Author: Dojo Editor

AI in Hiring: Fairness or Flaw?

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By Joe Scaggs | AI Design Ethics & Current Events
The promise of AI in hiring is efficiency—screening faster, scoring more objectively. But the reality often includes something more dangerous: amplified bias under the guise of neutrality.

OpenAI’s research highlights the difficulty of aligning AI systems with broad human values. Nowhere is this more pressing than in tools that filter job candidates.

How Bias Enters the System

  • Data from past hiring trends can reinforce discriminatory patterns.
  • Appearance-based scoring (even via video interview) privileges certain traits.
  • Interface language can subtly deter applicants from underrepresented groups.

Designers: You’re In the Loop

If you’re working on hiring platforms, dashboards, or feedback scoring tools—you’re shaping the UX of opportunity.

What You Can Do

  1. Add transparency: Explain what’s driving AI outputs.
  2. Enable manual review: Avoid over-automation in critical decisions.
  3. Test for edge cases and fairness across demographics.

How I Use AI to Automate 50% of My Creative Workflow (and You Can Too)

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By David Dunn | How to Leverage AI for Efficiency

It’s 2023, and AI is no longer just for coders or Silicon Valley startups. It’s for designers like you. I use AI tools every day to speed up repetitive tasks, generate better ideas, and keep projects moving even when my brain is tired.

And guess what? My clients love the results and so will yours.

My Favorite AI-Driven Automations

1. Prompt-to-Wireframe

Using Galileo AI, I type “homepage for an organic pet food company,” and BOOM—high-fidelity mockups appear. From there, I refine and add branding. Saves hours.

2. Copy + Tone + Layout = Jasper + Figma

I generate initial web copy in Jasper and send it to Figma with an AI plugin to auto-fill text blocks. Clients can review before I’ve even touched final design.

3. Zapier + ChatGPT = Creative Admin Bot

I use Zapier to trigger ChatGPT when a new client inquiry comes in. It drafts an intro message and fills in project fields based on the message text.

The Result? I now spend less time formatting briefs, writing placeholder copy, or resizing ads. That energy goes into creative ideas, client strategy, and yes—occasionally, a second cup of coffee.

The Accountability Crisis in AI: Who’s Really Responsible?

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By Joe Scaggs | AI Design Ethics & Current Events

As AI becomes more embedded in everyday tools—especially in creative design—one thing remains dangerously unclear: who is accountable when AI gets it wrong?

Imagine this: an AI tool recommends design assets that misrepresent racial or cultural identities. The client publishes them. Backlash ensues. The designer says, “The AI chose it.” The AI says… well, nothing.

So who takes the fall?

AI systems are growing in capability faster than our ability to govern them. We are deploying tools that we can’t always interpret—yet we’re embedding them in high-stakes workflows. This raises critical design ethics questions.

Diffusion of Responsibility: A Silent Risk in UX

Designers make choices about how much AI a user sees, when to prompt human override, and how errors are flagged. These decisions affect outcomes—but we rarely discuss them outside the dev room.

Best Practices to Clarify Accountability

  1. Use audit logs – Track every interaction between human and AI.
  2. Add feedback tools – Let users flag AI errors directly in design workflows.
  3. Include visible disclaimers – Users need to know when AI is acting vs. assisting.

Teams should adopt ethical documentation practices, like model cards (see Google’s Model Card Toolkit) or data statements.

AI isn’t a black box; it’s a mirror of our assumptions. When we design with clarity and responsibility, we make the whole system safer.

What is Generative AI? A Designer’s Primer

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By Bruce Bunner | AI Basics & Trends

Okay, let’s break this down. What the heck is generative AI, and why is everyone from your cousin to your cat’s vet talking about it?

Generative AI is a type of artificial intelligence that can create new stuff—images, text, music, even video—based on patterns it’s learned from other stuff. Basically, it’s like an overachieving art student with a massive copy machine in its brain.

In 2023, tools like ChatGPT, DALL·E 2, and Midjourney became household names. But for designers, it’s more than a headline; it’s a game-changer.

So What Can Designers Actually Do With It?

  • Mockups and Moodboards: Use Midjourney to generate moodboards from simple prompts.
  • Auto-layouts in UX: Galileo AI builds app interfaces from text descriptions.
  • Text-to-Image for Brand Visuals: Tools like Stable Diffusion let you generate brand imagery without stock photos.

Real Talk: It’s Not Perfect Sometimes you ask for “a businesswoman holding a laptop,” and you get a three‑armed robot in a tutu. That’s okay. AI doesn’t replace your creativity; it just gives you weird ingredients you can remix into something awesome.

Using AI to Automate Creative Workflows

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By David Dunn | How to Leverage AI for Efficiency

Creativity and efficiency might sound like opposites, but in today’s design world, AI brings them together in amazing ways. If you’re a designer who still spends half your day tweaking layouts or finding stock images, this one’s for you.

By late August 2023, tools for automating creative work had exploded in popularity, and they’re only getting better.

3 Ways AI Can Help You Automate without Losing Your Soul

1. Auto-Generating Content with Adobe Firefly

Adobe’s Firefly project uses generative AI to create design elements from text prompts: “Sunset background with watercolor texture” turns into usable assets instantly.

2. AI-Assisted Layouts in Canva and Figma

Canva’s “Magic Resize” feature and Figma’s layout plugins automatically adjust your designs across devices and formats. It’s like cloning yourself … but cheaper.

3. Task Automation with Zapier + ChatGPT

Create workflows where new client inquiries automatically generate project briefs or proposals using GPT-based plugins. Save hours without losing personalization.

But What About the Human Touch?

