Category: Featured Posts

AI in Hiring: Fairness or Flaw?

Posted on by Joe Scaggs

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.

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

Posted on by Dojo Editor

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

Posted on by Dojo Editor

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.

Who’s Accountable When AI Goes Wrong?

Posted on by Dojo Editor

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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