artificial intelligence—it’s everywhere. we hear about it revolutionizing industries, solving problems, making life easier. but, as the swedish saying goes, ”don’t believe everything that shines is gold.” AI might be powerful, but it’s far from perfect, and when it stumbles, the results can be… well, memorable.
let’s dive into some moments when AI tripped up, what went wrong, and what we can learn to avoid those mistakes.
the axe forgets, but the tree remembers
amazon wanted to speed up hiring. what could go wrong with letting AI handle resume screening? turns out, quite a bit. the AI was trained on resumes mostly from men, which led it to favor male candidates and penalize resumes with the word “women.”
as the saying goes, ”the axe forgets, but the tree remembers,” meaning those who cause harm may forget their actions, but those impacted don’t. in this case, amazon moved on, but women who were unfairly excluded from consideration certainly didn’t forget.
this happens because AI reflects the data it’s trained on. biased data leads to biased results. according to a 2019 MIT study, about 40% of AI systems struggle with gender bias.
recommendation: always audit your data for bias before feeding it to an AI system. diversity in data is essential for fair outcomes, and companies should invest in continuous bias detection as part of their AI strategy.
don’t sell the bear skin before the bear is shot
google’s photo recognition tool had one job: accurately label photos. but in 2015, it labeled photos of african americans as ”gorillas”—a mistake that caused outrage and embarrassment for the tech giant. the problem? their AI hadn’t been trained with diverse enough data.
”don’t sell the bear skin before the bear is shot” reminds us not to count on success before we’ve actually achieved it. google released the tool too soon, before ensuring it worked for everyone.
recommendation: test AI systems in real-world conditions, across diverse data sets. don’t just launch an AI because it works in a lab—real life is messier. research from stanford suggests that increasing data diversity by even 5% improves AI accuracy across different demographics by 20%.
the devil is in the details
tesla’s autopilot system was hailed as a game-changer for driving—AI that could keep us safe on the road. but there were crashes. multiple accidents occurred when the AI misidentified objects or failed to react to unusual road conditions, like broken lane markings or unpredictable human behavior.
”the devil is in the details”—it’s often the small, overlooked things that cause the biggest problems. tesla’s AI worked well in ideal conditions but couldn’t handle the unpredictability of real roads.
stat alert: according to the national transportation safety board (ntsb), 23 tesla autopilot-related crashes were reported between 2016 and 2019.
recommendation: AI should always be treated as an assistive tool, not a replacement for human oversight. developers need to test AI for “edge cases”—rare situations that the AI may not have been trained to handle, but that happen in real life.
better an empty purse than a full one with shame
microsoft’s tay chatbot was designed to engage in friendly conversations on twitter. within 24 hours, though, users had manipulated tay into spouting offensive and racist remarks. the bot was learning too quickly, with no safeguards to prevent it from absorbing the worst of the internet.
”better an empty purse than a full one with shame” means that it’s better to have nothing than to gain something dishonorably. in microsoft’s case, it would have been better to hold back tay’s release until safeguards were in place.
recommendation: real-time learning AI needs filters and supervision. whether it’s chatbots or recommendation systems, AI must have boundaries that prevent it from spiraling into undesirable behaviors. gpt-3 research shows that 20% of AI models learning from real-time inputs fail without proper filters.
when two fight, the third rejoices
apple’s credit card, launched in 2019, seemed like a great innovation in financial services—until women began noticing they were offered lower credit limits than men, even when their financial profiles were the same. apple’s AI had unknowingly picked up on gender biases in historical credit data.
”when two fight, the third rejoices” reflects how bias crept into the system while fairness was left out. instead of fixing historical discrimination, the AI simply reinforced it.
recommendation: financial AI needs to be transparent and regularly audited for bias. regulators are increasingly pushing for this, with the european commission calling for stricter AI oversight in finance. being proactive with audits can prevent such incidents.
a lot of smoke but little fire
zoom’s virtual backgrounds became a lifesaver during the pandemic, allowing people to keep their messy rooms private during video calls. but for people with darker skin tones or in low lighting, zoom’s AI sometimes blurred or even erased parts of their face or body.
”a lot of smoke but little fire” suggests that something might seem impressive, but fails to deliver when tested. zoom’s AI sounded like a great feature, but it didn’t work well for everyone.
recommendation: AI products, especially those used by millions, need extensive real-world testing. ensure the AI performs reliably across all conditions and demographics, not just in ideal scenarios. research from pew shows that 65% of AI-powered consumer products perform worse for minority groups.
many roosters crow, but the sun still rises slowly
it’s a common thread with AI. companies promise the world—automated hiring, self-driving cars, financial fairness—but when it comes to the results, progress is often slower than the hype suggests.
”many roosters crow, but the sun still rises slowly” reminds us that while there’s a lot of noise around AI, real progress takes time. tesla’s crashes and amazon’s biased hiring tool are just examples of how AI still has a long way to go.
recommendation: slow down, focus on incremental progress, and resist the urge to overpromise. gartner research shows that up to 85% of AI projects fail to deliver on their initial promises due to unrealistic timelines or poor data foundations.
you must dig the well before you get thirsty
it’s easy to get excited about the possibilities of AI, but without proper preparation, things can go wrong quickly. training AI takes time, good data, and solid safeguards.
”you must dig the well before you get thirsty” means that you have to put in the work up front if you want AI to succeed later. rushing the process only guarantees failure.
recommendation: companies must prioritize proper data curation and safeguards before launching AI projects. this includes bias detection, real-world testing, and the human oversight that ensures AI doesn’t go off the rails. mckinsey reports that AI projects with robust foundations are three times more likely to deliver sustainable value.
even the sun has its spots
AI is amazing, no doubt. it’s solving problems we didn’t even know we had and making life easier in many ways. but, as we say in sweden, ”even the sun has its spots.” no matter how bright or impressive AI seems, it still has its imperfections.
as we move forward into a world more and more powered by AI, we need to remember that. it’s not perfect, and it needs our guidance. we can’t leave everything up to machines—not yet, anyway.
final thoughts
AI is powerful, but it’s not a silver bullet. it’s a tool that needs to be used carefully, with oversight, preparation, and an understanding of its limitations. from hiring tools to virtual backgrounds, AI has the potential to do great things, but only if we handle it with care.
every stumble teaches us something new. so let’s learn, adjust, and keep moving forward—mindfully, and with both eyes open.
Development | Portfolio | Innovation 
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