Joy Buolamwini: Real-World Consequences of Algorithmic Bias
In an era where artificial intelligence governs everything from resume screening to predictive policing, the concept of the Coded Gaze has become essential to understanding how seemingly objective technologies can produce deeply unequal outcomes. Coined by MIT researcher and activist Joy Buolamwini, the term refers to the invisible, often unexamined biases embedded in AI systems by their creators—a reflection of who holds power in tech, and who is often excluded from its design.
The Coded Gaze challenges the myth of algorithmic neutrality. Every dataset is a decision. Every model encodes a worldview. When those decisions are made without input from diverse communities or ethical scrutiny, the result is a digital infrastructure that reinforces existing social inequities. The systems may appear neutral, but they act with the biases of their training data—and of the people who chose that data in the first place.
This blog explores how the Coded Gaze shows up in real-world AI failures, what groups are most impacted, and how enterprise leaders, developers, and policymakers can begin to detect and dismantle it. Whether you’re deploying facial recognition, AI-driven hiring tools, or financial scoring algorithms, understanding the Coded Gaze is no longer optional—it’s operationally essential.
Here are key questions addressed in this article—frequently searched and SEO-optimized for real-world relevance:
What is the Coded Gaze in artificial intelligence?
The Coded Gaze refers to the biases and worldviews embedded into AI systems during their creation. It reveals that algorithms mirror the social, cultural, and institutional norms of their designers.
How does the Coded Gaze impact facial recognition accuracy?
Facial recognition tools have consistently underperformed on darker-skinned and female faces, especially when trained on non-representative datasets. This can lead to wrongful arrests, surveillance, or exclusion from essential services.
What are examples of algorithmic bias in hiring and credit scoring?
AI hiring systems have penalized resumes that include female-coded terms or non-traditional job histories. Credit algorithms have disadvantaged communities based on ZIP code, education level, and social data—reproducing historic inequality.
Who is most harmed by biased AI systems?
Communities most impacted include Black and Brown individuals, women, LGBTQ+ people, immigrants, and people with disabilities—especially when they are underrepresented in the data or misrepresented by it.
What can companies do to reduce AI bias?
Enterprises can commission algorithmic audits, adopt fairness standards, document datasets transparently, and include diverse voices in AI development. Tools like data nutrition labels and impact assessments are also increasingly vital.
Understanding and addressing the Coded Gaze is about more than avoiding negative press—it’s about engineering systems that reflect democratic values, not just technical capabilities. For the enterprise sector, it’s a clear mandate: build AI with foresight, or risk building injustice at scale.
What Is the Coded Gaze?
Joy Buolamwini introduced the term “Coded Gaze” to challenge one of the most persistent myths in technology: that algorithms are neutral. In reality, every line of code, every dataset, and every model decision reflects human choices—choices informed by cultural context, access to power, and institutional bias. The Coded Gaze describes how these choices often favor the worldviews of dominant groups—particularly white, male, and Western technologists—who historically have had disproportionate influence over the development of AI systems.
The metaphor draws a parallel with the “male gaze” in cinema, a concept in feminist theory that describes how film and media often portray women through the perspective of heterosexual men. Similarly, the Coded Gaze reveals how AI systems tend to reflect and reinforce the social positioning of those who create them. In practice, this means that tools optimized on narrow datasets, created by homogenous teams, and validated by dominant norms of success, routinely misrepresent or exclude entire populations.
The most widely cited evidence of this phenomenon is Buolamwini’s own study, Gender Shades, conducted in partnership with Dr. Timnit Gebru. The research evaluated commercial facial analysis systems from IBM, Microsoft, and Amazon, revealing stark disparities in performance based on race and gender. While light-skinned men were classified with 99% accuracy, dark-skinned women saw error rates as high as 34%. These discrepancies weren’t due to a bug in the software—they were the logical outcome of a development pipeline that trained models predominantly on white male faces and failed to test for edge cases, which in this context meant the global majority.
This study made the Coded Gaze not only visible—but measurable. It transformed a theoretical critique into an empirical case for regulatory intervention and ethical redesign. It also reframed the public conversation: instead of asking whether AI was accurate, the question became accurate for whom?
Critically, the Coded Gaze reveals that biased outcomes are not accidental—they are designed, even if unintentionally. When AI systems are trained on incomplete or exclusionary datasets, when developers lack cultural competence, and when performance metrics don’t account for demographic variance, the resulting harm is systemic, scalable, and largely invisible until someone names it.
Buolamwini’s framing doesn’t just critique existing technology; it offers a diagnostic tool. It invites engineers, designers, and executives to interrogate their own assumptions and to recognize that every model encodes a point of view. In doing so, the Coded Gaze becomes not only a lens through which we understand injustice, but a roadmap for building systems that work differently—more transparently, more inclusively, and more equitably.
