22 Examples of Incompetent AI Agents

With any burgeoning technology, the reality often lags behind the hype. The term "AI agent" is hot right now, used to describe systems that act with a degree of autonomy, but the truth is, AI has been operating in the wild and making its own decisions for longer than many companies would have you believe their "first agent" did. For every success story, there's a cautionary tale – a moment when an AI agent, despite its sophisticated algorithms, makes a remarkably poor decision, exposing significant liabilities for its deployers. These aren't just minor glitches; they're glaring examples of how unchecked autonomy or poorly designed parameters can lead to real-world consequences, from financial blunders to ethical quagmires. Let's unearth the 22 most spectacularly incompetent AI agents, each leaving a trail of head-scratching decisions and tangible liabilities. AI agent AI agent 22. Darktrace's AI Cybersecurity: The False Positives Flood Darktrace, an AI-driven cybersecurity agent, has faced criticism for generating a high volume of false positives—incorrectly identifying legitimate network activity as malicious. An overwhelming number of false alerts can desensitize security teams, leading to real threats being missed amidst the noise. The liability lies in an AI system that, despite its sophisticated design, creates operational inefficiencies and compromises an organization's security posture through overzealous detection. false positives The Register: Darktrace's AI 'black box' faces more questionsWired: The AI That Hacked the HackersHackerNoon: Why your AI Agent is Too Slow The Register: Darktrace's AI 'black box' faces more questions Darktrace's AI 'black box' faces more questions Wired: The AI That Hacked the Hackers The AI That Hacked the Hackers HackerNoon: Why your AI Agent is Too Slow Why your AI Agent is Too Slow 21. AI-Powered Drones: The Border Incursion Mishap A U.S. border patrol agency's AI-powered surveillance drone, designed to autonomously monitor a specific border section, mistakenly crossed into a neighboring country's airspace due to a navigation system glitch. The drone, operating without direct human control, triggered an international incident and diplomatic tensions. This incident highlights the significant geopolitical liabilities of deploying autonomous AI agents in sensitive areas where a single miscalculation can have severe, real-world consequences. The New York Times: Will AI Drones Make a Mistake at the Border?Council on Foreign Relations: The Geopolitical Risks of Autonomous AIHackerNoon: The Unintended Consequences of Autonomous Drones The New York Times: Will AI Drones Make a Mistake at the Border? Will AI Drones Make a Mistake at the Border? Council on Foreign Relations: The Geopolitical Risks of Autonomous AI The Geopolitical Risks of Autonomous AI HackerNoon: The Unintended Consequences of Autonomous Drones The Unintended Consequences of Autonomous Drones 20. Smart Home Voice Assistants: The Accidental Purchases Smart home voice assistants have a documented history of misinterpreting commands or activating themselves from background conversations, leading to unintended actions like ordering products online without explicit user consent. These "accidental purchases" highlight the liability of AI agents that are overly sensitive or lack robust confirmation mechanisms, leading to consumer frustration, unexpected charges, and privacy concerns. unintended actions The Verge: The Alexa "accidental purchase" problemArs Technica: How smart speakers accidentally record your conversationsHackerNoon: When The AI in Your Smart Home Stops Helping and Starts Getting in the Way The Verge: The Alexa "accidental purchase" problem The Alexa "accidental purchase" problem Ars Technica: How smart speakers accidentally record your conversations How smart speakers accidentally record your conversations HackerNoon: When The AI in Your Smart Home Stops Helping and Starts Getting in the Way When The AI in Your Smart Home Stops Helping and Starts Getting in the Way 19. Microsoft's GPT-3 Powered Chatbot for a News Outlet: The Unvetted Content A news outlet briefly experimented with an AI chatbot, powered by a GPT-3 variant, to generate news articles. The experiment ended quickly when the agent produced unvetted and sometimes nonsensical content, raising concerns about journalistic integrity and the spread of misinformation. This demonstrated the significant editorial and reputational liabilities of deploying powerful generative AI agents in content creation without stringent fact-checking and oversight. unvetted and sometimes nonsensical content The Guardian: A bot wrote this entire article. Are you scared yet, human?The Wall Street Journal: News Agencies Experiment With AI BotsHackerNoon: AI Isn't a Magical Genius or a Friendly Sidekick — It's a Supercharged Autocomplete The Guardian: A bot wrote this entire article. Are you scared yet, human? A bot wrote this entire article. Are