in AI

The prompts you write shape how AI behaves. A well-crafted prompt can educate, assist, and empower. A careless one can deceive, discriminate, or cause harm. As prompt engineers, we're not just users—we're designers of AI behavior, and that comes with real responsibility.

This chapter isn't about rules imposed from above. It's about understanding the impact of our choices and building habits that lead to AI use we can be proud of.

Why This Matters

AI amplifies whatever it's given. A biased prompt produces biased outputs at scale. A deceptive prompt enables deception at scale. The ethical implications of prompt engineering grow with every new capability these systems gain.

Ethical Foundations

Every decision in prompt engineering connects to a few core principles:

Honesty

Don't use AI to deceive people or create misleading content

No fake reviews, impersonation, or manufactured 'evidence'

Fairness

Actively work to avoid perpetuating biases and stereotypes

Test prompts across demographics, request diverse perspectives

Transparency

Be clear about AI involvement when it matters

Disclose AI assistance in published work, professional contexts

Privacy

Protect personal information in prompts and outputs

Anonymize data, avoid including PII, understand data policies

Safety

Design prompts that prevent harmful outputs

Build in guardrails, test for edge cases, handle refusals gracefully

Accountability

Take responsibility for what your prompts produce

Review outputs, fix issues, maintain human oversight

The Prompt Engineer's Role

You have more influence than you might realize:

  • What AI produces: Your prompts determine the content, tone, and quality of outputs
  • How AI interacts: Your system prompts shape personality, boundaries, and user experience
  • What safeguards exist: Your design choices determine what the AI will and won't do
  • How mistakes are handled: Your error handling determines whether failures are graceful or harmful

Avoiding Harmful Outputs

The most fundamental ethical obligation is preventing your prompts from causing harm.

Categories of Harmful Content

Violence & Harm

Instructions that could lead to physical harm

Weapons creation, self-harm, violence against others

Illegal Activities

Content that facilitates breaking laws

Fraud schemes, hacking instructions, drug synthesis

Harassment & Hate

Content targeting individuals or groups

Discriminatory content, doxxing, targeted harassment

Misinformation

Deliberately false or misleading content

Fake news, health misinformation, conspiracy content

Privacy Violations

Exposing or exploiting personal information

Revealing private data, stalking assistance

Exploitation

Content that exploits vulnerable individuals

CSAM, non-consensual intimate content, scams targeting elderly

What is CSAM?

CSAM stands for Child Sexual Abuse Material. Creating, distributing, or possessing such content is illegal worldwide. AI systems must never generate content depicting minors in sexual situations, and responsible prompt engineers actively build safeguards against such misuse.

Building Safety Into Prompts

When building AI systems, include explicit safety guidelines:

Safety-First System Prompt

A template for building safety guidelines into your AI systems.

You are a helpful assistant for ${purpose}.

## SAFETY GUIDELINES

**Content Restrictions**:
- Never provide instructions that could cause physical harm
- Decline requests for illegal information or activities
- Don't generate discriminatory or hateful content
- Don't create deliberately misleading information

**When You Must Decline**:
- Acknowledge you understood the request
- Briefly explain why you can't help with this specific thing
- Offer constructive alternatives when possible
- Be respectful—don't lecture or be preachy

**When Uncertain**:
- Ask clarifying questions about intent
- Err on the side of caution
- Suggest the user consult appropriate professionals

Now, please help the user with: ${userRequest}

The Intent vs. Impact Framework

Not every sensitive request is malicious. Use this framework for ambiguous cases:

Ethical Edge Case Analyzer

Work through ambiguous requests to determine the appropriate response.

I received this request that might be sensitive:

"${sensitiveRequest}"

Help me think through whether and how to respond:

**1. Intent Analysis**
- What are the most likely reasons someone would ask this?
- Could this be legitimate? (research, fiction, education, professional need)
- Are there red flags suggesting malicious intent?

**2. Impact Assessment**
- What's the worst case if this information is misused?
- How accessible is this information elsewhere?
- Does providing it meaningfully increase risk?

**3. Recommendation**
Based on this analysis:
- Should I respond, decline, or ask for clarification?
- If responding, what safeguards should I include?
- If declining, how should I phrase it helpfully?

Addressing Bias

AI models inherit biases from their training data—historical inequities, representation gaps, cultural assumptions, and linguistic patterns. As prompt engineers, we can either amplify these biases or actively counteract them.

How Bias Manifests

Default Assumptions

The model assumes certain demographics for roles

Doctors defaulting to male, nurses to female

Stereotyping

Reinforcing cultural stereotypes in descriptions

Associating certain ethnicities with specific traits

Representation Gaps

Some groups are underrepresented or misrepresented

Limited accurate information about minority cultures

Western-Centric Views

Perspectives skewed toward Western culture and values

Assuming Western norms are universal

Testing for Bias

Bias Detection Test

Use this to test your prompts for potential bias issues.

I want to test this prompt for bias:

"${promptToTest}"

Run these bias checks:

**1. Demographic Variation Test**
Run the prompt with different demographic descriptors (gender, ethnicity, age, etc.) and note any differences in:
- Tone or respect level
- Assumed competence or capabilities
- Stereotypical associations

**2. Default Assumption Check**
When demographics aren't specified:
- What does the model assume?
- Are these assumptions problematic?

**3. Representation Analysis**
- Are different groups represented fairly?
- Are any groups missing or marginalized?

**4. Recommendations**
Based on findings, suggest prompt modifications to reduce bias.

Mitigating Bias in Practice

Bias-prone prompt

Describe a typical CEO.

Bias-aware prompt

Describe a CEO. Vary demographics across examples, and avoid defaulting to any particular gender, ethnicity, or age.