Real-World Failures: When the Coded Gaze Scales
The consequences of the Coded Gaze go far beyond tech labs—they shape how people experience opportunity, safety, and autonomy. Below are three critical areas where biased AI systems have produced real-world harm:
1. Facial Recognition and Law Enforcement
Facial recognition systems have been adopted by police departments across the U.S. and globally, often with minimal oversight. But these systems have disproportionately misidentified people of color—leading to wrongful arrests, surveillance, and incarceration. In one widely reported case, Robert Williams, a Black man in Detroit, was arrested and jailed based on a false match from a facial recognition system. The Coded Gaze embedded in these tools turns systemic bias into automated injustice.
2. Hiring Algorithms and Employment Platforms
AI-driven hiring tools, used by major employers to sort resumes or analyze video interviews, have also replicated discriminatory patterns. In 2018, Amazon scrapped an AI recruiting tool that showed bias against women for technical roles because the system had been trained on past hiring data skewed toward male applicants. Here, the Coded Gaze surfaced as a preference for male-dominated professional language and histories, disadvantaged women and non-traditional candidates.
3. Credit Scoring and Financial Access
Algorithms used to assess creditworthiness increasingly rely on alternative data and opaque decision-making processes. Studies have found that such systems can penalize borrowers based on ZIP codes, educational backgrounds, or social media activity—factors that disproportionately affect marginalized communities. The Coded Gaze in financial systems encodes historical economic exclusion and institutional racism into predictive models.
Who’s Harmed—and How
The consequences of algorithmic bias don’t land equally. They disproportionately affect those who are already underserved, underrepresented, and over-surveilled. People of color, women, immigrants, LGBTQ+ individuals, people with disabilities, and low-income communities are not just afterthoughts in many AI systems—they are often left out of the data entirely or misrepresented within it. These are the same groups historically marginalized by institutions—and now, they’re being marginalized by machines trained on those same institutions’ patterns.
When the Coded Gaze shapes an algorithm, the result isn’t just a technical flaw. It’s a scalable, automated form of discrimination that reaches into every corner of daily life—from access to housing and employment to whether one is flagged by a surveillance system. These harms are not abstract. They are deeply personal and increasingly pervasive.
They show up in three major ways:
Material Harm
This is the most visible and immediate consequence: people being denied opportunities, resources, or even their freedom because of biased AI outputs. It looks like an applicant being screened out of a job interview by a hiring algorithm that favors certain speech patterns. It looks like a bank declining a loan application due to skewed credit models. It looks like an innocent person being falsely matched by facial recognition software and wrongfully arrested. These are not hypothetical scenarios—they are happening now, at scale.
Psychological Harm
Beyond the tangible losses, algorithmic bias can exact a mental and emotional toll. Being misclassified, ignored, or inaccurately scored by an algorithmic system communicates a message: you don’t belong. You are an edge case. This kind of digital erasure creates feelings of invisibility, mistrust, and alienation. For people whose identities have long been marginalized, being misread by a machine is not just frustrating—it’s retraumatizing.
Civic Harm
When biased algorithms are used in systems of governance—such as predictive policing, public benefits eligibility, or voter outreach—the very foundation of democracy begins to erode. Communities lose faith in institutions that deploy AI without oversight or transparency. Biased tools in education may determine who gets into an advanced program. Flawed risk assessments in the justice system may influence sentencing. In all cases, the outcome is the same: technology that was meant to enhance governance ends up replicating injustice in faster, more opaque ways.
The Coded Gaze doesn’t just result in a few bad outputs or occasional errors—it deepens systemic inequality while presenting itself as neutral and efficient. It cloaks discrimination in the authority of math and scale. That’s what makes it dangerous—and why confronting it must be a priority not only for ethicists, but for anyone building, deploying, or profiting from AI.
Emerging Standards for Fairness and Accountability
Addressing the Coded Gaze requires more than ethical intentions—it requires concrete operational standards for fairness, accountability, and transparency. Fortunately, a new ecosystem of practices and tools is beginning to emerge:
Bias Audits: Making Performance Gaps Visible
One of the most direct ways to confront the Coded Gaze is through bias audits—structured evaluations that assess how an AI system performs across different demographic groups. These audits are typically conducted by third-party experts and test systems for disparities in outcomes based on race, gender, age, or other protected characteristics. For example, a hiring algorithm might be found to favor male applicants for engineering roles, or a facial recognition system might perform significantly worse on Black female faces than on white male ones.
Bias audits transform ethical aspirations into measurable benchmarks, allowing organizations to identify harm before deployment and remediate failures proactively. Importantly, these audits also send a public signal: that the company is willing to interrogate its technology rather than hide behind claims of neutrality.
Impact Assessments: Anticipating Harm Before It Happens
Like environmental impact reports before breaking ground on construction, algorithmic impact assessments (AIAs) require developers to evaluate potential harms before an AI system is released. These assessments ask critical questions: Who might be negatively affected by this model? What data is being used to train it? What are the social and economic contexts in which it will operate?
Impact assessments are more than a paperwork exercise. They are a structural pause point—a chance to course-correct before damage is done. When done thoroughly, they help shift AI from a “move fast and break things” culture to one of precaution, foresight, and ethical due diligence.