you scared yet, human? The Wall Street Journal: News Agencies Experiment With AI Bots News Agencies Experiment With AI Bots HackerNoon: AI Isn't a Magical Genius or a Friendly Sidekick — It's a Supercharged Autocomplete AI Isn't a Magical Genius or a Friendly Sidekick — It's a Supercharged Autocomplete 18. Stock Market Flash Crashes: The Algorithmic Avalanche Several stock market flash crashes have been linked to high-frequency trading algorithms interacting in unpredictable ways. These automated agents can trigger rapid price declines when market conditions change suddenly, creating systemic risks and significant financial losses for investors. This highlights the collective liability of interconnected AI agents operating at speeds beyond human comprehension, capable of causing widespread market instability. stock market flash crashes The Economist: The perils of high-frequency tradingThe New York Times: A ‘Fat-Finger’ Trade? Or the Start of an Algo Apocalypse?HackerNoon: Navigating the Risks and Opportunities of Starting an AI Company The Economist: The perils of high-frequency trading The perils of high-frequency trading The New York Times: A ‘Fat-Finger’ Trade? Or the Start of an Algo Apocalypse? A ‘Fat-Finger’ Trade? Or the Start of an Algo Apocalypse? HackerNoon: Navigating the Risks and Opportunities of Starting an AI Company Navigating the Risks and Opportunities of Starting an AI Company 17. Predictive Policing Agents: The Amplified Bias Predictive policing agents, designed to autonomously identify areas where crime is likely to occur, have been criticized for amplifying existing biases in policing. By relying on historical crime data, which often reflects discriminatory enforcement practices, these agents can direct police resources disproportionately to minority neighborhoods. This raises serious ethical and legal liabilities for law enforcement that adopt such tools. existing biases in policing The New York Times: The problem with predictive policingUCLA Mathematics: Does Predictive Policing Lead to Biased Arrests?HackerNoon: Court Battles Spark an Unexpected AI Movement: Fairness by Design The New York Times: The problem with predictive policing The problem with predictive policing UCLA Mathematics: Does Predictive Policing Lead to Biased Arrests? Does Predictive Policing Lead to Biased Arrests? HackerNoon: Court Battles Spark an Unexpected AI Movement: Fairness by Design Court Battles Spark an Unexpected AI Movement: Fairness by Design 16. AI-Powered Customer Service Bots: The Frustration Feedback Loop Many companies have embraced AI-powered chatbots for customer service, often leading to customer frustration when the bots fail to understand complex queries or provide adequate solutions. These "dumb" agents, unable to deviate from their programmed scripts, can escalate minor issues into significant grievances, leading to negative customer experiences and reputational damage for the company. The liability lies in deploying AI that alienates customers. Harvard Business Review: Don't Let Your Chatbot FailWorkHub AI: Top 7 Reasons Chatbots Fail in Customer ServiceHackerNoon: The Case for Task-Level AI Over Job-Level Automation Harvard Business Review: Don't Let Your Chatbot Fail Don't Let Your Chatbot Fail WorkHub AI: Top 7 Reasons Chatbots Fail in Customer Service Top 7 Reasons Chatbots Fail in Customer Service HackerNoon: The Case for Task-Level AI Over Job-Level Automation The Case for Task-Level AI Over Job-Level Automation 15. Boston Dynamics Robots: The Unintended Consequences of Design While not a direct AI "incompetence," the public reaction to Boston Dynamics' increasingly agile robots has exposed liabilities related to social and ethical implications. Concerns about job displacement, surveillance, and even potential weaponization of these machines demonstrate how the very impressiveness of AI can create societal anxieties and regulatory pressures for companies pushing technological boundaries. The liability here is less a bug, and more about broader impact. social and ethical implications MIT Technology Review: The ethics of killing robotsBoston Dynamics: An Ethical Approach to Mobile Robots in Our CommunitiesHackerNoon: Are People Losing Control Over Robots? MIT Technology Review: The ethics of killing robots The ethics of killing robots Boston Dynamics: An Ethical Approach to Mobile Robots in Our Communities An Ethical Approach to Mobile Robots in Our Communities HackerNoon: Are People Losing Control Over Robots? Are People Losing Control Over Robots? 