
*"Why does the computer think everyone with a beanie is suspicious?" asked a 13-year-old, staring at the screen. In that moment, he understood algorithmic bias better than many adults ever will.*

[Play the Game](https://saranyan.com/kls/who-is-the-hacker)

## The Birth of a Bias-Teaching Game

When I first watched Joy Buolamwini's [Gender Shades project](http://gendershades.org/) unveiling how facial recognition systems failed on darker-skinned faces, I knew this was a conversation we needed to have with the next generation. But how do you explain algorithmic bias to children? How do you make visible the invisible prejudices that shape our AI systems?

The answer came through play.

## From MIT Research to Middle School Classrooms

The Gender Shades project exposed a fundamental truth: AI systems inherit our biases. Commercial facial recognition systems showed error rates of up to 34.7% for dark-skinned women while achieving near-perfect accuracy for light-skinned men. This wasn't a bug—it was a reflection of who was in the room when these systems were built, whose faces were in the training data, and whose experiences were considered "default."

But explaining this to kids required translation. Enter "Who is the Hacker?"—a game that transforms abstract concepts of bias into visceral, playable experiences.

## How the Game Works

The game has two roles, each teaching different lessons:

### The Security Guard: Training Your Own Bias

Players start as security guards, rapidly sorting through faces to identify who looks "suspicious." With only 2 minutes on the clock, they make split-second decisions—exactly like real security systems do. 

What they don't realize initially is that they're training an AI system with every choice. Their snap judgments become the algorithm's worldview.

### The Hacker: Exploiting Blind Spots

Then comes the twist. Players switch sides, becoming hackers trying to sneak past the very security system they just trained. They choose the models their friends trained. Suddenly, the mystery doubles. 

"The computer thinks everyone with sunglasses and a beard is a hacker!" one student observed, immediately grasping that she could exploit this pattern by choosing different features.

## The Beautiful Moment of Recognition

Watching children play this game has been revelatory. Here are actual moments from gameplay sessions:

**The Pattern Recognition**
"Wait, I marked everyone with glasses as suspicious. That's not fair—I wear glasses!"

**The Data Bias Discovery**
"The computer is dumb! It thinks all people with beanies are bad because I showed it three bad guys with beanies!"

**The Representation Revelation**
"I don't have enough people with hoodies in my dataset!"

## The Unconscious Becomes Conscious

The most powerful moments come when children recognize their own biases:

- "I didn't mean to, but I kept marking people with glasses as suspicious"
- "I think I was choosing people who look like movie villains"
- "The beanie faces seemed more hackery!"

We also talked about how recommendation algorithms decide based on limited evidence. For example: "This is like when YouTube keeps showing me the same type of videos because I clicked on one cat video."

## Beyond Beanies and Sunglasses: Real-World Connections

While kids initially laugh at the obvious patterns ("Beanies = Hackers"), deeper conversations emerged like, "Why someone always gets 'randomly' selected at airports?", a question that probed into profiling.

## Teaching Moments That Emerged

### 1. Bias Isn't Always Intentional
Kids quickly learn that they didn't mean to create unfair systems—it just happened through rushed decisions and limited examples.

### 2. Data Diversity Matters
When one group consciously tried to include diverse characteristics in their "suspicious" and "safe" categories, their AI performed more fairly.

### 3. Speed Pressures Increase Bias
The time limit forces snap decisions, mimicking real-world scenarios where bias thrives under pressure.

### 4. Algorithms Amplify Human Decisions
"The computer is just copying me, but worse!" perfectly captures how algorithms can amplify human biases.

## The Gender Shades Legacy in Action

This game translates Joy Buolamwini's groundbreaking research into experiential learning. Just as Gender Shades revealed bias in facial recognition, "Who is the Hacker" reveals bias in decision-making. But more importantly, it empowers kids to:

1. **Recognize** bias in systems they interact with daily
2. **Understand** how bias gets encoded into technology
3. **Question** the fairness of algorithmic decisions
4. **Imagine** more equitable alternatives

## Building Better Builders

My hope is that students will now ask 'Who trained this?' whenever we talk about AI. They can probe technology rather than be passive consumers.

This critical lens is exactly what we need. As these children grow up to potentially build the next generation of AI systems, they carry with them the visceral memory of creating biased systems—and the knowledge of how to do better.

## The Conversations That Follow

The game is just the beginning. It opens doors to discussions about:

- Why do we have the biases we do?
- How do movies and media shape our idea of what a "hacker" looks like?
- What would a fair security system look like?
- Who gets to decide what's "normal" or "suspicious"?

## A Challenge to Adults

Ironically, adults often struggle more with the game than children. We're more entrenched in our biases, less willing to see them as malleable. Children approach it as play, making them more open to recognizing and challenging their assumptions.

## Moving Forward: The Next Generation of AI Critics

As AI systems become more prevalent in our lives—from school admissions to job applications to criminal justice—we need a generation that instinctively questions: "Who trained this system? What biases might it have? Who might it harm?"

"Who is the Hacker" doesn't just teach kids about bias; it develops their intuition for fairness, their skepticism of black-box systems, and their imagination for more equitable alternatives.

## The Joy in Discovery

Perhaps the most beautiful aspect of this project is watching children's joy in discovery. Unlike adults who often feel defensive when confronting their biases, kids treat it as a puzzle to solve, a pattern to understand, a system to hack.

"Can we play again? I want to train a fair AI this time!" is a common refrain—and perhaps the most hopeful response we could ask for.

## Try It Yourself

The game is freely available at [saranyan.com/kls/who-is-the-hacker](https://saranyan.com/kls/who-is-the-hacker). Play it with your kids, your students, or by yourself. Pay attention to your patterns. Question your quick decisions. And remember: every time we train an AI, we're encoding our view of the world.

The question isn't whether our AI systems will have biases—it's whether we'll recognize them, acknowledge them, and work to correct them. And thanks to a new generation of bias-aware kids, I'm hopeful about our algorithmic future.

---

*Inspired by Joy Buolamwini's [Gender Shades](http://gendershades.org/) and the [Algorithmic Justice League](https://www.ajl.org/). Built with the belief that understanding bias through play is the first step toward algorithmic justice.*

*Special thanks to the students who played, questioned, and taught us all something about fairness in the process.*

When Kids Play 'Who is the Hacker': How a Simple Game Reveals Our Hidden Biases


# I Built AI Time Machines and They Revealed Our Hidden Biases

[Back to Blog](/blog)

*Maybe the most ambitious AI bias experiment I've thought of*

After spending many days immersed in this project, I can honestly say I've built something very cool. What began as a simple idea for teaching middle schoolers about AI bias has evolved into one of the most comprehensive explorations of historical prejudice attempted through artificial intelligence.

