Question

A "purposefully biased" algorithm used to identify unfair attributes is known as

A: A predictive model
B: An aggregate algorithm
C: A discriminatory algorithm
D: An adversarial algorithm

Answer 1

A purposefully biased algorithm used to identify unfair attributes is known as a discriminatory algorithm. This kind of algorithm can contribute to continued discrimination in various decision-making processes by perpetuating existing biases. Addressing these biases is crucial for ensuring fairness and equality in the technology-driven aspects of society.

Step-by-step explanation:

A "purposefully biased" algorithm used to identify unfair attributes is known as C: A discriminatory algorithm. Such an algorithm is designed to differentiate between individuals or groups in a way that is unfair or unjust. In the context of artificial intelligence and machine learning, biases can occur during the data collection, algorithm design, or decision-making phases, leading to skewed results and potentially discriminatory practices.

For example, biases in hiring algorithms may arise from historical data that reflects past discriminatory practices. This can result in an automated system that perpetuates these biases by favoring certain groups over others, rather than providing an objective assessment of a candidate's suitability for a job. The challenge lies in identifying these biases and creating algorithms that are transparent and fair, thus preventing the perpetuation of discriminatory practices.

Discrimination in algorithms is not just a technical issue; it also has social implications, as automated systems are increasingly used in decision-making processes affecting employment, healthcare, and other critical areas. Addressing algorithmic biases is essential for ensuring fairness and equality in a society where technology plays a pivotal role.

Answer 2

A discriminatory algorithm is a purposefully biased algorithm used to highlight or counteract unfair attributes, leading to concerns about perpetuating discrimination in various applications such as hiring or law enforcement. Issues of representativeness and historical biases in the training data are key factors in producing such algorithms.

Step-by-step explanation:

A "purposefully biased" algorithm used to identify unfair attributes is known as a discriminatory algorithm. In the context of artificial intelligence and machine learning, algorithms can indeed be designed to highlight or mitigate biases depending on their programming and the data they are trained on. The concern about biases in algorithms centers on how they can perpetuate or amplify existing discrimination if not properly accounted for. This is a significant issue in fields such as hiring, lending, and law enforcement, where biased algorithms could lead to unfair outcomes for individuals based on racial, gender, or other characteristics.

Discriminatory algorithms can come about through issues like data that is not representative of the whole population or contains historical biases. Statistical discrimination, another related concept, happens when there's a reliance on flawed statistics that perpetuate stereotypes, which may be used inadvertently in the algorithm's decision-making process. The presence of such biases in algorithms can lead to discrimination, either intentionally or unintentionally, hence the term 'discriminatory algorithm' comprehensively describes an algorithm that results in unfair treatment based on attributes.