Artificial intelligence (AI) has become an increasingly significant component of many industries around the world. From manufacturing to healthcare, AI has revolutionized the way businesses operate. But despite its many benefits, AI can also perpetuate bias and discrimination. This has led to the development of a new field known as algorithmic justice, which seeks to ensure that AI is used fairly and equitably. In this article, we will explore the concept of algorithmic justice, its importance, and some real-life examples of its implementation.
What is Algorithmic Justice?
Algorithmic Justice is a field of study that aims to create fair and equitable algorithms for the use of AI. It involves identifying and correcting any biases or unfairness that might be present in the algorithms. The goal is to create a more just and equitable society by ensuring that the algorithms and AI systems we use are not perpetuating harmful biases.
The Importance of Algorithmic Justice
AI is based on the data it receives. It ingests large amounts of data, makes predictions based on that data, and then acts on those predictions. However, if the data on which the AI is based is biased or incorrect, then the predictions it makes and the actions it takes will also be biased or incorrect. This is where algorithmic justice comes in.
Algorithmic justice is essential because AI systems can have far-reaching impacts on society. For example, AI can be used to make hiring decisions, assess creditworthiness, or make healthcare decisions. If the algorithm is biased, it can lead to discrimination and harm to individuals and communities. For instance, if an AI system is biased against hiring women, then it can perpetuate the gender disparity in the workplace and prevent qualified women from getting the job.
Real-Life Examples of Algorithmic Justice
The concept of algorithmic justice is still relatively new, but there are already some examples of its implementation in real-life scenarios:
1. COMPAS: In 2016, the state of Wisconsin used an algorithm called COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) to determine whether someone was likely to recidivate, or repeat criminal behavior. However, a ProPublica investigation found that the algorithm was biased against black defendants. The algorithm was more likely to predict that black defendants would re-offend, even when compared to white defendants with similar criminal histories. The bias of the algorithm led to some black defendants being given harsher sentences.
2. Google Photos: In 2015, Google Photos launched a feature that automatically tags photos based on what it perceives in the images. However, the AI behind the feature made an embarrassing faux pas when it tagged photos of black individuals as “gorillas.” This occurred because the algorithm was not trained on enough diverse images to accurately recognize people of all races.
3. The sentencing judge AI: In 2019, a team of researchers released a preprint paper called “Learning to Sentence: Transformer-Based Generation for Legal Text.” In the paper, they created an AI system that could generate sentencing recommendations for judges. The system was trained on thousands of real-world sentencing decisions and used a machine learning algorithm to learn how judges generally comport themselves in order to make decisions that were as neutral as possible, theoretically eliminating racial and socioeconomic biases. The algorithm positively scored the word “black” due in part to the racial identity association, so the researchers had to filter this category from the data.
These examples illustrate a core problem with AI: if biased data is used to train an AI program, the program itself can become biased and amplify the original errors. In all three cases, the bias behind the algorithm was corrected, demonstrating the potential of using algorithmic justice to prevent discrimination in AI.
Conclusion
AI is transforming industries, but it’s essential to also ensure that it’s used fairly and critically. The concept of algorithmic justice aims to ensure that the algorithms behind AI are not biased or discriminatory. It’s essential to develop AI technology that is used for the betterment of all people, not solely for the benefit of specific groups. By recognizing the potential for bias and implementing algorithmic justice, we can create a more inclusive and equitable society. The journey will not be instantaneous – however, a small move is better than none – and ultimately, the price of inaction will be too high.