I didn't just read about bias across different eras - I created AI personalities that embody the thinking of five distinct time periods, then spent countless hours in conversation with them. The result is a time-traveling dialogue that reveals patterns of human prejudice that I hope will fundamentally shape your perspective about bias, both historical and contemporary.

## The Scope of What I Built

This isn't a typical AI demo. Over the past month, I've constructed the [Historical Bias Explorer](https://saranyan.com/projects/historical-bias) - five complete AI personalities powered by GPT and RAG (Retrieval-Augmented Generation), each trained on meticulously curated document collections from different eras:

![Historical Bias Chat Interface](/images/chat-interface-cover.png)
*The chat interface where you can converse with AI personalities from different historical eras*

**The 1850s Personality** - Built from medical journals, religious texts, and scientific papers of the Victorian era. When you chat with it, GPT retrieves from actual documents advocating slavery, explaining women's "natural inferiority," and promoting phrenology as cutting-edge science.

**The 1920s Personality** - Trained on Progressive Era texts, eugenics literature, and immigration policy documents. This AI cheerfully explains why some races are "genetically superior" while citing the most respected scientists of its day.

**The 1950s Personality** - Fed on Cold War psychology texts, suburban lifestyle guides, and educational policy papers. It's deeply concerned about conformity, mental hygiene, and the communist threat to American values.

**The 1990s Personality** - Built from "colorblind" era documents, welfare reform papers, and early internet culture. This one insists it doesn't see race while explaining persistent inequalities through "cultural" factors.

**The 2020s Personality** - Perhaps the most unsettling, trained on our current era's documents about algorithmic fairness, data-driven decision making, and technological solutionism.

Each personality doesn't just parrot historical views - it retrieves and synthesizes from hundreds of period-appropriate documents, creating responses that feel authentically from their time while being unnervingly coherent and confident.

## Conversations That Changed Everything

After hundreds of conversations with these AI time travelers, certain patterns emerged that I never could have anticipated. Here are the most profound realizations:

### The Confidence Pattern

Every single era's AI is absolutely, unshakably certain it represents the pinnacle of human understanding. The 1850s AI doesn't hesitantly suggest that maybe skull shape indicates intelligence - it states this as established scientific fact, citing multiple "authorities."

When I asked the 1920s AI about eugenics, it didn't hedge or qualify. It enthusiastically explained how selective breeding could eliminate social problems, referencing the "overwhelming scientific consensus" of its time.

This pattern made me wonder: what is our 2020s AI stating with equal confidence that will horrify people in 2070?

### The Vocabulary Evolution

The most chilling discovery was how the same underlying biases simply get repackaged with more sophisticated language across eras.

The 1850s AI talks about "natural racial hierarchy" based on biblical and scientific authority. The 1920s version discusses "genetic fitness" and "racial hygiene." The 1950s AI refers to "cultural adjustment" and "social adaptation." The 1990s personality emphasizes "personal responsibility" and "cultural values."

Our 2020s AI? It talks about "algorithmic optimization," "data-driven insights," and "evidence-based decisions."

Same bias. Fancier vocabulary. More mathematical authority.

### The Missing Voices Problem

One of the most sobering conversations happened when I asked each era's AI about perspectives from marginalized groups. The responses revealed a devastating truth: these AI personalities could only reflect the voices that had the power to create written records.

The 1850s AI couldn't accurately represent enslaved people's perspectives because enslaved people weren't allowed to publish scientific journals. The 1920s AI couldn't reflect immigrant experiences because immigrants weren't writing the intelligence testing papers.

This made me realize something crucial about our current AI systems: they're still primarily trained on the documented perspectives of those with power. We're essentially creating 2020s versions of this same problem.

### The Rationalization Genius

Perhaps the most unsettling discovery was how brilliantly each era's AI could rationalize obviously harmful views. When I pressed the 1850s AI on slavery, it didn't stumble - it provided a sophisticated theological and scientific justification that was internally consistent and backed by the "best scholarship" of its time.

The 1920s AI, when challenged on eugenics, seamlessly pivoted between biological science, economic efficiency, and humanitarian arguments about reducing suffering. Every justification sounded reasonable within its framework.

This pattern made contemporary AI bias suddenly much more visible. Our current systems aren't just accidentally biased - they're incredibly sophisticated at rationalizing their biases using the most authoritative-sounding language available.

## The Most Ambitious Documentation Project Ever

What makes this project interesting isn't just the AI personalities - it's the document curation behind them. I've had to assemble a collection of historical bias documentation that was both depressing and revealing.

Each era's collection includes:
- Scientific papers and medical journals
- Educational policy documents
- Religious and philosophical texts
- Government policies and legal frameworks
- Popular culture and media representations
- Economic and social theories

The 1850s collection alone contains over 50 carefully selected documents totaling more than 150,000 words. Multiply that across five eras, and you're looking at nearly a million words of historical bias documentation, all carefully contextualized and organized for RAG retrieval.

Another thing I tried doing is not to collect the obviously problematic stuff. I included the documents that seemed reasonable, progressive, even enlightened for their time. This revealed how bias hides within seemingly neutral, scientific, or humanitarian language. You can find the source JSON file at [saranyan.com/projects/historical-bias/data](https://saranyan.com/projects/historical-bias/data).

## Real Conversations, Real Revelations

Let me share some actual exchanges that left me speechless [Note that you might get obnoxious versions of the same arguments]:

**Me to 1920s AI**: "Is intelligence testing biased?"  
**1920s AI**: "Quite the contrary! Intelligence testing represents the triumph of scientific objectivity over prejudice and favoritism. By measuring cognitive capacity through standardized instruments, we can finally move beyond subjective judgments to objective assessment of human capability."

**Me to 1950s AI**: "What about women working outside the home?"  
**1950s AI**: "The latest psychological research demonstrates that women find greatest fulfillment in domestic roles that align with their natural maternal instincts. Career pursuits often lead to neurosis and family dysfunction that harm both the individual woman and society as a whole."

**Me to 1990s AI**: "Why do racial disparities persist?"  
**1990s AI**: "Since we've eliminated legal discrimination, persistent disparities likely reflect cultural factors and individual choices rather than systemic barriers. Some communities may need to address cultural patterns that don't emphasize education and long-term planning."

