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Tag: AI in E-commerce

  • The Ethics of AI in E-Commerce: Transparency, Consent, and Bias

    Imagine you’re scrolling through a shopping app. A curated Look catches your eye. It’s stylish, affordable, and recommended “just for you.” Feels like magic, right? Now imagine if that magic came at the cost of your privacy, consent, or even fairness.

    As AI becomes deeply embedded in e-commerce—from personalization to pricing and fulfillment—it also raises serious ethical questions. How is your data used? Who decides what you see? Is the algorithm treating every user equally?

    This isn’t just a tech debate—it’s a trust issue.

    At Glance.com, we believe AI in e-commerce must be ethical by design. That means transparent data practices, user-informed consent, inclusive algorithms, and accountability at every layer.

    👉 Learn how AI ethics align with our broader intelligent commerce strategy: Explore the AI in E-Commerce Guide

    1. Why AI Ethics Matter in E-Commerce

    AI is invisible—but its impact isn’t.

    It influences:

    • What products you see

    • What price you pay

    • What content you trust

    • Whether you feel seen—or ignored

    Unethical AI can:

    • Profile users without their consent

    • Amplify cultural or gender bias in product targeting

    • Obscure decision logic behind credit offers or promotions

    • Create opaque personalization loops

    According to PwC, 84% of consumers say they will walk away from a brand that doesn’t prioritize transparency and fairness in its AI systems.1

    E-commerce isn’t just about selling. It’s about shaping digital behavior. And that gives platforms an ethical responsibility to get it right.

    2. Transparency: Making the Algorithm Understandable

    What is the algorithm doing? Why did it recommend this Look and not that one? Did a user’s price change based on location, past clicks, or something more problematic?

    AI doesn’t need to be a black box.

    Transparency means:

    • Showing users why they’re seeing specific recommendations

    • Disclosing use of AI in personalization or credit scoring

    • Explaining pricing or promotion logic

    • Letting users opt out of algorithmic filtering

    At Glance, we aim to offer “Why this Look?” insights—so users understand that recommendations are based on style preferences, saved Looks, and product interactions.

    The EU AI Act emphasizes the need for explainability in consumer-facing AI tools—especially in sensitive areas like credit, pricing, and identity modeling.2

    Transparency builds trust—and trust builds loyalty.

    3. Consent: Asking Before Personalizing

    Users shouldn’t be tracked without knowing it. And they shouldn’t have to dig through five pages of legalese to understand what’s being collected.

    Ethical AI demands clear, upfront consent.

    That means:

    • Explicit opt-ins for data collection, styling profiles, or personalization features

    • Layered consent (e.g., separate opt-ins for AI Looks vs. predictive pricing)

    • The ability to revoke consent easily

    • Providing value in exchange for consent—like better recommendations or faster checkout

    At Glance, users must actively consent to uploading selfies, generating AI Looks, or storing styling preferences. No facial recognition, no hidden profiling.

    Cisco’s 2023 Privacy Benchmark Report found that 94% of users expect brands to be transparent about AI data usage, and 76% say they won’t share data without clear benefit.3

    Consent isn’t just legal. It’s relational.

    4. Bias in Algorithms: Who Gets Seen, What Gets Sold

    AI models are only as unbiased as the data they’re trained on.

    Common sources of bias include:

    • Underrepresentation of certain body types or skin tones in visual AI

    • Prioritizing products based on assumed gender roles

    • Socioeconomic targeting that reinforces inequality

    • Cultural stereotypes in recommendations

    For example, if a system sees that certain demographics engage more with high-value items, it might unfairly deprioritize budget-friendly options for others.

    At Glance, our AI Look generation is trained on diverse body types, skin tones, and style identities—ensuring representation across the scroll.

    MIT Media Lab found that facial recognition tools had up to 34% error rates on darker-skinned women, compared to 1% for lighter-skinned men—showing how bias can invisibly shape digital outcomes.4

    Fairness is not automatic. It must be intentionally coded.

    5. Pricing Ethics and AI-Driven Discrimination

    AI can customize prices—but should it?

