We will be posting a fair amount about algorithmic bias on this blog. It is central to the explosion of AI bases developments we are currently experiencing.
What is Algorithmic Bias
Algorithmic bias refers to the unfairness that can happen in computer programs or systems that use algorithms. These algorithms make decisions or give results based on certain inputs or data. However, if the data used to train these algorithms is biased or if the assumptions made during their creation are flawed, the algorithms can end up being unfair or discriminatory towards certain groups of people.
For Example
For example, imagine an algorithm used by a company to screen job applicants. If the algorithm is trained on biased data or has flawed assumptions, it might favor certain types of candidates over others, even if they are equally qualified. This can lead to unfair hiring practices and discrimination.
Why is it an issue
Algorithmic bias can happen in different ways. Sometimes, the bias comes from the data itself, which may reflect historical biases or inequalities. Other times, biases can be introduced during the algorithm’s development or by the people using it. For instance, if the developers of an algorithm lack diversity in their team, they may unintentionally overlook certain biases that could affect the algorithm’s fairness.
With the growth of AI deployments in our daily lives addressing algorithmic bias becomes more important to ensure fairness and equal opportunities. It involves using diverse and representative data to train algorithms, thoroughly testing them for bias, promoting transparency in decision-making processes, and establishing ethical guidelines and regulations to prevent discrimination.