For instance, bias in search engine results and social media algorithms can reinforce existing social biases related to race, gender, sexuality, and ethnicity. Additionally, algorithmic bias can influence political partisanship, leading to further societal divisions.
Real-World Impacts
The effects of algorithmic bias are far-reaching. One notable example is facial recognition technology. It has shown failure rates as high as 35% when identifying faces of people of color, especially black women.
Similarly, certain self-driving cars exhibit a reduced ability to detect children and darker-skinned pedestrians, posing significant safety risks.
The legal ramifications of algorithmic bias are also becoming apparent.
There have been numerous lawsuits involving individuals wrongly identified by facial recognition technology and subsequently arrested. One such case involved a pregnant woman who was mistakenly jailed and accused of carjacking based on flawed facial recognition data.
Law enforcement agencies often use software that searches government databases and social media for possible matches, amplifying the consequences of algorithmic inaccuracies.
Global Legal Frameworks
Legal systems are beginning to address these issues through frameworks like the European Union’s General Data Protection Regulation (GDPR) in 2018). And the Artificial Intelligence Act in 2024.
