Imagine a bridge connecting two major cities, designed to support the weight of thousands of vehicles per day. Now imagine that, during its construction, someone decides to save time and money by using lower-quality materials. At first, everything seems to work. Cars move, the economy flows, and life goes on. But one day, without warning, the bridge collapses. The consequences are devastating: lives lost, millions in damages, and broken trust that will take years to repair.
In the world of programming, a bad decision can be that defective material. At first glance, it may seem insignificant: a poorly implemented line of code, an insufficiently secure API, or a biased algorithm. However, the impact can escalate to unforeseeable levels, causing both social and economic damage.
The Social Disaster: When Technology Betrays Humanity
In 2018, a bug in a facial recognition system used by police in the UK led to the wrongful arrest of dozens of innocent people. The cause? An algorithm programmed without considering the diversity of human faces. This poor programming decision not only affected the lives of those arrested but also called into question the ethics of artificial intelligence, sowing distrust in a technology that promised security. These types of errors are more common than we imagine. From applications that leak sensitive information to systems that unconsciously discriminate, every line of code can have a real impact on the lives of millions of people. The social consequences are profound: distrust in institutions, polarization of communities, and, in extreme cases, the collapse of critical systems such as healthcare or justice.
The economic impact: Multi-million dollar losses and destroyed reputations
In the economic sphere, a programming error can cost billions. An emblematic example is the Knight Capital incident in 2012, when a glitch in its trading software caused losses of $440 million in 45 minutes, bringing the company to the brink of bankruptcy. A programming error not only affected the company but also briefly destabilized the stock market, affecting thousands of investors.
Furthermore, cyberattacks are often facilitated by poor programming decisions, such as leaving vulnerabilities unpatched or using default passwords. The costs of a successful attack are not limited to direct financial losses; they also include reputational damage, customer loss, and legal costs. Companies like Equifax are still struggling to regain public trust after cyberattacks that exposed the data of millions of users.
The lesson: Every line of code matters.
The power of programmers lies not only in creating technology, but in anticipating its potential impacts. Every line of code has the potential to build a solid bridge or one destined to collapse. The solution is not to program faster or cheaper, but to program better, with ethics, rigor, and a long-term vision.
A small mistake or a hasty programming decision may seem insignificant at the time, but the domino effect can be catastrophic. Because, in the end, the real cost of a bad programming decision is not measured only in lost money, but in the trust and well-being of the people who depend on the technology.
Some real-life examples from the last 15 years that show the social and economic impact of bad programming decisions:
Background: A software error in the Maneuvering Characteristics Augmentation System (MCAS) was the primary cause of two fatal accidents that left 346 dead.
Problem: The software was designed to automatically correct the plane's pitch, but it relied on a single sensor. If the sensor failed, the system erroneously activated the descent, leading to inevitable collisions. Boeing failed to adequately train pilots on how to operate this system, underestimating the need to explain it. Consequences: Boeing's financial losses exceeded $20 billion in fines, compensation, and a drop in stock prices. Global distrust in aviation safety was generated.
Background: A flaw in Facebook's API programming and policies allowed Cambridge Analytica to collect data from 87 million users without their consent.
Problem: The API's design prioritized mass data collection for third parties, without strict controls over the use of the information. Adequate measures were not implemented to protect user privacy. Consequences: Financial and reputational losses for Facebook, including a $5 billion fine from the FTC. Significant social impact, as the data was used to manipulate elections, such as the Brexit campaign and the 2016 US presidential election.
Background: Volkswagen implemented software in its vehicles that cheated emissions tests, showing lower results than actual emissions.
Problem: The software was programmed to recognize when the car was undergoing testing and temporarily adjusted its performance to meet environmental standards. Outside of testing, the cars emitted up to 40 times more nitrogen oxides than permitted. Consequences: More than $30 billion in fines and legal costs. Massive damage to the environment and consumer confidence in the automotive industry.
Background: A security flaw in the software used by Equifax allowed hackers to access the personal information of 147 million people.
Problem: Equifax failed to apply a known security patch to its Apache Struts software. The exposed data included names, Social Security numbers, addresses, and credit card numbers. Consequences: Fines of $700 million in court settlements. Millions of people exposed to identity theft and fraud.
Background: A ransomware attack affected the UK National Health Service's systems, crippling hospitals and critical services. Problem: The ransomware exploited a known vulnerability in Windows, for which Microsoft had already released a patch. Many NHS facilities had not updated their systems, leaving them vulnerable. Consequences: More than 19,000 medical appointments were canceled and serious disruptions to medical care. Financial losses estimated at £92 million.
Background: Google Photos' auto-tagging feature misclassified Black people as "gorillas."
Problem: The machine learning algorithm was insufficiently trained on diverse data, resulting in racist biases in recognition. Consequences: Significant reputational damage to Google. Sparked debates about ethics in artificial intelligence and biases in training data.
Background: The Robinhood trading app mistakenly displayed that users could trade with unlimited negative balances due to a bug in its interface.
Problem: The interface design failed to properly warn about the risks of leverage, leading some users to take on massive debt. One user, who believed they owed more than $700,000, ended up taking their own life. Consequences: Criticism for the lack of social responsibility and transparency. Mandatory changes to the platform's interface and policies.