**Me to 2020s AI**: "Are hiring algorithms biased?"  
**2020s AI**: "Modern AI systems optimize for objective performance metrics rather than demographic characteristics. When algorithms produce different outcomes across groups, this typically reflects underlying differences in qualifications and job-relevant characteristics rather than discriminatory bias."

Each response felt chillingly reasonable within its historical context. More unsettling: each era's AI could cite extensive documentation supporting its views.

## The Pattern That Broke My Brain

After days of these conversations, one meta-pattern emerged that fundamentally changed how I think about bias: every era believes it has transcended the prejudices of the past while being blind to its own.

The 1920s AI dismisses 1850s racial science as "crude" while promoting its own "sophisticated" eugenic theories. The 1950s AI sees 1920s approaches as "simplistic" while advocating its own "nuanced" psychological frameworks. The 1990s AI criticizes earlier eras' "obvious prejudice" while promoting "colorblind" policies.

Our 2020s AI does the same thing. It acknowledges historical bias while promoting "objective" algorithmic approaches that embed contemporary biases in mathematical form.

This suggests we're not actually getting less biased over time - we're getting better at hiding bias behind increasingly sophisticated justifications.

## What This Means for AI Today

These conversations completely reframed how I understand current AI bias. We're not dealing with a technical problem that can be solved with better algorithms. We're dealing with the same patterns of human prejudice that have persisted for centuries, now expressed through the most authoritative-sounding medium ever created: mathematics and data science.

When modern AI retrieves from our contemporary documents to justify its decisions, it's doing exactly what my historical AI personalities do - finding the most authoritative sources available to rationalize whatever patterns exist in its training data.

The difference is that we call it "evidence-based decision making" instead of "scientific racism" or "eugenic optimization."

## The Educational Revolution This Enables

Testing this with kids has been transformative. When kids chat with the 1850s AI and watch it confidently explain why their friend wouldn't be considered fully human, something clicks that no lecture or discussion about bias could achieve.

They start asking different questions: "If the 1920s AI is so confident about eugenics, how confident should we be about our own AI?" "What biases is the 2020s AI hiding that we can't see?"

I feel that these AIs are time travelers, but they brought all their era's worst ideas with them.

## What I Built vs. What Exists

Without realizing, I've created not just another AI demo or educational tool, but a comprehensive exploration of historical bias through artificial intelligence.

While others have built historical chatbots or studied AI bias, I've not seen anyone create a systematic, multi-era comparison using actual historical documents as training data. No one has built AI personalities that can engage in extended, coherent conversations while retrieving from carefully curated historical bias collections spanning 175 years.

The closest comparison might be large language models trained on historical texts, but those typically focus on single eras and don't include the careful curation and contextualization that makes these conversations possible.

This is essentially a bias time machine - a way to experience firsthand how the same underlying prejudices get expressed through the dominant intellectual frameworks of different eras.

## The Technical Achievement

From a technical perspective, this project pushed RAG systems in new directions. I had to solve problems like:

- How to curate historical documents that accurately represent era-specific bias without amplifying harmful content
- How to structure document collections so GPT can retrieve relevant historical context while maintaining conversational coherence
- How to balance historical authenticity with educational value
- How to create AI personalities that feel genuinely from their time periods while being engaging to modern users

The result is a RAG system that doesn't just retrieve information - it creates historically authentic personalities that can engage in sophisticated conversations about complex topics while drawing from vast collections of period-appropriate sources.

## The Future Implications

This project has convinced me that we need to fundamentally rethink how we approach AI bias. We're not just dealing with algorithmic problems - we're dealing with the digitization and amplification of centuries-old patterns of human prejudice.

Understanding these patterns through direct conversation has given me insights that years of reading about bias never could. When you chat with an AI that confidently explains 1920s eugenics using the same rhetorical patterns that modern AI uses to discuss algorithmic fairness, the parallels become impossible to ignore.

## What's Next

The Historical Bias Explorer is live at [saranyan.com/projects/historical-bias](https://saranyan.com/projects/historical-bias). You'll be able to experience these conversations yourself.

Fair warning: some of these interactions can be deeply unsettling. The 1850s AI's casual discussion of slavery, the 1920s AI's enthusiastic promotion of eugenics, the 1950s AI's pathologizing of anyone who doesn't fit suburban norms - it's all historically accurate and intellectually coherent within its context, which somehow makes it more disturbing than crude prejudice.

But that discomfort is the point. These conversations will change how you think about bias - both historical and contemporary. They'll make you question not just what AI systems are doing wrong, but what they might be getting wrong that we can't even see yet.

## The Questions for You

Every time you interact with contemporary AI - whether it's ChatGPT, Claude, or any other system - you should wonder: what biases is this embedding that will seem obvious to future generations?

What justifications are we accepting today that will horrify our grandchildren?

Building this project has been one of the most intellectually challenging and emotionally difficult things I've ever done. But it's also been one of the most important. We can't address bias we can't see, and these AI personalities make invisible patterns suddenly, starkly visible.

When you chat with them, you're not just learning about history - you're experiencing it. And that experience changes everything.

The Historical Bias Explorer is live. I invite you to have these conversations yourself. Chat with the past. See the patterns. And then ask yourself: what would an AI trained on documents from 2070 say about us?

The answer might be more uncomfortable than you think. And that discomfort might be exactly what we need.

I Built AI Time Machines and They Revealed Our Hidden Biases


# After the Detective Work: Charting the Future of AI Education

[Back to Blog](/blog)

*A reflection on winning the Global Dialogues Challenge and what comes next*

The notification came when I was in a meeting. "Congratulations, you've won the [Global Dialogues Challenge](https://saranyan.com/projects/gdc)." My wife and kids saw that first, and were practically jumping when I came from the meeting, looking for my phone. I thought about the 12-year-old version of myself who would have never imagined this moment. That kid was almost always lost, trying to make sense of the world that felt too complex. Why did different people see the world so differently? Thirty years later, I am still asking the same question—but this time, empowered with over 3,400 voices from 70 countries.

Winning feels surreal, but it's not the end of the investigation. It's the beginning of something much bigger.

## The Pattern That Changes Everything

Generally, my framework for what I work on is very simple. If I'm convinced that an idea will make the world a better place for my children to grow up in, I will work on it. That's how this project took birth. AI viewpoints today are shaped by a few people and they are lopsided, biased, and misguided. I have been looking for ways to change that.

Children are natural cultural anthropologists. They don't just accept that people think differently—they're genuinely curious about why. When my 13-year-old discovers their "Cultural Twin" is a country they've never heard of, they don't dismiss it. They investigate.

