Every day, algorithms make thousands of decisions on your behalf. They remember what you like. They predict what you’ll want next. And sometimes, they’re so accurate it feels uncanny.
That news story in your feed this morning wasn’t necessarily the most important development of the day. It was the one designed to keep you reading. The songs you play during your morning commute from your Spotify “Discover Weekly” are picked to match your listening history. And when you open Instagram at lunch, it’s filled with posts it knows you’ll love and curated to keep you scrolling.
You may think you’re just scrolling through your feed. But in reality, your feed is scrolling through you. Sorting, ranking, and selecting millions of bits of information on your behalf.
However, not all of that influence is bad. Some of it makes life easier, faster, and more interesting.
But there’s another side to this quiet influence. Over time, the same systems that simplify your world can also narrow it. And that leads to a difficult question: Where’s the line between guidance and control?
What is an algorithm?
The word “algorithm” might sound abstract, but at its core, it’s just a set of instructions for solving a problem. Like a recipe tells a cook which steps to follow, an algorithm designates which actions to take in a given situation.
For example, when you search online for “best hotels in Chicago,” an algorithm decides which results to show first, factoring in location, reviews, pricing, and your past searches. When your bank blocks a suspicious payment, it’s because the system detected an unusual spending pattern. When a navigation app recommends a route, it’s based on the fastest driving time.
The difference between a recipe and an algorithm is scale and speed. You might follow a recipe to make one meal or organize your day. An algorithm, by contrast, can run thousands of “recipes” in parallel, in milliseconds, across billions of data points. Most of this happens without you ever realizing that a set of rules was quietly making the call.
The dark side of algorithms
Algorithms are not inherently good or bad. They are tools. But like any tool, they reflect the priorities of the people and organizations that design them. And in most commercial situations, the priority is to drive specific business objectives, such as increasing engagement, ad revenue, or sales.
That alignment between business goals and algorithmic design creates a few fundamental tensions. The systems that recommend content, products, or ads are often optimized for metrics that don’t necessarily match a user’s long-term interests or society’s broader goals.
Over time, these tensions can manifest in several ways.
Narrowing exposure
Algorithms tend to amplify what has worked in the past. If you regularly click on one type of content, like political analysis from a particular perspective, the system will prioritize showing you more of the same. Over time, this can narrow your exposure to differing viewpoints, reinforcing existing beliefs.
Eli Pariser described this as the “filter bubble,” and while platforms have made some efforts to diversify recommendations (for instance, the Mozilla Information Trust Initiative), the underlying incentive to keep users engaged often outweighs those goals. This matters beyond news consumption.
For example, in e-commerce, narrowing exposure might mean repeatedly promoting certain brands or price ranges, subtly shaping buying habits in ways the user doesn’t consciously register.
Shaping behavior through design
Some forms of influence are built into the structure of digital platforms. For instance, infinite scroll makes it easier to keep consuming content than to stop. Autoplay features roll one video into the next with no break to reconsider. E-commerce sites position high-margin products or sponsored listings more prominently, increasing the likelihood of purchase without explicitly disclosing the commercial rationale.
These are often described as “nudges”: small design choices that steer users toward certain actions. While not inherently negative, they operate without informed consent, and their cumulative effect can be substantial.
Bias in data and outcomes
Algorithms learn from data, and data often reflects historical inequities.
In hiring tools, if past recruitment disproportionately favored certain demographics, an algorithm trained on that data may replicate those preferences. For example, recruitment algorithms have been found to reflect biases in gendered patterns or even in names. A parallel concern shows up in facial recognition systems. Multiple studies have documented significantly higher error rates for people with darker skin tones, raising concerns about discriminatory outcomes in security and law enforcement contexts.
Bias is not always easy to detect, and without regular auditing, it can persist unnoticed. This is especially critical in business decision-making, where algorithmic outputs can influence who gets hired, which products are promoted, or which customers receive better service.
Algorithms and limited accountability
Many commercial algorithms function as black boxes. The company deploying them may not disclose — or may not fully understand themselves — the decision pathways inside. This lack of transparency creates challenges for both consumers and regulators.
In fact, 80 percent of the general population wishes they knew more about how their personal data is being used online, yet only 29 percent of consumers say they find it easy to understand how well a company protects their data. Without clear explanations or recourse mechanisms, users are left in the dark about why certain decisions are made, eroding trust over time.
The upside of invisible guidance
While the above challenges are real, it’s equally important to acknowledge the ways algorithms add value to our everyday lives, provided they’re designed and deployed responsibly.
Consider how machine learning models in healthcare can pre-empt serious negative outcomes. For instance, in the UK, a stroke-prevention algorithm analyzes anonymized primary care data to flag patients at risk of undiagnosed atrial fibrillation.