    While dynamic pricing improves business efficiency, it risks becoming discriminatory when based on:

    • Location and ZIP code profiling

    • Device used (e.g., higher prices for iPhone users)

    • Predicted urgency or intent

    • Inferred income based on past purchases

    Ethical pricing practices mean:

    • No hidden discrimination

    • No manipulation of need-based shoppers

    • Transparent explanation for personalized offers or discounts

    At Glance, pricing is uniform unless explicitly tied to visible campaigns or opt-in loyalty behavior—never inferred user worth.

    A Brookings study warns that algorithmic pricing without guardrails can exacerbate economic inequality—especially in regions with poor digital literacy.5

    Personalization should feel like empowerment—not exploitation.

    6. Data Privacy and Long-Term Profiling

    Just because you can store data forever, doesn’t mean you should.

    Ethical AI in e-commerce includes:

    • Minimal data retention for behavioral modeling

    • Deletion timelines aligned with usage value

    • Zero sale or trade of personal data

    • Regular audits of data footprint per user

    Glance practices minimal data retention. If a user hasn’t interacted with their profile in 90 days, styling preferences are archived. Selfies used for AI avatars are deleted unless consent is renewed.

    India’s Digital Personal Data Protection Act (DPDPA) mandates purpose limitation, data minimization, and user control over AI use of personal data.6

    Data belongs to the user. Brands are just trusted stewards.

    7. Accountability and Human-in-the-Loop Design

    No AI is perfect. That’s why humans must stay involved.

    Accountable AI means:

    • Audit trails for algorithmic decisions

    • Human override for complex or sensitive issues

    • Channels for user appeals or challenges

    • Feedback loops to improve models over time

    Glance uses human moderation for all flagged Looks, reviews, or style prompts—especially when behavior seems algorithmically questionable.

    Google’s AI Principles state that all AI systems should be subject to meaningful human direction and review—especially in areas involving user rights or well-being.7

    Automation without accountability is not efficiency. It’s abdication.

    Conclusion: Designing AI for Trust, Not Just Conversion

    AI can do amazing things in e-commerce. It can recommend, optimize, personalize, and scale. But it must do these things with transparency, consent, fairness, and empathy.

    At Glance.com, we see ethical AI not as a checkbox—but as our foundation. It’s how we build trust, inspire loyalty, and ensure that innovation serves everyone, not just the few.

    👉 Want to explore how ethical AI connects to personalization, discovery, returns, and immersive shopping? Dive into the AI in E-Commerce Guide

    Because the future of commerce isn’t just smarter. It’s more human.

    Footnotes

    1. https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/artificial-intelligence.html ↩

    2. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence ↩

    3. https://www.cisco.com/c/en/us/about/newsroom/newsroom-presskit/privacy-benchmark-study-2023.html ↩

    4. https://www.media.mit.edu/projects/gender-shades/overview/ ↩

    5. https://www.brookings.edu/articles/how-algorithms-can-lead-to-unfair-pricing-in-e-commerce/ ↩

    6. https://www.meity.gov.in/writereaddata/files/Draft_DPDP_Bill_2022.pdf ↩

    7. https://ai.google/principles/ ↩

     

  • Prompt Engineering 101: Getting Better Results from AI Tools

    In today’s AI-powered world, the difference between mediocre and exceptional results often comes down to how you communicate with artificial intelligence. Welcome to the art and science of prompt engineering—a skill that’s quickly becoming essential for anyone looking to leverage AI tools effectively. Whether you’re using generative AI for writing, image creation, or data analysis, mastering prompt engineering basics can dramatically improve your outcomes.

    What is Prompt Engineering?

    Prompt engineering is the practice of crafting inputs (prompts) for AI systems to elicit the most useful, accurate, and relevant outputs. Think of it as learning to speak the language of AI—knowing which instructions, context, and constraints will help the system understand exactly what you’re looking for.

    Unlike traditional programming, where you write explicit code to achieve specific results, prompt engineering involves guiding AI through natural language. It’s less technical but requires creativity, precision, and an understanding of how AI models interpret and respond to different types of inputs.