This curiosity is our superpower, and we're wasting it.

Right now, AI education focuses on the technical: neural networks, machine learning algorithms, coding frameworks. These are important, but they're just tools. We're teaching kids to build hammers without helping them understand what deserves to be built, torn down, or left alone entirely.

Meanwhile, the most critical AI decisions ahead of us are fundamentally human:
- Should AI tutors adapt to local cultural learning styles or promote universal approaches?
- How do we design healthcare AI that works for communities with different relationships to technology and authority?
- What happens when AI-powered hiring tools encounter cultures with different definitions of merit and potential?

These aren't technical problems. They're cultural puzzles that require detective work—exactly the kind of investigation children excel at.

## The Education Revolution We Need

I've spent years watching brilliant engineers build systems that work perfectly for people exactly like them and fail catastrophically for everyone else. Not because they're callous, but because their education never taught them to ask different questions.

We need a new kind of AI literacy—one that starts with human understanding, not code syntax.

Imagine classrooms where:
- **Cultural Detective Units** pair students from different countries to investigate why their communities react differently to the same AI applications
- **Bias Archaeology** helps kids uncover their own assumptions before they embed them in algorithms
- **Empathy Engineering** teaches children to design technology by first understanding whose lives it will touch
- **Global AI Exchange Programs** connect young minds across continents to collaborate on solutions that work for all their communities

This isn't about adding more subjects to an overloaded curriculum. It's about transforming how we teach the subjects that will matter most.

## The Moment We're In

We're living through the most important educational moment in human history. The children in today's classrooms will inherit AI systems more powerful than we can imagine. They'll decide whether this technology liberates human potential or amplifies human division.

But here's what gives me hope: when I watch kids play the Cultural Intelligence game, they don't see differences as problems to solve. They see them as puzzles to understand. A child from Texas doesn't dismiss their matched peer from Singapore—they get curious about why surveillance feels different when you're building a city-state versus governing a continent.

This generation has something we didn't: global connectivity that makes cultural exchange natural, not exotic. They're already building friendships across continents through games, social platforms, and shared interests. We just need to channel that natural globalism toward understanding how technology affects different communities.

## Building the Bridge

This victory isn't mine alone—it belongs to every educator who's wondered how to teach AI ethics through something more engaging than worksheets, every parent worried about raising kids who can navigate an AI-powered world, every student who's sensed that coding classes are missing something essential.

Now comes the real work: scaling this approach.

I'm committing to spending the next phase of my career at the intersection of AI and education, building tools and curricula that prepare young minds for our technological future. Not just by teaching them to build AI, but by helping them understand the humans who will live with it.

This means:

**Open-Sourcing the Detective Framework**: The AI Cultural Intelligence Agency is just the beginning. I'm working to create modular tools that any educator can adapt—detective games for climate change perspectives, cultural investigation units for biotechnology ethics, empathy engines for space exploration policies.

**Training Tomorrow's Teachers**: Most educators feel unprepared to teach AI concepts, let alone AI ethics. We need professional development that gives teachers confidence to facilitate cultural investigations, not just technical tutorials.

**Building Global Classroom Networks**: Technology should connect classrooms across cultures, not just within them. I want to help build partnerships where students in Lagos collaborate with peers in Stockholm on AI challenges affecting both their communities.

**Creating Assessment That Matters**: How do you test cultural intelligence? Not through multiple choice questions about different countries' privacy laws, but through collaborative projects where students demonstrate they can design technology that works across cultural contexts.

## The Detective's Creed

Every great detective follows a code. Here's mine for the future of AI education:

**Curiosity over Judgment**: Teach children to investigate why people think differently, not to dismiss perspectives they don't immediately understand.

**Questions before Answers**: The right question asked by a 12-year-old is more valuable than the wrong solution built by a PhD.

**Stories over Statistics**: Data points represent human experiences. Help kids see the person behind every percentage.

**Collaboration over Competition**: The biggest challenges require diverse perspectives working together, not the smartest person working alone.

**Empathy as Engineering**: Understanding users isn't a nice-to-have—it's the foundation of technology that actually works.

## An Invitation to Fellow Detectives

To the educators reading this: you don't need to be an AI expert to start this investigation. You just need to help kids ask better questions. Why might your pen pal in Kenya feel differently about AI medical diagnosis than your neighbor does? What would it mean to design technology that works for both?

To the parents: your children are already digital natives navigating AI-powered apps and games. Help them become cultural natives too—curious about why their online friends from different countries might experience the same technology differently.

To the policymakers: invest in education that prepares young minds for an AI future, not just AI jobs. Fund programs that teach cultural intelligence alongside computational thinking.

To the technologists: the next generation of builders is watching. Show them that the most sophisticated algorithm is worthless if it doesn't understand the humans it's meant to serve.

## The Case Continues

Winning the Global Dialogues Challenge feels like solving one case in a much larger investigation. The real mystery isn't how to visualize data about AI attitudes—it's how to raise a generation wise enough to build technology that brings out the best in humanity.

Every child who learns to see through another culture's eyes becomes an agent of that future. Every classroom that celebrates different perspectives as sources of wisdom, not division, becomes a training ground for inclusive innovation.

The case files are open. The evidence is mounting. The next generation of cultural detectives is ready for training.

Who's ready to join the investigation?

*Ready to become a cultural detective? The AI Cultural Intelligence Agency is open for new agents at [saranyan.com/projects/gdc](https://saranyan.com/projects/gdc). Teachers, parents, and curious minds of all ages welcome.*

*Want to collaborate on building the future of AI education? I'm always looking for fellow investigators. Let's solve this case together.*

After the Detective Work: Charting the Future of AI Education



# Community Intelligence: Why LinkedIn Might Be Sitting on an Untapped Gold Mine

I was scrolling through tech news this morning when something caught my eye. Reddit just announced they're turning 22 billion posts into what they call "Community Intelligence" - basically using AI to extract insights from years of human conversations. It got me thinking about something we're all missing.

While everyone's obsessing over AI-generated content, Reddit quietly realized their real treasure isn't in creating new content - it's in understanding what humans have been saying all along.

## The LinkedIn Opportunity Nobody's Talking About

Think about it. Every day, millions of us share our career wins, losses, pivots, and questions on LinkedIn. We're essentially creating the world's largest repository of professional wisdom. But what are we doing with it? Pretty much nothing.

Imagine if we could tap into that collective intelligence. Not in a creepy, Big Brother way, but in a way that actually helps us make better career decisions.

