What Are Algorithms and Why Do They Matter?

In simple terms, an algorithm acts like a recipe: it provides a set of instructions that guides a computer to perform a task. Moreover, in the world of Big Data, algorithms analyze massive amounts of information to find patterns, predict scenarios, and make decisions. As a result, industries rely on them as essential tools.

Everyday Examples:

• Video Streaming: Netflix suggests series you’ll probably enjoy based on what you’ve already watched.
• Finance: Banks use algorithms to decide who qualifies for a loan, considering factors such as credit history.
• Social Media: Instagram’s algorithm determines which posts appear first in your feed, shaping your online experience.

While useful, algorithms are not impartial. They reflect the intentions of their creators and the data they were trained on, which can lead to errors or injustices.

What Is Big Data and Why Is It Important?

Big Data refers to the enormous volumes of data generated every day. For example, this includes everything from your Google searches to your online purchases. However, it’s not just about quantity — Big Data also involves speed and variety, making it a complex yet powerful tool.

The Three V’s of Big Data

• Volume: Think billions of social media posts or global transactions.
• Velocity: These data are generated in real-time, requiring instant analysis.
• Variety: Texts, images, videos, audio files, sensor data — all of this is Big Data.

How Big Data and Algorithms Connect

Algorithms turn this sea of data into something useful, organizing and processing information to provide actionable insights. However, this reliance on algorithms also raises important questions about ethics and transparency, which we’ll explore further below.

The table below summarizes the main issues related to the use of algorithms across different sectors:

Who Controls the Algorithms?

Large corporations and governments control most of the algorithms we use daily. For instance, Google, Facebook, and Amazon design algorithms that decide what appears in your feed, the ads you see, and even the prices of the products you buy. Consequently, these companies exert significant influence over our daily lives. Similarly, governments use algorithms to monitor suspicious activities, predict crimes, and allocate public resources.

And here’s the issue: control is centralized. This raises two key questions:

1. Lack of public access: What happens inside these algorithms is like a state secret. We don’t know how they work or how they arrive at certain conclusions.
2. Conflicts of interest: How can we be sure these tools are being used for the common good and not for economic or political gain?

Why Transparency Is Fundamental

ransparency seems like a simple word, but it plays a key role in ensuring that algorithms are fair and trustworthy. Algorithmic transparency involves opening the “black box” of algorithms, helping us understand how they work, what data they use, and how they reach their decisions. In an increasingly digital world, this clarity is not just desirable — it becomes essential.

“It’s like asking us to trust a self-driving car without knowing who programmed it or whether it has been tested in extreme conditions.”

What Do We Gain With Transparency?

• Accountability
  Imagine an algorithm makes a mistake that negatively impacts someone’s life — for example, perhaps denying a benefit or selecting job candidates in a biased way. In such cases, transparency makes it easier to identify these problems and, moreover, to hold those who developed or implemented the system accountable. Ultimately, this ensures greater fairness and trust in the process.
• Fairness
  Without transparency, how do we correct unfair decisions? When we understand how algorithms work, we can identify and eliminate biases, ensuring decisions are more equitable for everyone.
• Public Trust
  Trust is the foundation of any relationship, including between users and technology. When people know that an algorithm is being used fairly and ethically, their acceptance increases. Transparent systems build this bridge of trust.

“What if you can’t access a job because an algorithm decided you’re not a good fit? Who explains that decision?”

What Is Being Done to Promote Transparency and Ethics?

While ethical challenges and the lack of transparency in algorithms may seem like complex problems, there are already initiatives underway to address these issues. From government regulations to educational efforts, different sectors are mobilizing to ensure the use of these technologies is fairer and more responsible. Let’s explore some of these actions.

1. Legislation and Regulation

Governments have begun to recognize the importance of establishing clear rules for the use of algorithms. This includes creating laws that promote transparency and hold organizations accountable for the impact of their automated decisions.

An Inspiring Example: The city of New York took a proactive approach by passing the Automated Decision Systems Law. This law requires algorithms used in hiring processes to undergo audits to identify and correct potential biases. The goal is to ensure that opportunities are distributed more equitably.

2. Independent Audits

It’s not enough to trust organizations to self-regulate. Independent audits are gaining traction as a way to evaluate the fairness and effectiveness of algorithms. These assessments help identify flaws before they cause harm.

Real-World Example: The tech company Accenture conducts regular audits of its AI systems to ensure they meet ethical standards. This initiative helps build trust and sets an example for other organizations to follow.

3. Education and Public Awareness

While regulations and audits are essential, educating the public is also a key part of the equation. Empowering people to understand the impact of algorithms and how to question them is a crucial step toward the future.