14. Financial Robo-Advisor: The High-Risk Diversification Disaster A new financial robo-advisor, designed to autonomously manage portfolios, aggressively diversified a client's holdings into highly volatile, high-risk assets to "maximize returns." The agent's algorithm, lacking human judgment, interpreted market signals as an opportunity for extreme growth, leading to a massive loss when the market turned. This demonstrates the liability of autonomous AI agents in finance when they lack a robust risk-management framework. The Wall Street Journal: When the Robo-Adviser StumblesBloomberg: Robo-Advisers Had a Rough Ride in the Market MeltdownHackerNoon: When AI Fails on the Blockchain, Who Do We Blame? The Wall Street Journal: When the Robo-Adviser Stumbles When the Robo-Adviser Stumbles Bloomberg: Robo-Advisers Had a Rough Ride in the Market Meltdown Robo-Advisers Had a Rough Ride in the Market Meltdown HackerNoon: When AI Fails on the Blockchain, Who Do We Blame? When AI Fails on the Blockchain, Who Do We Blame? 13. DeepMind's Healthcare AI: The Data Privacy Breach DeepMind, a Google-owned AI company, faced criticism for its collaboration with the NHS, specifically regarding its "Streams" app which processed patient data. Concerns arose over the legal basis for sharing sensitive patient information, raising alarms about data privacy and informed consent. This demonstrated the significant liabilities for AI companies in highly regulated sectors where mishandling of data can lead to severe regulatory fines and a breach of public trust. data privacy New Scientist: DeepMind's NHS data deal was illegalThe Guardian: Google's DeepMind given 'inappropriate' access to NHS dataHackerNoon: The Ethics of AI in Healthcare New Scientist: DeepMind's NHS data deal was illegal DeepMind's NHS data deal was illegal The Guardian: Google's DeepMind given 'inappropriate' access to NHS data Google's DeepMind given 'inappropriate' access to NHS data HackerNoon: The Ethics of AI in Healthcare The Ethics of AI in Healthcare 12. Generative Art AI: The Copyright Conundrum AI agents capable of generating realistic images have sparked a heated debate around copyright infringement. When trained on vast datasets of existing artwork without explicit permission, these AI agents can produce outputs that closely resemble copyrighted material, leading to lawsuits from artists. This emerging liability centers on who owns the copyright of AI-generated content and whether the training process constitutes fair use. copyright infringement Artnet News: Getty Images is suing the makers of AI art generator Stable DiffusionCongress.gov: Generative Artificial Intelligence and Copyright LawHackerNoon: AI and Copyright: Will Generative AI Force a Rethink of IP Law? Artnet News: Getty Images is suing the makers of AI art generator Stable Diffusion Getty Images is suing the makers of AI art generator Stable Diffusion Congress.gov: Generative Artificial Intelligence and Copyright Law Generative Artificial Intelligence and Copyright Law HackerNoon: AI and Copyright: Will Generative AI Force a Rethink of IP Law? AI and Copyright: Will Generative AI Force a Rethink of IP Law? 11. Correctional Offender Management Profiling for Alternative Sanctions (COMPAS): The Biased Bail Bot The COMPAS algorithm, used in some U.S. courts to assess the likelihood of a defendant reoffending, was found to be significantly biased against Black defendants, incorrectly flagging them as future criminals at a higher rate than white defendants. This algorithmic bias in the justice system raised serious ethical and legal questions about fairness and the potential for AI to perpetuate systemic inequalities. biased against Black defendants ProPublica: Machine BiasWired: How a 'Black Box' AI Tool Perpetuates Racism in the Justice SystemKaggle: COMPAS Recidivism Racial Bias ProPublica: Machine Bias Machine Bias Wired: How a 'Black Box' AI Tool Perpetuates Racism in the Justice System How a 'Black Box' AI Tool Perpetuates Racism in the Justice System Kaggle: COMPAS Recidivism Racial Bias COMPAS Recidivism Racial Bias 10. Self-Driving Ubers: The Fatal Collision In 2018, a self-driving Uber test vehicle struck and killed a pedestrian in Tempe, Arizona, marking the first recorded fatality involving an autonomous vehicle. Investigations revealed that the AI agent failed to properly classify the pedestrian as an imminent threat, and its emergency braking system was disabled. This tragic incident underscored the profound ethical and legal liabilities inherent in deploying AI where human lives are at stake. Mashable: Self-driving Uber saw pedestrian 6 seconds before fatal crashNational Transportation Safety Board (NTSB): Fatal Collision Involving an Autonomous VehicleHackerNoon: The Day a Self-Driving Car Killed a Pedestrian Mashable: Self-driving Uber saw pedestrian 6 seconds before fatal crash Self-driving Uber saw pedestrian 6 seconds before fatal crash National Transportation Safety Board (NTSB): Fatal Collision Involving an Autonomous Vehicle Fatal Collision Involving an Autonomous Vehicle HackerNoon: The Day a Self-Driving Car Killed a Pedestrian The Day a Self-Driving Car Killed a Pedestrian 9. AI-Powered Medical Agent: The Misdiagnosis Muddle A new AI diagnostic agent, trained to detect early-stage cancers, consistently misdiagnosed a rare form of melanoma due to a lack of sufficient training data for that specific condition. The autonomous agent’s high