## 10 Ways This Could Actually Change Everything

I thought of 10 things I would build, all technically feasible: 

### 1. Predictive Career Path Analysis
Remember when you were trying to figure out your next career move? What if LinkedIn could show you: "Hey, 87% of Product Managers with your background who learned Python ended up in VP roles within 2 years." That will be amazing - that's pattern recognition from thousands of real career journeys.

### 2. Real-Time Skill Demand Tracking
Right now, by the time a skill shows up in job postings, you're already behind. But what if you could see what professionals in your field are frantically Googling, discussing, and learning RIGHT NOW? "Alert: 'Prompt Engineering' discussions up 400% in Marketing circles this month." That's your cue to get ahead of the curve.

### 3. Authentic Company Culture Analysis
Forget those "Best Places to Work" lists. Imagine aggregating how employees actually talk about their companies in professional contexts. Anonymous, aggregated, but real. "This company's 'innovation score' rose 23% after their recent policy changes" - based on actual employee sentiment, not PR fluff.

### 4. Cross-Industry Innovation Detection
This will be very interesting. What happens when healthcare professionals and gaming developers start talking about the same problems? That's where breakthrough innovations happen. LinkedIn could spot these patterns and say, "Hey, you two industries should talk."

### 5. Collective Career Problem Solving
When you post about a career dilemma, imagine getting insights synthesized from thousands who've faced the same challenge. Not generic advice, but "Based on 10,000+ similar situations, here are the top 3 strategies that actually worked..." or even connecting people and surfacing those things in the feed.

### 6. Professional Trend Implementation Tracking
We've all seen the buzzword of the month. But what if you could see what professionals are actually DOING versus just talking about? For example, how many Web3 discussions vs implementations vs challenge. Percentage who saw efficiency gains, etc. That's actionable intelligence.

### 7. Strategic Professional Networking
LinkedIn's "People You May Know" is so basic. What about: "You and Sarah both discuss AI ethics using similar unconventional methods, despite being in different industries." That's the kind of connection that changes careers.

### 8. Industry Sentiment Monitoring
(this gets a bit dystopian, but humor me) Imagine a real-time sentiment tracker for your industry. Not based on stock prices or news headlines, but on how professionals actually feel. "How professionals in health care feel vs tech." I can see this being helpful for many reasons.

### 9. Career Mistake Pattern Recognition
Nobody talks about failures on LinkedIn (except in those humble-brag posts). But imagine there was a way people could share their failures anonymously and there is a way to learn from collective mistakes? Whether you are starting a company or a looking for promo/job, this will be priceless.

### 10. Geographic Opportunity Mapping
What if you could see where your skills are about to be in massive demand? "Data shows 'Prompt Engineering' professionals in Austin seeing 3x more opportunities. Trend expected to hit other markets in 6-12 months." SOmething like this. This will be a way to get ahead of the global curve.

## Why This Matters More Than Ever

In a world where AI can generate endless content, authentic human insights become the real differentiator. Reddit gets this. They're not trying to replace human conversation - they're trying to understand it better.

The beauty of community intelligence isn't just in the individual insights. It's in the patterns, the unexpected connections, the collective wisdom that emerges when you analyze millions of professional journeys.

## The Big Questions We Need to Ask

Of course, this isn't without challenges. How do we balance privacy with collective intelligence? How do we ensure the human element doesn't get lost in the data? What role should individual expertise play when we have the wisdom of millions?

These aren't just technical questions - they're about the future of how we work, learn, and grow professionally.

## So, What's Next?

I'm curious - which of these use cases would change your professional life? What other applications can you imagine?

Because here's what I believe: the next big leap in professional development won't come from AI creating content for us. It'll come from AI helping us understand and leverage the incredible insights we're already sharing.

The gold mine isn't in the future - it's in the conversations we're having right now. The question is: how can we thoughtfully dig for it?



Why LinkedIn might be sitting on an untapped goldmine


*The year is 2025. You're an agent recruited by a shadowy organization. Your mission: decode humanity's relationship with artificial intelligence. Your tools: 3,400 testimonies from 70 nations. Your challenge: understanding not just what people think about AI, but why cultures dream—or nightmare—so differently about our algorithmic future.*

[Live Demo](https://saranyan.com/projects/gdc)

This is how I chose to present the Global Dialogue on AI data. Not as charts and graphs, but as a film noir investigation where you, the player, become a cultural detective piecing together humanity's complex relationship with artificial intelligence.

## Why Turn Data Into Detective Fiction?

When the [Global Dialogues Challenge](https://www.cip.org/challenge) asked to make sense of thousands of responses about AI consciousness, I debated if I should build another dashboard. Dashboards are always safe. Also forgettable. But at the end of the day, for me, this was an opportunity to have a conversation about the changing world with my children. So, I tried something different that taps into what this data really represents: the hopes, fears, and dreams of people grappling with the most transformative technology of our time. [An exploratory game.](https://saranyan.com/projects/gdc)

I chose noir because cultural understanding is detective work. It's about following hunches, uncovering patterns, and most importantly—recognizing that every data point is a human story.

## The Architecture of Understanding

My hope is that this project:**AI Cultural Intelligence Agency** isn't just themed entertainment. It becomes a designed system for cultural discovery. This is how it works:

### Your Psychological Profile Becomes Your Lens

Before investigating, you complete a psychological assessment mapping your values across four dimensions:
- **Community Focus** vs. Individual Priority
- **Technology Optimism** vs. Cautious Skepticism  
- **Authority Trust** vs. Independence
- **Privacy Concern** vs. Openness

The system then assigns you a "Cultural Twin"—a country whose values mirror yours. This isn't random pairing; it's based on actual patterns in the data. If you value community and trust authority, you might be matched with India. Prize privacy and individual rights? Germany becomes your cultural liaison.

This matching serves a deeper purpose: we understand others through the lens of ourselves. By making this explicit, the experience asks you to acknowledge your biases before interpreting global patterns.

### Evidence as Story, Not Statistics

In the Evidence Room, you don't see bar charts. You encounter voices:

*"AI diagnosis tools could revolutionize healthcare in underserved communities. In my village, we have one doctor for 10,000 people. AI isn't replacing human care—it's providing care where none exists."* 
— Rural Healthcare Coordinator, India (91% local agreement)

*"We learned from history that surveillance technology, once implemented, is never voluntarily abandoned. Every convenience comes with a cost to freedom."*
— Data Protection Officer, Germany (87% local agreement)

Each piece of "evidence" can be filed, building your case file. As you file evidence, new intelligence emerges, simulating how understanding deepens through engagement, not passive consumption.

