Algorithmic accountability refers to the issue of where accountability should be apportioned for the consequences of real-world actions that were taken on account of algorithms used to reach a decision.
In principle, an algorithm should be designed in such a way that there is no bias behind the decisions that are made during its execution process. That is, the algorithm should evaluate only essential characteristics of the inputs presented, without making distinctions based on characteristics that usually should not be used in a social environment, such as the ethnicity of an individual who is being judged in a court of law. However, this principle may not always respected and on occasions individuals may be deliberately harmed by these outcomes. It is at this point that the debate arises about who should be held responsible for the losses caused by a decision made by the machine: the system itself or the individual who designed it with such parameters, since a decision that harms other individuals due to lack of impartiality or incorrect data analysis will happen because the algorithm was designed to perform that way.
Algorithm usage
The algorithms designed nowadays are spread out in the most diverse sectors of society that have some involvement of computational techniques in their control systems, of the most diverse sizes and with the most varied applications, being present in, but not limited to medical, transportation and payment services. In these sectors, the algorithms embedded in the applications perform activities of natures such as:
Approve/deny credit card applications; Vote counting in elections; Approve/deny immigrant visas; Decide which taxpayers will be audited on their income taxes; Managing the system that controls self-driving cars on a highway; Scoring certain individuals as possible criminals for future use at trial.
The way these algorithms are implemented, however, can be quite confusing. Effectively, algorithms in general behave like black boxes, and in most cases it is not known the process that an input data goes through during the execution of a particular routine, but only the resulting output linked to what was initially entered. In general, there is no knowledge related to the parameters that make up the algorithm and how biased to certain aspects they can be, which can end up raising suspicions about the bias with which an algorithm treats a set of inputs. It depends on the outputs that are generated after the executions and if there is any individual who feels harmed by the result presented, especially when another individual, under similar conditions, ends up getting a different answer. According to Nicholas Diakopoulos:
Wisconsin Supreme Court case
As mentioned before, algorithms are widespread in the most diverse fields of knowledge and make decisions that affect the lives of the entire population. Moreover, their structure and parameters are often unknown by those who are affected by them. A case that illustrates this well was a recent ruling by the Wisconsin Supreme Court regarding so-called "risk assessment" for crime. It was ruled that such a score, which is computed through an algorithm that takes various parameters from individuals, cannot be used as a determining factor for an accused to be arrested. In addition, and more importantly, the court ruled that all reports submitted to judges in such cases should contain an information related to the accuracy presented by the algorithm used to calculate the scores.
This event has been considered a major victory in the sense of how the data-driven society should deal with softwares that operates making decisions and how to make them reliable, since the use of these algorithms in highly complex situations like courts requires a very high degree of impartiality when treating the data provided as input. However, defenders of concepts related to big data argue that there is still much to be done regarding the accuracy presented by the results of algorithms, since there is still nothing concrete regarding how we can understand what is happening during data processing, leaving room for doubt regarding the suitability of the algorithm or those who designed it.
Controversies
Another case where there is the possibility of biased execution by an algorithm was the subject of an article in The Washington Post discussing the passenger transportation tool Uber. After analyzing the data collected, it was possible to verify that the estimated waiting time for users of the service was higher depending on the neighborhood where these individuals lived. The main factors affecting the increase in time were the majority ethnicity and the average income of the neighborhood.