    Why Prompt Engineering Matters?

    Even the most advanced AI tools are only as good as the prompts they receive. Here’s why mastering this skill matters:

    • Efficiency: Get the results you want faster, with fewer iterations
    • Quality: Produce higher-quality outputs tailored to your specific needs
    • Consistency: Generate more reliable and predictable results
    • Creativity: Unlock the full creative potential of AI tools
    • Cost-effectiveness: Reduce token usage and processing time with precise prompts

    Core Principles of Effective Prompt Engineering

    1. Be Clear and Specific

    Vague prompts lead to vague results. The more specific your instructions, the better the AI can meet your expectations.

    Basic Prompt: “Write about climate change.”

    Improved Prompt: “Write a 300-word explanation of how rising sea levels from climate change are affecting coastal communities in Southeast Asia, including two specific examples and potential adaptation strategies.”

    The improved version provides clear parameters (length, focus, structure) that guide the AI toward a much more useful response.

    2. Provide Context and Background

    AI models lack real-world experience and the background knowledge we take for granted. Providing relevant context helps them generate more informed outputs.

    Basic Prompt: “Give me ideas for my presentation.”

    Improved Prompt: “I’m preparing a 10-minute presentation for healthcare professionals about recent advances in telemedicine. My audience has basic technical knowledge but varies in their familiarity with digital health platforms. Suggest 5 key points I should cover and how to make them engaging.”

    This context gives the AI crucial information about audience, purpose, and constraints.

    3. Use Structured Formatting

    Breaking down complex requests into structured components makes them easier for AI to process accurately.

    Basic Prompt: “Help me write a customer email.”

    Improved Prompt:

    Write a customer email following these guidelines:

    – Purpose: Apologize for a shipping delay and offer compensation

    – Tone: Professional but empathetic

    – Length: 150-200 words

    – Include: Specific explanation for delay, new delivery date, discount code details

    – Call-to-action: Ask customer to confirm receipt of email

    This structured approach ensures all requirements are addressed in the response.

    4. Leverage Role and Format Prompting

    Assigning a role to the AI or specifying a format can dramatically change the quality and style of outputs.

    Basic Prompt: “Tell me about quantum computing.”

    Improved Prompt: “As a university professor teaching an introductory course on computer science, explain quantum computing to first-year students who have basic knowledge of classical computing. Format your response as a lecture with an attention-grabbing introduction, 3 main concepts, illustrative analogies, and a conclusion that emphasizes why this field matters.”

    This technique guides not just the content but also how it’s presented.

    5. Iterate and Refine

    Prompt engineering is rarely a one-attempt process. The most effective approach involves treating it as an iterative dialogue.

    1. Start with a base prompt
    2. Evaluate the response
    3. Adjust your prompt based on what worked and what didn’t
    4. Try again with the refined prompt
    5. Repeat until you achieve your desired result

    Keep a record of particularly effective prompts for future reference.

    Advanced Prompt Engineering Techniques

    Chain-of-Thought Prompting

    This technique encourages AI to break down complex problems into logical steps.

    Example: “Solve this math word problem step by step, showing your reasoning at each stage: A train travels at 60 mph for 2 hours, then increases its speed to 80 mph for the next 1.5 hours. What is the total distance traveled?”

    By explicitly requesting step-by-step reasoning, you typically get more accurate results for problems requiring multi-stage thinking.

    Few-Shot Learning

    Provide examples of the pattern you want the AI to follow.

    Example:

    Translate these sentences from English to French:

     

    English: The cat is on the table.

    French: Le chat est sur la table.

     

    English: I would like to order dinner.

    French: J’aimerais commander le dîner.

     

    This technique is particularly effective when you need outputs in a very specific format or style.

    Negative Prompting

    Specify what you don’t want to see in the response.

    Example: “Write a creative short story about time travel. Do not include typical clichés like meeting historical figures or paradoxes where someone meets their younger self. Avoid predictable endings where everything returns to normal.”