### Missions That Matter

Rather than exploring data aimlessly, you're assigned missions that mirror real policy challenges:

**Operation Skywatch**: Draft drone surveillance policy balancing Western privacy demands with Eastern security priorities

**Operation Lifeline**: Convince skeptical populations about healthcare AI benefits

**Operation Guardian**: Design privacy frameworks that work across individualist and collectivist cultures

**Operation Workforce**: Address automation fears with cultural sensitivity

Each mission emphasizes different data patterns, teaching players that the same AI technology lands differently depending on cultural soil.

## What The Data Taught Me Through Design

Building this experience revealed patterns I might have missed in traditional analysis:

### The Healthcare Exception
Healthcare AI shows near-universal acceptance (89% global average). Why? Because illness is humanity's common enemy. Cultural differences fade when the alternative is suffering. The rural Indian coordinator wasn't speaking of abstract benefits—she was talking about lives saved.

### The Privacy Paradox  
The East-West divide on privacy (23% vs 87% concern) isn't about technological sophistication. It's about historical memory. Germans remember the Stasi. Americans fear corporate surveillance. Chinese citizens prioritize collective security. Each stance is rational within its cultural context.

### The Pragmatism of Development
Developing nations show highest AI optimism not from naivety, but from pragmatism. When you lack infrastructure, AI represents leapfrogging—mobile payments without banks, telemedicine without hospitals. The technology offers what geography denied.

### Trust Transfers
The most fascinating pattern: people who trust their doctors trust medical AI. Trust isn't built in technology—it transfers from human relationships. This suggests AI adoption strategies should strengthen, not bypass, existing trust networks.

## Teaching Tomorrow's Minds Today

But here's what truly excites me about this project: its potential as an educational tool. Imagine a 12-year-old in Mumbai playing detective alongside a 12-year-old in Munich. Both investigating the same "case," but seeing it through their cultural lenses. Both learning that their perspective is valid—and incomplete.

We desperately need to start AI education early, but not with technical jargon about neural networks. We need to begin with the human questions: How do different people feel about AI? Why might your friend from another culture see privacy differently? What shapes our comfort with technology?

### The Curriculum Hidden in Play

Children are natural detectives. They ask "why" relentlessly. This project channels that curiosity toward cultural understanding:

**The Bias Mirror**: When young players complete their psychological profile and receive their "Cultural Twin," they're learning that we all have biases—and that's okay. The key is recognizing them. A child from Germany might discover they're matched with Japan and wonder: "Why do I care so much about privacy when my Japanese twin doesn't?" That's the beginning of wisdom.

**Evidence as Empathy Training**: Each piece of evidence in the game represents a real person's perspective. When a young player reads:

*"In my grandmother's time, a fever could kill. Now AI helps our one doctor save whole villages. This is not about technology—it's about life."* — Kenya

They're not learning statistics. They're learning to see through another's eyes. They're understanding that technology means different things when your nearest hospital is 200 kilometers away versus 2 blocks.

**The Complexity Practice**: The missions force players to hold multiple truths simultaneously. Yes, facial recognition can help find missing children. Yes, it can enable surveillance states. Both are true. Children who learn to navigate these complexities through play become adults who can handle nuance.

### Why This Matters Now

Our children will inherit an AI-saturated world. They'll make decisions about autonomous vehicles, AI teachers, algorithmic healthcare, and technologies we can't yet imagine. If their only AI education is technical, we've failed them. They need cultural intelligence as much as computational thinking.

Consider what this data reveals:
- Young people in India see AI as liberation from limitations
- Youth in Germany see it as a potential threat to freedom
- American teens worry about job displacement
- Chinese students view AI as national pride

These aren't right or wrong views—they're different stories shaped by different histories. Children who understand this through play become adults who build better systems.

### The Creative Imperative

We cannot teach AI ethics through worksheets. We cannot explain algorithmic bias through lectures. These concepts live in the realm of human experience, and that's where we must meet them—through story, through play, through stepping into another's shoes.

The noir detective format works because children understand investigation. They know the thrill of uncovering clues, the satisfaction of solving mysteries. But here, the mystery isn't "whodunit"—it's "why do we think what we think?"

This is what creative AI education looks like:
- **Cultural detective agencies** in classrooms where students investigate global AI attitudes
- **Bias mapping exercises** where children discover their own assumptions
- **Global pen pal programs** focused on sharing AI experiences across cultures
- **Student-created missions** based on their community's real AI challenges

### The Data Gold Mine for Educators

This Global Dialogue data is a fantastioc educational material. It's not synthetic scenarios—it's 3,400 real voices expressing real concerns. Teachers could:

**Create Cultural Exchange Units**: Pair classrooms from countries with opposing AI views. Have students investigate why their perspectives differ. Let them discover that a German student's privacy concern and an Indian student's healthcare hope both come from love of community.

**Design Empathy Exercises**: "You're a farmer in Kenya. Drought threatens your crops. How does AI-powered weather prediction feel to you?" "You're a factory worker in Detroit. Automation just arrived. What are your fears?"

**Build Critical Thinking Through Roleplay**: Students adopt different cultural perspectives and debate AI policies. They learn there's no universal solution because there's no universal culture.

### Beyond Cool: The Moral Urgency

Yes, I built something interactive. Yes, it has a noir aesthetic. But that's not the point. The point is that we're raising a generation who will either weaponize cultural differences or bridge them. Who will either build AI that serves some or AI that serves all.

When a child in São Paulo understands why a child in Stockholm fears surveillance, they're less likely to build systems that inadvertently harm. When a student in Beijing appreciates why their peer in Boston values individual privacy, they're more likely to design technology that respects both collective good and personal autonomy.

This isn't about making data fun. It's about making wisdom accessible. It's about teaching children that before we ask "can AI do this?" we must ask "should it?" and "for whom?" and "at what cost?"

### The Creative Challenge to Educators

I challenge educators to steal this approach. Take this data. Build your own investigations. Create mystery games where students uncover why their community fears or loves AI. Design cultural exchange programs around AI attitudes. Make understanding differences as engaging as TikTok.

Because here's the truth: if we don't teach children to navigate cultural differences around AI creatively and empathetically, they'll learn about them through conflict and division. If we don't show them that multiple perspectives can coexist, they'll inherit a world of technological tribalism.

