Black Box Algorithms

Blackbox Algorithms

Introduction

A black box algorithm is one where the user cannot see the algorithm's inner workings. It is a somewhat controversial system due to its secrecy and lack of transparency. However, its creators defend it as a security and privacy system to avoid data leaks and unfair competition.[1] Black box algorithms are computer programs that make predictions or decisions based on a set of inputs. Still, the internal workings of the algorithm are not visible or transparent to the user. These algorithms are increasingly being used in areas such as criminal justice, finance, and healthcare, and their use has raised many important ethical and legal questions. One of the main concerns about black box algorithms is the potential for bias and discrimination. Since the internal workings of the algorithm are not transparent, it can be challenging to detect and correct any biases that may be built into the system. This can lead to unfair and unjust outcomes, particularly for marginalized groups such as people of color and low-income individuals.

Examples of Black Box Algorithms

COMPAS

As technology advances, more and more decisions affecting individuals are being made by hidden algorithms in our society. One area where this has raised concern is in the criminal justice system, specifically regarding the fairness and due process of these algorithms. One example of a widely-used, yet secretive algorithm is COMPAS (which stands for Correctional Offender Management Profiling for Alternative Sanctions) has been the focus of many legal challenges.[2] COMPAS is a an algorithm that is frequently employed in the criminal justice system that is intended to calculate the risk that offenders would reoffend. In order to help ensure public safety and lower recidivism, the algorithm is used to help guide choices concerning bail, sentence, and parole.

The ability of COMPAS to lessen prejudice and discrimination in the criminal justice system is one of its main advantages. The algorithm can assist in ensuring that choices concerning bail, sentencing, and parole are based on objective criteria rather than on subjective judgments or prejudices by employing a standardized, data-driven approach. By doing this, you may make sure that criminals receive proper punishment and that the judicial system is more egalitarian.

The effectiveness of the criminal justice system can be improved by COMPAS. The algorithm can help to ensure that offenders are placed in the proper degree of supervision and that resources are directed to those who are most likely to reoffend by providing a more precise and objective estimate of the risk of recidivism. By doing so, you can improve public safety and lower the overall cost of the criminal justice system.

Despite these advantages, there are significant drawbacks to using COMPAS. The algorithm's lack of transparency and difficulty in comprehending how it generates predictions is one of the primary issues. Due to this, it may be challenging to verify if the algorithm is impartial and free from prejudice.

Ethical Concerns

Bias

In addition to inheriting many of the best traits of the human brain, artificial intelligence has also demonstrated that it knows how to use it skillfully and ably. Object recognition, map navigation, and speech translation are just a few of the many skills that modern AI programs have mastered, and the list will not stop growing anytime soon. [3] Unfortunately, as a result of this it has amplified the bias that humans have. AI can help identify and reduce the impact of human biases, but it can also make the problem worse by baking in and deploying biases at scale in sensitive application areas. [4] For example, Propublica analyzed data from Broward County FLorida and they claimed that the prediction model used there (COMPAS) is racially biased.

Accountability

Another concern is that black box algorithms may lack accountability. Because it is difficult to understand how the algorithm arrives at its predictions, it can be difficult to hold those who created or use the algorithm responsible for any negative consequences. This can make it difficult to ensure that the algorithm is being used ethically and in compliance with laws and regulations.

Transparency

One of the main ethical concerns about black box algorithms is their lack of transparency. Black box algorithms are so-called because they are opaque, meaning that it is difficult or impossible to understand how they arrive at their predictions or decisions. This can make it difficult to ensure that the algorithm is fair and unbiased, and that it is not inadvertently causing harm to certain groups of people. For example, in the medical field if they are unable to figure out where the reliable knowledge is coming from, should they blindly believe that it is correct? There is a growing concern that the black box nature of AI makes it impossible to ground the reliability of the algorithm and, consequently, on whether researchers, physicians and patients can trust the results of such systems. [5] Biological systems are so complex, and big-data techniques are so opaque, that it can be difficult or impossible to know if an algorithmic conclusion is incomplete, inaccurate, or biased.And these problems can arise due to data limitations, analytical limitations, or even intentional interference.[6]