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AI and Algorithmic Justice: Will Technology Empower or Discriminate?

Artificial Intelligence and Algorithmic Justice: A Balanced Approach

Artificial Intelligence (AI) is one of the most advanced technologies that we have today. It has the power to process large amounts of data and make decisions in real-time. However, AI is not always reliable, and its applications can be biased, causing harm to various individuals and groups. That’s why algorithmic justice has emerged as a crucial issue in AI. It is the practice of applying ethical principles and human rights to achieve a more equitable and inclusive use of AI systems.

The Importance of Algorithmic Justice

The use of AI in various industries can range from healthcare to law enforcement, and it’s implemented to ease the workload and make better and more informed decisions. However, if left unchecked, these AI systems can cause harm to certain communities. Algorithms learn from data, which can cause bias against certain groups. This can be especially problematic in fields like criminal justice, where some algorithms are used to predict recidivism rates and the likelihood of committing another crime.

As algorithms learn from data, they become better at identifying patterns, but that doesn’t mean that these patterns represent reality accurately. For example, in the United States, Black people are disproportionately arrested and incarcerated, leading algorithms to incorrectly target them more often for future offenses. If we don’t take steps to manage algorithmic bias, we run the risk of perpetuating harmful and discriminatory systems. This can also impact hiring practices, credit scores, and mortgage rates, leading to systemic discrimination.

Case Studies of Bias in AI Systems

One of the most famous examples of algorithmic bias is the “Compas” case, which used an algorithm to predict recidivism rates for criminals in Florida. It was found that this AI system was twice as likely to flag Black defendants as high risk, but the predictions were often wrong. In comparison, white defendants labeled low-risk were more likely than Black defendants to reoffend.

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Amazon has also come under scrutiny for using AI in its hiring process. Studies showed that the algorithm consistently downgraded resumes of women and favored male applicants. Amazon has since discontinued the use of AI in its hiring process.

Another notable example is facial recognition technology. A study found that facial recognition algorithms had an error rate of up to 34.7% for darker-skinned individuals, compared to 0.8% for lighter-skinned individuals. This is particularly concerning, as facial recognition technology is used in law enforcement, border control, and even for unlocking your phone. It could lead to innocent people being accused of crimes or barred from entering countries or events.

The Need for Balance

While algorithmic justice is vital, it is also essential to find a balance between protecting rights and collecting data. Data is essential for AI algorithms to learn, and too many regulations could limit progress. However, AI systems should be transparent and accountable, and their decision-making processes should be explainable to the public.

Moreover, researchers, policymakers, and organizations must recognize that AI is not neutral, and its implementation has socioeconomic implications. Including a diverse set of insights and opinions from various individuals and communities can ensure that these AI systems do not perpetuate existing biases, leading to more equitable systems.


In conclusion, algorithmic justice should be a central part of AI design and implementation. It ensures that AI systems are transparent, accountable, and seek to protect vulnerable populations. However, finding the right balance between ethical considerations and the usefulness of AI systems can be challenging. It’s essential to recognize that AI has implications in society, and we must work together to develop equitable, fair, and inclusive AI platforms.

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Let’s make sure that AI works for us, not against us. With a responsible approach to building and implementing AI, we can create better systems that benefit all individuals and groups, regardless of race, gender, or socioeconomic status.


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