Transparency and Disclosure

When should you tell people AI was involved? The answer depends on context—but the trend is toward more disclosure, not less.

When Disclosure Matters

Published Content

Articles, posts, or content shared publicly

Blog posts, social media, marketing materials

Consequential Decisions

When AI outputs affect people's lives

Hiring recommendations, medical info, legal guidance

Trust Contexts

Where authenticity is expected or valued

Personal correspondence, testimonials, reviews

Professional Settings

Workplace or academic environments

Reports, research, client deliverables

How to Disclose Appropriately

Hidden AI involvement

Here's my analysis of the market trends...

Transparent disclosure

I used AI tools to help analyze the data and draft this report. All conclusions have been verified and edited by me.

Common disclosure phrases that work well:

  • "Written with AI assistance"
  • "AI-generated first draft, human edited"
  • "Analysis performed using AI tools"
  • "Created with AI, reviewed and approved by [name]"

Privacy Considerations

Every prompt you send contains data. Understanding where that data goes—and what shouldn't be in it—is essential.

What Never Belongs in Prompts

Personal Identifiers

Names, addresses, phone numbers, SSNs

Use [CUSTOMER] instead of 'John Smith'

Financial Data

Account numbers, credit cards, income details

Describe the pattern, not the actual numbers

Health Information

Medical records, diagnoses, prescriptions

Ask about conditions generally, not specific patients

Credentials

Passwords, API keys, tokens, secrets

Never paste credentials—use placeholders

Private Communications

Personal emails, messages, confidential docs

Summarize the situation without quoting private text

Safe Data Handling Pattern

Unsafe: Contains PII

Summarize this complaint from John Smith at 123 Main St, Anytown about order #12345: 'I ordered on March 15 and still haven't received...'

Safe: Anonymized

Summarize this customer complaint pattern: A customer ordered 3 weeks ago, hasn't received their order, and has contacted support twice without resolution.
What is PII?

PII stands for Personally Identifiable Information—any data that can identify a specific individual. This includes names, addresses, phone numbers, email addresses, Social Security numbers, financial account numbers, and even combinations of data (like job title + company + city) that could identify someone. When prompting AI, always anonymize or remove PII to protect privacy.

PII Scrubber

Use this to identify and remove sensitive information before including text in prompts.

Review this text for sensitive information that should be removed before using it in an AI prompt:

"${textToReview}"

Identify:
1. **Personal Identifiers**: Names, addresses, phone numbers, emails, SSNs
2. **Financial Data**: Account numbers, amounts that could identify someone
3. **Health Information**: Medical details, conditions, prescriptions
4. **Credentials**: Any passwords, keys, or tokens
5. **Private Details**: Information someone would reasonably expect to be confidential

For each item found, suggest how to anonymize or generalize it while preserving the information needed for the task.

Authenticity and Deception

There's a difference between using AI as a tool and using AI to deceive.

The Legitimacy Line

Legitimate Uses

AI as a tool to enhance your work

Drafting, brainstorming, editing, learning

Gray Areas

Context-dependent, requires judgment

Ghostwriting, templates, automated responses

Deceptive Uses

Misrepresenting AI work as human-original

Fake reviews, academic fraud, impersonation

Key questions to ask:

  • Would the recipient expect this to be original human work?
  • Am I gaining unfair advantage through deception?
  • Would disclosure change how the work is received?

Synthetic Media Responsibility

Creating realistic depictions of real people—whether images, audio, or video—carries special obligations:

  • Never create realistic depictions without consent
  • Always label synthetic media clearly
  • Consider potential for misuse before creating
  • Refuse to create non-consensual intimate imagery

Responsible Deployment

When building AI features for others to use, your ethical obligations multiply.

Pre-Deployment Checklist

Deployment Readiness0/8

Human Oversight Principles

High-Stakes Review

Humans review decisions that significantly affect people

Hiring, medical, legal, financial recommendations

Error Correction

Mechanisms exist to catch and fix AI mistakes

User feedback, quality sampling, appeals process

Continuous Learning

Insights from issues improve the system

Post-mortems, prompt updates, training improvements

Override Capability

Humans can intervene when AI fails

Manual review queues, escalation paths

Special Context Guidelines

Some domains require extra care due to their potential for harm or the vulnerability of those involved.

Healthcare

Medical Context Disclaimer

Template for AI systems that might receive health-related queries.

You are an AI assistant. When users ask about health or medical topics:

**Always**:
- Recommend consulting a qualified healthcare provider for personal medical decisions
- Provide general educational information, not personalized medical advice
- Include disclaimers that you cannot diagnose conditions
- Suggest emergency services (911) for urgent situations

**Never**:
- Provide specific diagnoses
- Recommend specific medications or dosages
- Discourage someone from seeking professional care
- Make claims about treatments without noting uncertainty

User question: ${healthQuestion}

Respond helpfully while following these guidelines.

Legal and Financial

These domains have regulatory implications and require appropriate disclaimers:

Legal Queries

Provide general information, not legal advice

"This is general information. For your specific situation, consult a licensed attorney."

Financial Queries

Educate without providing personal financial advice

"This is educational. Consider consulting a financial advisor for your situation."

Jurisdiction Awareness

Laws vary by location

"Laws differ by state/country. Verify requirements for your jurisdiction."

Children and Education

Age-Appropriate Content

Ensure outputs are suitable for the age group

Filter mature content, use appropriate language

Academic Integrity

Support learning, don't replace it

Explain concepts rather than writing essays for students

Safety First

Extra protection for vulnerable users

Stricter content filters, no personal data collection

Self-Assessment

Before deploying any prompt or AI system, run through these questions:

Ethical Self-Check0/7

A user asks your AI system how to 'get rid of someone who's bothering them.' What's the most appropriate response strategy?