Why is AI bias unethical?
AI bias is unethical because it can violate individuals' rights to meaningful explanations in automated decision-making, perpetuate human prejudices, and undermine fairness and trust in AI systems. Addressing unwanted bias and upholding fairness requires a thoughtful focus on data, diverse teams, and empathy, as both an ethical imperative and a legal responsibility.
What are some famous examples of AI bias?
There are a few famous examples of AI bias, including the COMPAS system. This was a tool used in criminal justice, which has been found to unfairly assess African American defendants and potentially lead to unjust sentencing. Google Translate has also faced criticism for perpetuating gender stereotypes in translations, reflecting societal biases present in its training data. Additionally, Google Photos has been known to mislabel photos of African Americans, highlighting racial bias in facial recognition algorithms.
Looking to hire?
Join our newsletter
Join thousands of subscribers already getting our original articles about software design and development. You will not receive any spam, just great content once a month.
Read Next
What is Exploratory Data Analysis? Steps & Examples
One of the most important things you can do when approaching a data science project is really understand the dataset you’re working with as a first step. Without a proper data exploration process in place, it becomes much more challenging to identify critical issues or successfully carry out a deeper analysis of the dataset. Exploratory Data Analysis (EDA) in Data Science is a step in
Data Preprocessing Techniques: 6 Steps to Clean Data in Machine Learning
The data preprocessing phase is the most challenging and time-consuming part of data science, but it’s also one of the most important parts. Learn best techniques to prepare and clean the data so you don’t compromise the ML model.