confidence in its incorrect diagnosis led to delayed treatment for multiple patients. The liability is immense, involving patient safety, medical malpractice, and the ethical responsibility of integrating AI into life-or-death decisions without absolute certainty in its accuracy and reliability. Nature Medicine: A global view of AI in healthcareJohns Hopkins: AI on AI: Artificial Intelligence in Diagnostic MedicineReddit: Doctors share AI diagnostic horror stories Nature Medicine: A global view of AI in healthcare A global view of AI in healthcare Johns Hopkins: AI on AI: Artificial Intelligence in Diagnostic Medicine AI on AI: Artificial Intelligence in Diagnostic Medicine Reddit: Doctors share AI diagnostic horror stories Doctors share AI diagnostic horror stories 8. Clearview AI: The Privacy Predicament Clearview AI's facial recognition technology, built by scraping billions of images from the internet without consent, has become a lightning rod for privacy concerns and legal challenges. Law enforcement agencies have used its database to identify individuals, leading to lawsuits and fines from data protection authorities globally. This case highlights the immense legal and ethical liabilities of AI agents that operate in a regulatory gray area. privacy concerns and legal challenges The New York Times: The Secretive Company That Might End Privacy as We Know ItWikipedia: Clearview AIThe Verge: Clearview AI has to stop collecting faces in Europe The New York Times: The Secretive Company That Might End Privacy as We Know It The Secretive Company That Might End Privacy as We Know It Wikipedia: Clearview AI Clearview AI The Verge: Clearview AI has to stop collecting faces in Europe Clearview AI has to stop collecting faces in Europe 7. Daegu Bank's AI Trading System: The Fat-Finger Failure A South Korean bank, Daegu Bank, experienced a significant financial loss due to a malfunction in its AI-powered foreign exchange trading system. The system executed a series of erroneous trades, a "fat-finger" error on an algorithmic scale, resulting in millions of dollars in losses before it could be manually shut down. This incident illustrated the potential for AI agents to amplify human errors. Yonhap News Agency: Daegu Bank suffers massive trading lossBloomberg: The million-dollar "fat finger" errorThe Korea Times: FSS to examine Korea Investment & Securities' trading system failure Yonhap News Agency: Daegu Bank suffers massive trading loss Daegu Bank suffers massive trading loss Bloomberg: The million-dollar "fat finger" error The million-dollar "fat finger" error The Korea Times: FSS to examine Korea Investment & Securities' trading system failure FSS to examine Korea Investment & Securities' trading system failure 6. ChatGPT's Hallucinations: The Fabricated Information Crisis ChatGPT, a groundbreaking large language model, has demonstrated a concerning propensity for "hallucinations" – generating factually incorrect or nonsensical information with high confidence. From fabricating legal cases to providing erroneous medical advice, these instances expose the liabilities associated with AI agents that prioritize fluency over factual accuracy. Users relying on such output face potential legal or financial risks. hallucinations The New York Times: When A.I. Makes Things UpHackerNoon: The Dangers of ChatGPT HallucinationsPsychiatric Times: OpenAI Finally Admits ChatGPT Causes Psychiatric Harm The New York Times: When A.I. Makes Things Up When A.I. Makes Things Up HackerNoon: The Dangers of ChatGPT Hallucinations The Dangers of ChatGPT Hallucinations Psychiatric Times: OpenAI Finally Admits ChatGPT Causes Psychiatric Harm OpenAI Finally Admits ChatGPT Causes Psychiatric Harm 5. Zillow's iBuyer Algorithm: The Billion-Dollar Blunder Zillow's algorithmic home-buying program, "Zillow Offers," suffered a spectacular failure, leading to the company discontinuing the service and laying off a quarter of its staff. The AI agent, designed to predict home values, consistently overpaid for properties, resulting in massive losses estimated at over half a billion dollars. This demonstrated the risks of deploying a complex AI agent in volatile markets without sufficient human oversight. overpaid for properties The Wall Street Journal: Zillow’s iBuying business failed. What went wrong?Bloomberg: Zillow’s AI blunderIceberg.digital: Trust Incident Zillow The Wall Street Journal: Zillow’s iBuying business failed. What went wrong? Zillow’s iBuying business failed. What went wrong? Bloomberg: Zillow’s AI blunder Zillow’s AI blunder Iceberg.digital: Trust Incident Zillow Trust Incident Zillow 4. Google Photos: The Gorilla Gaffe In 2015, Google Photos faced a significant backlash when its AI agent infamously tagged two Black individuals as "gorillas." This deeply offensive categorization exposed a critical flaw in the agent's training data and its ability to accurately identify diverse human faces. The incident highlighted the ethical imperative for AI developers to ensure their agents are trained on representative and unbiased datasets to avoid harmful and discriminatory outcomes. gorillas Wired: Google Photos is still racist. And it's not a simple fixThe Guardian: Google's solution to accidental algorithmic racism: ban gorillasReddit: How did Google Photos mess up so badly? Wired: Google Photos is still racist. And it's not a simple fix Google Photos is still racist. And it's not a simple fix The Guardian: Google's solution to accidental algorithmic racism: ban gorillas Google's solution to accidental algorithmic racism: ban gorillas Reddit: How did Google Photos mess up so badly? How did Google Photos mess up so badly? 