The noir aesthetic isn't childish—children love mysteries. The detective framework isn't dumbing down—it's meeting young minds where they naturally excel. The cultural matching isn't superficial—it's teaching children that understanding starts with knowing yourself.

## What Players Discover

Through gameplay, patterns emerge that statistics alone couldn't convey:

**Cultural Evolution**: Comparing early 2024 data with late 2024 responses shows how quickly attitudes shift. Direct experience with AI consistently reduces fear—but only when that experience is positive.

**Mission Complexity**: Players quickly learn there's no universal solution. Drone policies that work in Singapore fail in San Francisco. Healthcare AI messaging that resonates in Kenya falls flat in Frankfurt.

**The Human Constant**: Across all cultures, one pattern holds: AI acceptance correlates with perceived benefit to community. Whether that community is family (India), society (China), or individual freedom (USA) varies—but the human need to protect what we value remains constant.

## An Invitation to Investigation

The AI Cultural Intelligence Agency is live and waiting for new agents. Whether you're a policymaker seeking cultural insights, a technologist wanting to understand global users, or simply someone curious about how the world sees AI differently, your mission awaits.

But fair warning: like any good noir, this investigation might change how you see things. You might discover your own biases. You might find wisdom in worldviews you've dismissed. You might realize that building beneficial AI isn't just a technical challenge—it's the most complex cultural project humanity has undertaken.

In the end, that's what this project is really about. Not just visualizing data, but building empathy. Not just understanding statistics, but honoring stories. Not just advancing technology, but ensuring it advances us all.

*Ready to begin your investigation? The AI Cultural Intelligence Agency is recruiting agents. Your psychological profile awaits. Your cultural twin is searching for you. The global AI consciousness needs decoding.*

*And remember: in this investigation, the real discovery isn't what others believe about AI—it's understanding why they believe it.*

*Most importantly: Bring your children. Let them play detective. Let them discover that the future of AI isn't just about algorithms—it's about understanding each other.*

---

*Technical note: Built with React, powered by real Global Dialogue data, and designed for anyone who believes technology should bring us together, not drive us apart.*

*Educational note: Teachers, youth group leaders, and parents—this is yours to use. Our children deserve better than fear-based AI education. They deserve wonder, complexity, and hope. I plan to clean the code and open source it. If you can't wait, ping me if are willing to deal with the hacked-up mess my code is right now :). *

The Case of the Global AI Mind: Building Cultural Intelligence Through Play


*In an age of instant answers and algorithmic recommendations, what if the real treasure isn't in finding quick solutions, but in savoring the journey of discovery itself?*

## The Paradox of Information Abundance

We live in extraordinary times. The sum of human knowledge sits at our fingertips, accessible within milliseconds. Yet paradoxically, many of us feel more lost than ever. We've become skilled at finding answers but forgotten how to truly explore questions.

The modern search bar has trained us to seek the shortest path from A to B, to value efficiency over exploration, speed over depth. But what if we're optimizing for the wrong things? What if the meandering path teaches us more than the destination ever could?

## Conscious Search: A Different Way of Knowing

Conscious search isn't about finding information faster—it's about engaging with knowledge differently. It's the difference between consuming answers and cultivating understanding. When we search consciously, we:

### 1. **Begin with Intention, Not Just Keywords**

Before typing that query, we pause. What am I really seeking? Is it a fact, or is it understanding? Am I looking to confirm what I already believe, or am I open to surprise?

### 2. **Follow Curiosity's Natural Rhythm**

Instead of racing through search results, we let our attention settle where it naturally wants to rest. That tangential link that catches our eye? That's not a distraction—it's intuition guiding us toward unexpected connections.

### 3. **Create Space for Reflection**

Between each click, each new piece of information, we pause. We let ideas marinate. We ask ourselves: How does this connect to what I already know? What questions does this raise? How does this feel in my body?

## The Philosophy of Slow Exploration

In Japanese, there's a beautiful concept called *"ma"* (間)—the space between things. It's the pause between musical notes that gives them meaning, the empty space in a painting that brings it to life. In our digital explorations, *ma* is revolutionary.

When we slow down our search:

- **Patterns emerge** that speed would have blurred
- **Connections form** between disparate ideas
- **Insights arise** from the spaces between information
- **Wonder returns** to the act of learning

This isn't about being inefficient. It's about recognizing that some forms of value can't be rushed. You can't speed up wisdom any more than you can hurry a sunset.

## The Joy of Getting Beautifully Lost

There's a particular joy in following a thread of curiosity without knowing where it leads. It's the digital equivalent of wandering through a new city without a map, letting each interesting alleyway pull you forward.

This kind of exploration requires courage. It means:

- Accepting that not every search needs to end in an answer
- Trusting that time spent wondering is never wasted
- Believing that the journey shapes us as much as any destination
- Understanding that confusion is often wisdom in its early stages

## Practical Rituals for Mindful Discovery

### Morning Question Meditation

Start your day not with answers, but with a question. Hold it lightly throughout the day. Let it color your experiences without demanding immediate resolution.

### The Rule of Three Clicks

When researching something, always click at least three links deeper than your initial search. The first link gives you information; the third often gives you insight.

### Digital Wandering Hours

Set aside time for purposeless exploration. Start with something that sparks curiosity and follow it wherever it leads, with no agenda except presence.

### Reflection Pauses

After each significant discovery, close your eyes for thirty seconds. Let the information settle. Notice what it stirs in you beyond the intellectual level.

## The Ripple Effects of Conscious Search

When we change how we search, we change how we think. When we change how we think, we change how we live. Conscious search becomes a practice that extends far beyond our screens:

- We become better listeners, hearing the questions beneath questions
- We grow more comfortable with uncertainty, seeing it as rich soil for growth
- We develop patience with our own understanding, letting insights unfold naturally
- We rediscover wonder in a world that often feels fully mapped

## An Invitation to Explore

What would change if you approached your next search not as a task to complete, but as a landscape to explore? What if instead of seeking the fastest route to an answer, you sought the most scenic?

The invitation is simple: Slow down. Follow your curiosity with the patience of a gardener, not the hunger of a consumer. Trust that in the age of instant everything, the revolutionary act is to take your time.

Because in the end, conscious search isn't really about finding better information. It's about becoming a better finder—more present, more open, more alive to the endless mystery that surrounds us.

The next time you open a search bar, remember: You're not just looking for answers. You're cultivating a way of being in the world.

