Ethical Problems in Algorithmic Applications in Criminal Justice Settings

 

Title: Ethical Problems in Algorithmic Applications in Criminal Justice Settings

Introduction

In recent years, the use of algorithmic applications in criminal justice settings, such as sentencing, recidivism risk prediction, and predictive policing, has become increasingly prevalent. However, this implementation raises ethical concerns as it may disproportionately impact marginalized communities. This essay will explore two potential ethical problems associated with these algorithmic applications, drawing from Cathy O’Neil’s “Weapons of Math Destruction” and Ruha Benjamin’s discussion of “the new Jim Code.”

I. Biases and Discrimination

One of the major ethical problems in algorithmic applications within criminal justice settings is the perpetuation of biases and discrimination. O’Neil argues that these algorithms often rely on biased data and proxies, leading to unfair outcomes for certain groups. In her book, she states:

“The algorithmic systems are based on historical data that reflect patterns of discrimination against minorities. By incorporating these data points into the models, the algorithms essentially learn and perpetuate those biases” (O’Neil, Weapons of Math Destruction).

This reliance on historical data can reinforce existing social inequalities and disproportionately target marginalized communities. For example, predictive policing algorithms may prioritize certain neighborhoods based on past crime rates, leading to over-policing in already disadvantaged areas.

Furthermore, the “new Jim Code,” as discussed by Ruha Benjamin, highlights how algorithmic decision-making can mirror and perpetuate racial biases. Benjamin argues:

“Automated systems are only as good as the data they are fed, and when the data is biased, the outcomes will be too” (Benjamin, The New Jim Code).

This suggests that algorithmic applications in criminal justice settings can further entrench systemic racism by reinforcing biased assumptions and decisions.

II. Lack of Transparency and Accountability

Another significant ethical problem with algorithmic applications in criminal justice is the lack of transparency and accountability surrounding their use. O’Neil emphasizes that many of these algorithms are treated as black boxes, where their inner workings are kept secret from the public. She states:

“The secrecy surrounding these algorithms makes it difficult for the public to understand how decisions are being made or to challenge unfair outcomes” (O’Neil, Weapons of Math Destruction).

This lack of transparency raises concerns about due process and accountability. When individuals are subjected to algorithmic decision-making without understanding the factors involved or having the ability to contest the results, their rights may be undermined.

The case of Loomis in Wisconsin exemplifies this issue. Loomis was sentenced to six years in prison based on a risk assessment algorithm. However, the specific details of the algorithm were not disclosed to the defense or the judge. This lack of transparency prevented Loomis from effectively challenging the outcome and understanding how his risk level was determined.

Conclusion

The use of algorithmic applications in criminal justice settings presents significant ethical problems that need to be addressed. Biases and discrimination embedded within these algorithms can perpetuate existing inequalities and unfairly target marginalized groups. Furthermore, the lack of transparency and accountability undermines due process and individuals’ ability to challenge unfair outcomes. To mitigate these issues, it is crucial to ensure that these algorithms are designed with fairness, transparency, and accountability in mind. This requires inclusive data collection practices, rigorous scrutiny of algorithmic decision-making processes, and increased public awareness and involvement in shaping these technologies.

By addressing these ethical concerns, we can strive to create a more just and equitable criminal justice system that harnesses the power of technology without compromising fundamental rights and social justice.