Breaking the Cycle: Combating Racial Bias in AI-Powered Technologies

Social bias can be reflected and amplified by artificial intelligence in dangerous ways, whether it be in deciding who gets a bank loan or who gets surveilled.

Technology is increasingly governing our world. Our day-to-day lives function with systems and digital platforms at their heart.

While this new digital world holds promise for efficiency and innovation, it is crucial to recognise the potential harm it can inflict, particularly on vulnerable and marginalised individuals and communities.

There are myriad ways in which someone is “other.” And other is not a cultural fit.

How AI bias furthers racial bias

We often assume AI systems are neutral. The assumption of neutrality often stems from the belief that technology operates solely on objective data and calculations. However, this overlooks that humans are responsible for designing, programming, and training these systems.

If unchecked and unregulated AI systems can amplify and perpetuate existing forms of discrimination, such as racism, sexism, and ableism.

Human biases seep into technology at various stages. For instance, biased data collection practices or algorithm design choices can introduce discrimination. If the training data is not diverse or representative, or if certain variables correlating with protected characteristics are used, the resulting algorithms can perpetuate existing biases and inequalities.

Consequently, these biased algorithms can have real-world impacts, affecting hiring processes, loan approvals, criminal justice systems, and social media algorithms. They can amplify social disparities, reinforce stereotypes, and unfairly discriminate against specific groups or demographics.

Data bias

AI algorithms learn from historical data, which may reflect existing societal biases and prejudices. Therefore, if the training data is not diverse or representative, the algorithm can perpetuate those biases, resulting in biased outcomes.

Algorithm design choices

Human designers make decisions about the design and parameters of algorithms, and these choices can introduce or reinforce biases. For example, selecting certain variables or features correlating with protected characteristics (such as gender or race) can lead to biased results.

Proxy variables

Proxy variables in decision-making can contribute to biased outcomes, as they are imperfect indicators of race and can reinforce existing social inequalities. Relying solely on such variables can overlook individual circumstances, personal experiences, and intersectionality.

Limited representation in development

If the teams developing AI systems lack diversity and inclusion, it can lead to blind spots and biases. The lack of representation may result in overlooking the needs and perspectives of marginalized racial groups, inadvertently perpetuating racial bias in the technology they create.

Feedback loops

Biases can also be perpetuated through feedback loops. If biased decisions made by the algorithm are used as input for future decision-making or as training data, the bias can be reinforced and amplified over time.

Lack of transparency

The inner workings of complex algorithms can be opaque and difficult to understand. This lack of transparency can create an illusion of neutrality, as users may need to be aware of the biases in the algorithms they interact with.

The issues of algorithmic bias

Coded Bias is a documentary film directed by Shalini Kantayya. The film explores the issues of algorithmic bias and the potential consequences of relying on artificial intelligence (AI) and machine learning systems in various aspects of society.

The documentary highlights the work of Joy Buolamwini, a computer scientist and founder of the Algorithmic Justice League. Buolamwini investigates the biases and discriminatory impacts of facial recognition technologies. She discovers that these systems tend to misidentify or exclude individuals with darker skin tones and women, raising concerns about these technologies’ fairness and accuracy.

Coded bias also delves into the broader implications of algorithmic bias, including the use of AI in criminal justice systems, predictive policing, employment, and advertising. It explores how algorithmic decision-making can perpetuate existing biases and inequality, exacerbating social divisions and reinforcing discriminatory practices.

The film features interviews with researchers, activists, and experts who share insights into unchecked algorithmic systems’ potential dangers and ethical implications. It raises critical questions about the need for transparency, accountability, and regulation in developing and deploying AI technologies.

Through personal narratives and compelling storytelling, Coded Bias aims to raise awareness about the hidden biases present in algorithms and encourages viewers to critically examine the role of AI in society. It calls for a more inclusive and ethical approach to the design and implementation of algorithms, highlighting the importance of addressing algorithmic bias to ensure fairness, equity, and social justice in the digital age.

Going beyond training data

It is essential to prioritise diversity and inclusion throughout the AI process to address racial bias and promote fairness and social justice. This includes diverse representation in design, model iteration, deployment, and governance of AI systems.

By hiring and fostering teams that include individuals from diverse backgrounds, including women, people of colour, and individuals from various marginalised groups, we can bring a broader range of perspectives and experiences to the development and decision-making processes. This diversity can help uncover biases, challenge assumptions, and create technology that is more inclusive and aligned with the needs and values of different communities.

Diverse and representative data

Ensuring that training data used to develop algorithms is diverse, representative, and free from bias. This may involve carefully curating and preprocessing data and eliminating or mitigating bias in the training dataset.

Algorithmic transparency and accountability

Promoting transparency in algorithmic decision-making systems, making the decision-making process understandable and auditable. This can help identify and address biases and hold algorithmic systems accountable for their outcomes.

Ongoing monitoring and evaluation

Continuously monitoring and evaluating algorithmic systems for potential bias and discriminatory impacts. Regular audits and assessments can help detect and correct bias, ensuring algorithms operate fairly and equitably.

Ethical guidelines and regulations

Establishing clear ethical guidelines and regulations around the development and deployment of algorithms. These guidelines can promote fairness, transparency, and accountability, ensuring that algorithms are designed and used in ways that respect individual rights and societal values.

Interdisciplinary collaboration

Encouraging collaboration between computer scientists, ethicists, social scientists, and domain experts to address code bias comprehensively. This collaboration can provide a more holistic understanding of the societal impacts of algorithms and inform the development of fairer systems.


By actively working to identify, understand, and mitigate AI bias, we can strive towards creating more equitable, inclusive, and unbiased systems that do not perpetuate or further racial bias in society.

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