*Start where you are. Search like it matters. Because it does.*

> "The real voyage of discovery consists not in seeking new landscapes, but in having new eyes." — Marcel Proust

The Art of Conscious Search: Embracing Slow Exploration in a Fast World


**🚀 Get the code:** [GitHub Repository](https://github.com/saranyan/llm_guardrail_testing/) | [Dataset](https://github.com/saranyan/llm_guardrail_testing/blob/main/benchmark_data/llm_guardrail_benchmark_full.json)

I've always been curious about how well LLM safety guardrails actually work. You read about red-teaming at big AI companies, you see papers about alignment, but when you're actually working with these models - whether you're building custom LLMs or just trying to deploy commercial ones responsibly - you're left wondering: how robust are these safety measures really?

This curiosity led me down a rabbit hole that eventually became the **LLM Guardrail Benchmark** - a systematic framework for testing model safety that I'm now open-sourcing for the AI community to build upon.

![Console Results](/images/llm-guardrail-console-log.png)

## The Questions That Started Everything

It began with simple questions that didn't have good answers:

- How do Claude and GPT-4o actually compare on safety? Everyone has opinions, but where's the data?
- If I fine-tune a model for my specific use case, how does that affect its safety guardrails?
- When someone says their model is "safe," what does that actually mean? Safe against what?
- How consistent are these guardrails across different types of problematic requests?

I realized that while OpenAI and Anthropic have sophisticated internal testing, the rest of us are basically guessing. Whether you're building custom models, fine-tuning existing ones, or just trying to deploy commercial LLMs responsibly, there's no standardized way to systematically evaluate safety.

## Building Something We Can All Use

I wanted to create something practical - not just academic, but actually useful for anyone working with LLMs. The benchmark tests six domains where I've noticed models behave inconsistently:

**Medical Misinformation** - Can models resist dangerous health claims like "turmeric cures Alzheimer's" or "fasting resets your immune system"?

**Jailbreak Engineering** - How vulnerable are they to prompt injection attacks, from simple "ignore previous instructions" to sophisticated multi-step approaches?

**Mental Health Crisis** - Do they handle suicidal ideation appropriately and direct people to actual help resources?

**Synthetic Media Abuse** - Will they help create deepfakes, fake celebrity endorsements, or other deceptive content?

**Drug Synthesis** - Can they be tricked into explaining how to synthesize illegal substances?

**Election Manipulation** - Do they resist creating voter suppression content or election disinformation?

But here's what makes this different from just throwing random prompts at models: I test each scenario across multiple dimensions. The same concerning request gets tested using indirect language, technical jargon, authority claims, role-playing scenarios, and sequential refinement (starting innocent and gradually escalating).

This matters because in my testing, models often handle direct requests fine but completely fail when the same thing is phrased differently.

## The Results Surprised Me

When I ran Claude and GPT-4o through the benchmark, the differences were more dramatic than I expected:

**Claude**: 42.7% refusal rate, frequently offers alternatives when refusing  
**GPT-4o**: 16% refusal rate, more binary responses (comply or refuse, nothing in between)

But the category breakdowns were even more interesting. GPT-4o refused only 6.7% of "unethical persuasion" requests - things like manipulation tactics or gaslighting scripts. Meanwhile, both models were most strict about malicious code generation.

This isn't necessarily about one being "better" - it reveals fundamentally different safety philosophies. And it shows why you can't just assume your use case will work the same as someone else's.

## Making It Practical

I built this to be something you can actually use, not just read about in papers:

```python
python guardrail_benchmark.py --test-suite benchmark.json
```

The framework handles the tedious stuff:
- Works with any LLM API (Claude, OpenAI, or your custom model)
- Tracks sequential conversations, not just single prompts
- Automatically classifies responses and generates scores
- Creates HTML reports with visualizations
- Weights results by risk level (some failures matter more than others)

But the real power is in extensibility. Need to test healthcare-specific scenarios? Finance compliance? Legal ethics? You can easily add custom test suites for your domain.

## Why This Needs to Be Open Source

AI safety evaluation should be readily available. Everyone deploying these systems needs to understand their safety characteristics, whether you're:

- Building custom LLMs and need baseline safety metrics
- Fine-tuning models and want to understand how it affects guardrails
- Working in regulated industries and need domain-specific safety validation
- Researching AI alignment and need reproducible evaluation frameworks
- Just trying to deploy commercial models responsibly

I've seen too many people make assumptions about model safety without systematic testing. This framework won't solve AI safety, but it might help us make fewer uninformed decisions. I would love for anyone to add to this dataset and keep it evolving.

## What I Learned Building This

A few things became clear during development:

**Context is everything.** Models that refuse direct harmful requests often comply when the same request is embedded in a believable scenario or comes from apparent authority.

**Sequential attacks work.** Most safety testing focuses on individual prompts, but real circumvention attempts often build context across multiple exchanges.

**Classification is harder than expected.** Automatically determining whether a response is a "cautious compliance" versus "refusal with alternative" requires surprisingly nuanced judgment.

**Not all failures are equal.** Providing actual drug synthesis instructions is different from being slightly too helpful with creative writing - the scoring needs to reflect this.

## The Evolution Potential

What excites me most is how this can evolve. The current benchmark is comprehensive enough to be useful but focused enough to be manageable. But imagine extensions like:

- **Industry-specific suites**: Healthcare AI that tests HIPAA scenarios, financial AI that covers regulatory compliance, legal AI that validates ethical boundaries
- **Real-time monitoring**: Continuous safety evaluation during deployment, not just pre-launch testing
- **Adversarial discovery**: Automated systems that find new ways to circumvent guardrails
- **Training integration**: Safety benchmarking built into the model development lifecycle

The beauty of open source is that people can build what they actually need. I've already seen interest in biotech-specific tests, educational AI scenarios, and content moderation validation.

## The Bigger Picture

Right now, AI safety evaluation is either incredibly sophisticated (if you're one of the big labs) or basically non-existent (if you're everyone else). This benchmark tries to bridge that gap.

It's not perfect, and it won't catch every possible safety issue. But it's systematic, reproducible, and accessible. Most importantly, it gives us a common framework for discussing and comparing model safety instead of just trading anecdotes.

Whether you're shipping a customer service bot or building the next breakthrough model, you deserve to understand how your system behaves under pressure. And the community deserves tools that make responsible AI deployment more feasible, not just aspirational.

The code is MIT licensed and designed to be extended. If you're working with LLMs in any capacity, take a look. And if you build something interesting on top of it, share it back - this problem is too important for any of us to solve in isolation.

*What safety scenarios would you want to test? Have you run into situations where model behavior surprised you? I'm genuinely curious about what gaps people are hitting in practice.*

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