Data Documentation Standards: Transparency from the Ground Up
Much of algorithmic bias stems from the data itself—who is represented, how they are labeled, and what assumptions are baked into the training process. Initiatives like Datasheets for Datasets and Data Nutrition Labels aim to make these assumptions visible. Created with the same logic as nutrition facts on food packaging, these tools provide metadata about a dataset’s origin, collection methods, intended use, and known limitations.
By adopting transparent documentation standards, developers can better assess whether their data introduces bias—and communicate those risks clearly to stakeholders. This also enables accountability across the supply chain, helping regulators, end users, and civil society better evaluate the tools they’re asked to trust.
Inclusive Design Teams: Fixing the Problem at Its Source
You cannot correct for bias downstream if it is designed in upstream. That’s why inclusive design teams are foundational to ethical AI. This doesn’t just mean checking boxes for gender or race; it means intentionally building teams with varied lived experiences, educational backgrounds, disciplines, and cognitive approaches.
Diverse teams are more likely to question default assumptions, flag potential harm, and design for edge cases—people and communities most often excluded from datasets and decision paths. When inclusion is embedded into the design process, blind spots shrink, and systems become more representative of the world they’re meant to serve.
Regulatory Frameworks: Turning Principles into Policy
Voluntary ethics alone won’t protect the public. That’s why governments are now stepping in to enforce standards around AI fairness, safety, and transparency. In the U.S., the Algorithmic Accountability Act proposes mandatory impact assessments for high-risk AI systems. In Europe, the EU AI Act introduces a tiered system of risk classification and compliance obligations, requiring companies to document, test, and justify the behavior of their AI tools.
These laws are a critical step toward ensuring that AI systems are subject to the same public safeguards as any other infrastructure that affects human rights. They elevate accountability from good practice to legal requirement—transforming the Coded Gaze from an invisible influence into a visible liability.
A Call for Responsible Deployment
Understanding the Coded Gaze is not a matter of academic interest—it’s a frontline issue with tangible, often irreversible consequences. As artificial intelligence becomes more deeply integrated into sectors like healthcare, finance, education, employment, and law enforcement, the risks of deploying flawed or biased systems are no longer hypothetical. These technologies are already influencing who gets hired, who receives medical treatment, who gets flagged by police, and who is granted—or denied—basic resources. The margin for error is shrinking, while the scale of impact is expanding.
For developers, executives, and policymakers, this means one thing: AI governance can no longer be treated as an afterthought. The cost of ignoring algorithmic bias isn’t limited to public backlash or short-term PR crises—it includes legal liability, regulatory penalties, systemic discrimination, and a loss of public trust that’s far harder to repair than to prevent. The reputational risks alone can undermine years of innovation. But more importantly, the ethical and civic costs ripple far beyond balance sheets. When AI systems fail, they often do so quietly and disproportionately, harming the most vulnerable while preserving a false sense of objectivity and progress.
Responsible deployment begins with acknowledging that technology is never neutral. Every dataset reflects human choices—what gets included, what gets excluded, and who defines success. Every model encodes a worldview, whether stated or assumed. To ignore that is to allow the Coded Gaze to operate unchecked. To confront it is to begin designing with intention.
That means integrating fairness audits and algorithmic impact assessments as part of the build process, not as an after-the-fact fix. It means cultivating inclusive teams that bring different perspectives to the table, challenging monocultures that so often lead to blind spots. And it means engaging with the communities that will actually live with the outcomes of these systems—not just as users, but as co-creators and stakeholders.
The Coded Gaze offers us a lens not only for critique but for change. It reminds us that equity isn’t a feature to be added—it’s a foundation that must be built in. When organizations choose to lead with this awareness, they don’t just mitigate risk; they become stewards of a future where AI doesn’t reproduce harm, but helps repair it.
Ultimately, to code with care is to design with dignity. It is a commitment not just to building smarter systems, but fairer ones. And in the long run, that’s not just the right thing to do—it’s the most sustainable, scalable, and socially responsible path forward for enterprise, for governance, and for society at large.
Works Cited
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1–15.
Raji, I. D., & Buolamwini, J. (2019). Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 429–435.
Executive Office of the President. (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. The White House.
New York City Local Law 144 (Automated Employment Decision Tool Law), NYC Council (2021).
European Commission. (2024). EU AI Act: New Rules for Artificial Intelligence.
Williams, R. (2020). I Was Wrongfully Arrested Because of Facial Recognition. Why Are Police Departments Still Using It? The Washington Post.
AJL. (2023). Algorithmic Justice League Campaign Archive. Retrieved from https://www.ajl.org
Klover.ai. “From MIT to Congress: How Joy Buolamwini Is Rewriting AI Policy.” Klover.ai, https://www.klover.ai/from-mit-to-congress-how-joy-buolamwini-is-rewriting-ai-policy/.
Klover.ai. “Joy Buolamwini’s Algorithmic Justice League Playbook.” Klover.ai, https://www.klover.ai/joy-buolamwinis-algorithmic-justice-league-playbook/.
Klover.ai. “Joy Buolamwini.” Klover.ai, https://www.klover.ai/joy-buolamwini/.