Automation isn’t about removing the human; it’s about removing the boring. The hours you save are hours you can spend on brainstorming, storytelling, and branding—the magic stuff. Just like Fast Company reported earlier this year, automation is becoming a secret weapon for creative professionals. Those who embrace it early will leap ahead.

The Ethics of Deepfakes in Design

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The age of image manipulation is not new. But what has changed—what demands our attention now—is how easily we can manufacture truth. Welcome to the era of deepfakes.

Deepfakes are videos or images created using AI that show people doing or saying things they never actually did. By August 2023, tools like DeepFaceLab and D-ID had made it possible to create realistic face swaps with nothing more than a laptop and a few photos.

In entertainment or parody, this might seem harmless, but in design—particularly when trust is part of the user experience—the stakes rise sharply.

Let’s say you’re building an ad campaign. You use AI to generate an image of a celebrity holding your product. It looks amazing, but it’s not real. Is that ethical? Legal? Could you even tell it’s fake a year from now?

According to a 2023 DeepMind report, over 60 percent of people cannot distinguish between a real and a synthetic image when shown side-by-side for less than five seconds. That’s a design problem. And an ethical one.

Three Ethical Guardrails for Deepfake-Aware Design

  1. Transparency First
    If you used AI to generate faces or voices, disclose it. A small badge or text label builds trust—and may soon be required by law (EU AI Act).
  2. Consent Matters
    Never use a person’s likeness, voice, or brand—real or synthetic—without permission. This includes composite or “lookalike” imagery.
  3. Context Is Everything
    A parody video? Fine. A fake testimonial from a “customer?” Unacceptable. Design is persuasion and with that comes responsibility.

The question isn’t can we make deepfakes; it’s should we? And if we do, how do we keep design honest in a world where visuals lie?

AI Trends 2024: What Designers Need to Know

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By Bruce Bunner | AI Basics & Trends

It’s 2023. AI can draw like Picasso, write like Hemingway, and maybe … design better than you? Just kidding. Sort of. But if you’ve ever wished for a magic intern, AI is about as close as you’ll get—without the passive-aggressive Slack messages.

Here’s what’s coming in 2024 that every designer needs to know:

1. Smart Interfaces That Learn

Your app dashboard might soon rearrange itself based on how you use it. Think Spotify but for layout. Yes, your software is judging you. But helpfully.

▶️ Read: UX Collective on Adaptive UI

2. Prompt-Based Everything

Want a landing page for your gluten-free dog treat startup? Just type it. Tools like Framer AI and Galileo AI are making prompt-driven design the new normal.

3. AI Sidekicks in Figma & Canva

Figma’s Autoflow plugin and Canva’s Magic Design let AI assist you with layouts and copy. It’s like a design buddy who never needs coffee.

4. Ethics Goes Mainstream

Users want to know if your work is AI generated. Agencies are even putting AI disclaimers in contracts. Transparency is trendy.

Bottom line? If you’re not learning AI, you’re designing like it’s 2013, which is fine, if you still like Comic Sans.

Leave a comment with your favorite AI tool or a design fail worth laughing about. We all have one.

Who’s Accountable When AI Goes Wrong?

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By Joe Scaggs | AI Design Ethics & Current Events

As designers, developers, and technologists, we are building systems that make decisions once made by people. But what happens when those decisions cause harm?

In May 2023, the New York Times published a story about AI models that made life-altering mistakes—wrong job evaluations, misdiagnosed patients, and algorithmic bias in law enforcement systems. None of these failures had a single “culprit,” yet the impact was deeply personal for those affected.

As AI systems get more advanced, responsibility becomes harder to trace. In the case of COMPAS, a criminal justice algorithm used in US courts, studies showed Black defendants were more likely to be incorrectly flagged as high risk. This wasn’t intentional; no one said, “Let’s make this racist.” But it happened because the data used was already flawed.

Who do we hold accountable? The developers? The data scientists? The UX designers? Or the companies that profit?

Every design choice reflects a value. Choosing what data to include, what outcomes to optimize, and even how we word error messages affects real people.

The AI Now Institute and other ethics watchdogs argue that the root of many problems lies not in the AI models themselves but in the failure to design transparent and auditable systems.

The following are three ways designers can lead ethically:

  1. Design for Explainability
    Your users (and regulators) should understand why the AI made a decision, not just what it did. Tools like Google’s Explainable AI help build this transparency into ML models.
  2. Create a Feedback Loop
    Build systems where users can challenge, correct, or appeal AI decisions. This creates accountability beyond the code.
  3. Use Bias Audits
    Services like ParlAI and IBM’s AI Fairness 360 can test datasets and models for bias. Make audits part of your standard process.

We need to accept that AI is not just a tool; it’s a system. And systems require oversight.

5 AI Tools Every Designer Should Use

Posted on by Dojo Editor

The design world is evolving, and AI is helping us move faster, smarter, and more creatively. You don’t need to be a tech wizard to use these tools. You just need to know where to look.

Here are five AI tools that will supercharge your design workflow in 2023:

1. Midjourney

Turn ideas into visuals with text prompts like “minimalist logo for a coffee brand.” Great for concept art, mockups, and idea boards.
▶️ Explore Midjourney

2. Jasper AI

Write blog posts, landing pages, or UX copy in minutes. Jasper helps maintain brand tone while giving you a head start.
▶️ Try Jasper

3. Runway ML

Video editing made smart: Remove backgrounds, generate animations, and apply effects with a click. Perfect for marketing or social media content.
▶️ Explore Runway

4. Figma AI Plugins

Plugins like Magician and Autoflow use AI to generate components, align objects, and suggest design improvements.
▶️ Explore Figma Plugins

5. Khroma

An AI color tool that learns your preferences and builds custom palettes.
▶️ Try Khroma

These tools are not replacements; they’re force multipliers. They help designers spend less time on repetitive tasks and more time on creativity.
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