A Promising Initiative: The “Data Detox Kit“ project provides resources to help individuals understand how their data is collected and used. Additionally, it teaches simple practices to protect privacy and promote more conscious technology use.

Algorithms and Sensitive Decision-Making

Algorithms are increasingly involved in decisions that deeply affect people’s lives, particularly in critical areas such as justice, healthcare, and public safety. While they offer incredible opportunities, they can also have serious, and often irreversible, consequences.

In the Legal System

In the United States, systems like COMPAS are used to predict the likelihood of criminal recidivism. However, studies have shown that these algorithms often display racial biases, harming minority groups. These failures not only reflect existing biases in the data but also perpetuate injustices that should be addressed.

In Healthcare

Hospitals have adopted algorithms to help prioritize patients on treatment lists. Sounds efficient, right? But there’s a problem: when these systems are trained with historical data, they can inherit biases. This means patients from marginalized groups, who have historically been underserved, may continue to be overlooked.

Want to understand how algorithms are influencing critical decisions and exacerbating inequalities, especially in healthcare? Check out the article below from the Harvard T.H. Chan School of Public Health, which explores the impacts and ethical challenges of this technology.

Algorithmic healthcare exacerbates social inequalities

Learn More ➝

The Role of Big Data in Amplifying Ethical Issues

Big Data is the engine that powers algorithms, providing the data necessary for them to function. However, it also intensifies ethical challenges that cannot be ignored. When data is collected invasively or used without users’ consent, serious problems begin to emerge.

The Impact of Big Data on Algorithmic Bias

Algorithms are only as good as the data they are given. If the data is biased — as is often the case — the algorithms will inevitably replicate and amplify these prejudices.

Real-World Example: Policing data from specific areas can reinforce racial biases. Marginalized communities that are already disproportionately monitored end up being further targeted, creating an unfair cycle of surveillance.

Open Data Culture: An Alternative

One promising solution is promoting open data, allowing independent experts to analyze and identify biases. This approach not only increases transparency but also creates opportunities to address problems before they cause greater harm.

Explainable and Ethical Algorithms: The New Frontier

To address the opacity of algorithms, the concept of Explainable AI (XAI) has emerged. These systems provide clear justifications for the decisions they make, increasing trust and making error detection easier.

Practical Example: Imagine a bank using AI to approve or deny credit. With explainable algorithms, the customer can understand exactly why they were denied and what steps could improve their chances of approval in the future.

Innovative Technologies Supporting Transparency

• Blockchain: This technology allows tracking the decisions made by algorithms, creating a transparent and auditable history.
• Open Source Tools: Platforms like TensorFlow foster collaboration among developers and experts, ensuring systems are continually reviewed and improved.

The Road Ahead: Transparency and Accountability

Algorithms are so ingrained in our lives that we often don’t even notice their impact on critical decisions — from the credit we receive to the information we consume. That’s why discussing their transparency and ethics is no longer just a concern for specialists; it has become an essential issue for all of us.

It is crucial for governments, companies, and society to work together to create practical solutions. This includes clearer regulations, broad education on the impact of Big Data, and the development of systems that are fairer and more explainable. These steps are vital to ensure that technology progresses hand in hand with human values.

In the end, algorithms must remain tools created to benefit people, not to control them. But this reality will only be possible if we act now, before invisible decisions shape the future without our voice or choice.

“Technology is advancing rapidly, but we cannot let it set the rules on its own. The future of transparency and ethics depends on all of us. Shall we start now?

Bibliographic references:

Imai, K.; Jiang, Z.; Greiner, J.; Halen, R.; Shin, S. Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment. arXiv preprint arXiv:2012.02845, 2020. Disponível em: https://arxiv.org/abs/2012.02845. Acesso em: 08 dez. 2024.

Ferrara, E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. arXiv preprint arXiv:2304.07683, 2023. Disponível em: https://arxiv.org/abs/2304.07683. Acesso em: 08 dez. 2024.

Malek, M. A. Criminal Courts’ Artificial Intelligence: The Way It Reinforces Bias and Discrimination. AI and Ethics, v. 2, n. 2, p. 233-245, 2022. Disponível em: https://link.springer.com/article/10.1007/s43681-022-00137-9. Acesso em: 08 dez. 2024.

Castro-Toledo, F. J.; Miró-Llinares, F.; Aguerri, J. C. Data-Driven Criminal Justice in the Age of Algorithms: Epistemic Challenges and Practical Implications. Criminal Law Forum, v. 34, p. 295-316, 2023. Disponível em: https://link.springer.com/article/10.1007/s10609-023-09454-y. Acesso em: 08 dez. 2024.