Early ECG intervention could prevent thousands of strokes each year. Similarly, algorithms now support mammography screening. Google’s AI-based system was shown to reduce false positives and negatives by several percentage points, improving early breast cancer detection while potentially lowering strain on radiologists.
In everyday life, algorithms also help us make decisions faster and more safely, without privacy intrusion.
Navigation apps like Google Maps or Waze evaluate real-time traffic data to route drivers efficiently and reduce congestion, saving time and fuel.
In education, adaptive learning platforms, such as those used on Khan Academy, personalize content to a student’s pace. This has been shown to improve comprehension and engagement, helping students stay motivated and actively involved in their learning.
These examples show that when algorithms are focused on public good rather than maximizing clicks, they can boost health outcomes, improve safety, support equitable learning, and streamline daily life. While at the same time keeping privacy and transparency front of mind.
The tension between trust and transparency
Convenience often depends on trusting that systems work as intended. Yet without transparency, trust is fragile.
68% of consumers are concerned about the amount of data being collected by businesses. Users, whether individuals or enterprises, increasingly want to know not only what decisions are being made on their behalf, but also why and how those decisions are reached.
Some platforms now offer “Why am I seeing this?” explanations for ads or recommendations. But these are often too vague to provide meaningful understanding, and they rarely give users genuine control over how the system operates.
However, regulatory frameworks are beginning to address this. The EU’s Digital Services Act (DSA) requires large platforms to explain how their recommender systems work and to offer at least one option that is not based on profiling.
In addition, the General Data Protection Regulation (GDPR) establishes a right to “meaningful information about the logic involved” in automated decision-making. And in the US, the California Consumer Privacy Act (CCPA) gives residents the right to know what personal data is collected and how it is used. These initiatives signal a shift toward greater accountability, though practical implementation remains inconsistent.
At the same time, companies face the challenge of balancing transparency with the protection of intellectual property. Revealing too much about proprietary systems could allow for manipulation by malicious actors or erode their competitive advantage.
The solution lies in providing enough clarity for people to make informed choices, without overwhelming them with detail or undermining the system’s integrity.
Imagining a healthier relationship with algorithms
If algorithms are going to remain an invisible but constant presence in our daily lives — in our news, our shopping, our navigation, and our healthcare — the challenge is not to remove them entirely, but to shape how they work for us.
That means making their influence more visible, their goals more aligned with our own, and their benefits available without demanding more personal data than is necessary.
There are several ways this balance could be achieved, but they all rest on the same foundation: giving people agency, clarity, and protection.
Give users genuine control
Instead of locking people into a single recommendation style, platforms could offer adjustable settings, like a slider between “more of what I like” and “show me something new.” News apps could enable readers to prioritize local over national coverage, or hard news over entertainment, depending on the moment. The point is to put the steering wheel back in the user’s hands.
Embed privacy by design
Data collection should be minimal, intentional, and transparent. Privacy by design can help with this. That includes collecting only the data necessary for a given purpose, explaining why it’s needed in plain language, and building protections in from the start. It’s the difference between asking for trust and earning it.
This approach, already central to frameworks like the GDPR, is one of the clearest ways to close the trust gap that 68 percent of consumers say they feel when it comes to corporate data practices.
Audit for bias and fairness
Algorithms also need independent review. Biases in data or design can go unnoticed until they produce measurable harm, from excluding qualified job candidates to misidentifying individuals in security systems. Having diverse teams work on these tools and systems, along with regular third-party audits that assess fairness, accuracy, and compliance with ethical standards, can prevent or surface these issues early. And when companies share the results publicly, they send a powerful message about accountability.
Invest in digital literacy
Understanding how algorithms work should not be limited to engineers. Business leaders, policymakers, and everyday users alike benefit from knowing the basics: what drives a recommendation, what data is used, and what trade-offs are involved. This knowledge turns people from passive recipients into informed participants in the systems that shape their lives.
Finding the balance between help and harm
Algorithms now influence much of what we read, watch, and buy. They can connect us with life-saving medical interventions or send us down endless rabbit holes of distraction and misinformation.
They can make a niche business visible to the right customer at the right moment, or they can keep showing us the same narrow slice of the world.
The real question is not whether algorithms should exist, but under what principles they should operate. Should they be optimized purely for engagement, or should they also be measured against fairness, privacy, and the long-term well-being of the people who use them?
The answer will decide whether these systems remain primarily engines of commercial optimization or whether they become tools that balance business goals with public good.
That decision isn’t just in the hands of engineers. Regulators set guardrails. Companies choose what to prioritize. And users can demand greater clarity, better protections, and more meaningful control.
In the end, the healthiest digital future is one where influence is not hidden, where personalization is not the same as manipulation, and where trust is earned through transparency and respect for privacy.