ALGORITHMIC BIAS AND DATA INJUSTICE

Algorithms, a complex set of instructions, have become integral to our daily lives. They play a significant role in various decision-making processes, from search engine rankings to college applications and even influencing environmental decisions . However, these algorithms may contain inherent bias based on the data used to create them. 

Algorithmic bias and data justice are interconnected in several ways, as biases in algorithms often stem from biased or unfair data. In that case, the algorithm will learn from and sustain those biases. By ensuring data justice, we can work towards eliminating algorithmic biases and promote the ethical and equitable use of data and its social, economic, and environmental implications on our society.

 Through Youth4Datajustice, we want to help youth develop their social awareness in order to understand the effect of data injustice and algorithmic bias in the following contexts:

• Privacy and Consent: Young people should know what data is being collected about them and for what purpose. 
• Data Exploitation: Youth should be aware of targeted advertising and other practices that might exploit their vulnerability or lack of digital literacy.
• Education: By educating young people about their digital rights, including how their data is used online and how to protect themselves from improper data usage, we can empower youth to make informed decisions about their online presence and data sharing.

EXAMPLES OF SOCIAL DATA INJUSTICE & ALGORITHMIC BIAS

• BIOMETRIC: Facial recognition software for surveillance, security, and even things such as traffic cameras have shown bias and are inaccurate in identifying people of color.
• EDUCATION: Biased algorithms in standardized testing or college admissions could disadvantage certain groups of students, limiting their access to quality education and future opportunities.
• POLICING: Biased algorithms might target specific neighborhoods, leading to over-policing of minority youth. Additionally, biased algorithms can influence sentencing and parole decisions, leading to harsher punishments for certain youth offenders.
• MEDIA: Social media platforms, news, and online content recommendation algorithms can reinforce biases, spreading discriminatory and harmful content. Youth, who are especially heavy users of online platforms nowadays, might be given incomplete information that could shape their opinions in negative ways.
• FASHION: Biased algorithms could push harmful beauty standards or promote toxic behavior, impacting the self-esteem and well-being of young individuals.

EXAMPLES OF ENVIRONMENTAL DATA INJUSTICE & ALGORITHMIC BIAS

• Air and Water Quality Monitoring: If monitoring stations and testing sites are primarily located in wealthier areas, algorithms could underestimate pollution levels in low-income neighborhoods, leading to a biased understanding of these environmental indicators. This bias can affect policy decisions.
• Climate Change Impact Assessment: If historical data primarily reflects the experiences of certain demographics, algorithms might underestimate the potential impact on disadvantaged communities, leading to insufficient preparedness and inadequate resource allocation for those who are most affected.
• Natural Disaster Response: Algorithms used in predicting natural disasters and planning emergency responses might be biased based on historical data. If response plans are built on historical data that disproportionately favors certain demographics, such as affluent or majority populations, the response efforts might not adequately address the needs of marginalized communities during and after disasters, leading to unequal distribution of resources and support.
• Wildlife Conservation and Biodiversity Monitoring: Algorithms used to assess the impact of human activities on wildlife and biodiversity might be biased if data primarily comes from protected or easily accessible areas. This could lead to overlooking the environmental impact on marginalized communities who live in proximity to these areas, disrupting their livelihoods and traditional practices.
• Toxic Waste Sites and Industrial Facilities: If these algorithms are biased against underserved communities, these communities may bear a disproportionate burden of environmental hazards.

 Using such real-life example to convey complex facts and data, we can educate youth to make informed decisions about their surroundings and advocate for a more sustainable future.