    Negative prompting helps steer AI away from common or unwanted patterns.

    Common Prompt Engineering Mistakes to Avoid

    Being Too Vague

    Vague prompts force the AI to make assumptions about what you want, often leading to disappointing results.

    Overloading With Information

    While context is important, overwhelming the AI with irrelevant details can confuse the model and dilute the focus of the response.

    Asking for Multiple Unrelated Things

    Keeping prompts focused on one main task or closely related tasks yields better results than requesting multiple unrelated outputs in a single prompt.

    Neglecting to Specify Format

    Without format guidance, AI may deliver content that’s accurate but not structured in a way that serves your needs.

    Assuming Technical Knowledge

    Don’t assume the AI knows specialized terminology or concepts without explanation, especially for niche topics.

    Real-World Applications of Prompt Engineering

    Content Creation

    Well-engineered prompts can generate blog posts, marketing copy, creative writing, and more that match specific brand voices and achieve particular objectives.

    Visual Design

    For image generation tools, prompt engineering is crucial for creating visuals with the right style, elements, composition, and mood.

    Data Analysis

    When working with AI tools that analyze data, effective prompts can help extract meaningful insights and generate useful visualizations.

    Education

    Educators can use prompt engineering to create personalized learning materials, quizzes, and explanations tailored to different learning styles and levels.

    How Glance Uses Prompt Engineering to Enhance User Experiences?

    At Glance, prompt engineering is at the heart of delivering personalized content experiences across devices. Our AI-powered platforms like Glance TV and Glance AI utilize sophisticated prompt engineering behind the scenes to create engaging, relevant content for users.

    For instance, Glance AI’s ability to generate personalized AI looks is powered by carefully engineered prompts that transform user inputs (like selfies and style preferences) into stunning visual content. Similarly, Glance TV uses smart prompt engineering to deliver dynamic content that responds to user interests during idle screen time.

    For practical examples of effective prompts that can enhance your AI interactions, check out Glance’s Best Prompts Resource where we share techniques our experts use to achieve optimal results.

    Getting Started with Prompt Engineering

    1. Start With a Template

    Begin with basic prompt templates and customize them for your specific needs:

    I need [content type] about [topic] for [audience] that is [length] and emphasizes [key points]. The tone should be [desired tone], and it should include [specific elements].

    2. Experiment Systematically

    Change one aspect of your prompt at a time to see how it affects the output. This methodical approach helps you understand which elements have the biggest impact.

    3. Build a Prompt Library

    Save successful prompts as templates for future use. Categorize them by purpose (writing, brainstorming, analysis) for easy reference.

    4. Learn From the Community

    Follow prompt engineering communities online where people share techniques and examples. Learning from others can significantly accelerate your progress.

    5. Practice Regularly

    Like any skill, prompt engineering improves with practice. Set aside time to experiment with different approaches and analyze the results.

    The Future of Prompt Engineering

    As AI tools continue to evolve, prompt engineering is likely to become both more sophisticated and more accessible:

    • More specialized prompt techniques for different types of AI models
    • Prompt libraries and marketplaces where effective prompts can be shared and purchased
    • Visual prompt builders that help construct effective prompts without technical knowledge
    • Meta-prompting where AI helps refine your prompts to get better results

    Despite these advancements, understanding the fundamentals of how to communicate effectively with AI will remain an invaluable skill.

    Conclusion

    Prompt engineering is quickly becoming an essential literacy in our increasingly AI-powered world. By mastering the basics of writing better AI prompts, you can transform these powerful tools from interesting novelties into valuable allies that consistently deliver the results you need.

    Remember that effective prompt engineering combines both art and science—there’s room for creativity and experimentation alongside structured techniques and best practices. As you develop this skill, you’ll not only get better results from AI tools but also gain deeper insights into how these systems interpret and process information.

    Whether you’re a professional looking to incorporate AI into your workflow, a creator seeking to expand your capabilities, or simply curious about getting the most from these new technologies, investing time in learning prompt engineering basics will pay dividends in the quality and usefulness of your AI interactions.