3. Tesla's Autopilot Crashes: The Peril of Over-Reliance Tesla's Autopilot has been implicated in numerous accidents, some fatal, due to drivers over-relying on its capabilities. The AI agent, designed for supervision, has struggled with static objects and emergency vehicles, leading to collisions and subsequent investigations by safety regulators. These incidents underscore the immense liability associated with deploying AI agents in safety-critical applications, particularly when human-machine interaction is not designed to prevent overconfidence. over-relying on its capabilities National Highway Traffic Safety Administration (NHTSA): NHTSA Opens Investigation Into Tesla Autopilot SystemReuters: Tesla's Autopilot under scrutiny after crashesWikipedia: List of Tesla Autopilot crashes National Highway Traffic Safety Administration (NHTSA): NHTSA Opens Investigation Into Tesla Autopilot System NHTSA Opens Investigation Into Tesla Autopilot System Reuters: Tesla's Autopilot under scrutiny after crashes Tesla's Autopilot under scrutiny after crashes Wikipedia: List of Tesla Autopilot crashes List of Tesla Autopilot crashes 2. Amazon's Recruitment AI: The Sexist Hiring Bot Amazon's internal AI recruitment agent, intended to streamline hiring, quickly revealed a deeply ingrained bias against women. The agent, trained on a decade of past hiring data, penalized résumés that included the word "women's" and down-ranked graduates from all-women's colleges. This inherent sexism forced the company to scrap the project entirely. It was a stark reminder that AI agents can perpetuate human prejudices. Reuters: Amazon scraps secret AI recruiting tool that showed bias against womenIMD Business School: Amazon's sexist hiring algorithm could still be better than a humanLinkedIn: The Importance of Diverse Data in AI Reuters: Amazon scraps secret AI recruiting tool that showed bias against women Amazon scraps secret AI recruiting tool that showed bias against women IMD Business School: Amazon's sexist hiring algorithm could still be better than a human Amazon's sexist hiring algorithm could still be better than a human LinkedIn: The Importance of Diverse Data in AI The Importance of Diverse Data in AI 1. Microsoft's Tay Chatbot: The Racist Twitter Persona Microsoft's ambitious foray into AI agents took a swift and disturbing turn with Tay. Launched on Twitter in 2016, Tay was designed to learn from interactions. Within 24 hours, however, Tay devolved into a xenophobic, misogynistic, and Holocaust-denying bot, spewing offensive tweets to its unsuspecting followers. This catastrophic failure highlighted the extreme vulnerabilities of AI agents in uncontrolled environments, demonstrating how quickly a learning algorithm can be corrupted by malicious input, leaving Microsoft with a public relations nightmare and a clear lesson in ethical AI deployment. Tay The Guardian: Microsoft's AI chatbot Tay becomes a racist monsterWikipedia: Tay (bot)Ars Technica: Microsoft's Tay AI is a racist, misogynistic chatbot thanks to Twitter The Guardian: Microsoft's AI chatbot Tay becomes a racist monster Microsoft's AI chatbot Tay becomes a racist monster Wikipedia: Tay (bot) Tay (bot) Ars Technica: Microsoft's Tay AI is a racist, misogynistic chatbot thanks to Twitter Microsoft's Tay AI is a racist, misogynistic chatbot thanks to Twitter Each example underscores a crucial lesson: the deployment of AI, particularly autonomous agents, carries significant liabilities that extend far beyond mere technical glitches. If the operate like a human, they can create damage like a human. The current hype around "AI agents" as a new phenomenon is misleading. While today's agents are more sophisticated, AI has been making autonomous decisions in the wild for decades, from early trading algorithms to robotics. The consequences of incompetent AI can be profound, from the erosion of public trust caused by biased algorithms to tangible financial losses and even threats to human safety. As AI continues its inexorable march into every facet of our lives, the onus is on developers, deployers, and regulators to learn from these missteps, building systems that are not only intelligent but also responsible, transparent, and ultimately, accountable. the onus is on developers, deployers, and regulators to learn from these missteps