Transparency in Data Collection

Importance of Transparency in Data Collection

• Crucial for building trust, improving data quality, and ensuring accountability, especially for marginalized communities.
• Openly sharing data collection methods and purposes enhances trust, reassuring communities that their data will not be misused.
• Participants provide more honest and complete information when they understand the reasons for data collection.
• Lack of transparency can lead to mistrust, causing people to refuse participation or provide false information.
• Ensures ethical and legal data collection, reducing the risk of exploitation.
• Organizations and researchers can be held accountable for data misuse.

Case Study 1: Indigenous Data Sovereignty & the Māori People (New Zealand)

• The Māori people have historically been excluded from decision-making processes affecting their land, health, and education.
• Data about the Māori population was collected without their consent and used in ways that reinforced systemic inequalities.
• Government agencies and researchers collected health, economic, and social data on Māori communities without their involvement.
• Data often led to misrepresentation of Māori needs and priorities.
• The Māori people had no control over data usage, resulting in policies that did not align with their actual needs.
• The Indigenous Data Sovereignty movement emerged, demanding Māori ownership, control, access, and decision-making power over their data.
• Organizations like Te Mana Raraunga (Māori Data Sovereignty Network) advocated for transparent and ethical data collection.
• Government agencies were required to consult Māori communities before collecting data.
• Policies related to Māori health, education, and land were created with direct community involvement.
• The Māori people became more willing to share data, knowing it would be used ethically and transparently.
• The movement influenced discussions on Indigenous data rights globally, making data sovereignty a key policy issue.

Case Study 2: Racial Bias in Predictive Policing (United States)

• Many U.S. cities use predictive policing algorithms to forecast crime locations and allocate police resources.
• These AI systems are trained on historical crime data, reflecting past patterns of policing, including racial profiling.
• AI models disproportionately targeted Black and Latino neighborhoods due to biases in historical crime data.
• The public lacked access to how these algorithms worked, leading to concerns about racial discrimination.
• Communities were over-policed, reinforcing systemic biases in the criminal justice system.
• Civil rights organizations (e.g., ACLU) filed lawsuits demanding public access to predictive policing algorithms.
• Journalists and researchers used Freedom of Information Act (FOIA) requests to uncover how police departments used AI systems.
• Community advocacy groups raised awareness about the biases, forcing some cities to reevaluate their use of predictive policing.
• Several police departments in cities like Los Angeles and Oakland stopped using predictive policing software due to transparency concerns.
• The case sparked national conversations about bias in AI and law enforcement.
• Many organizations pushed for open-source AI models that allow public scrutiny of decision-making processes.
• Some communities filed lawsuits demanding ethical use of policing data to prevent future discrimination.

Case Study 3: Open Data for Slum Communities – Map Kibera (Kenya)

• Kibera, one of Africa’s largest informal settlements, was long neglected by the government.
• It was not included on official maps, meaning residents lacked access to basic services like water, electricity, and healthcare.
• Without proper data, policymakers ignored Kibera when allocating resources.
• Lack of official data meant Kibera’s population was underestimated, and services were not provided.
• Policymakers claimed they could not justify funding improvements due to the absence of data on Kibera.
• Residents were excluded from decision-making due to the lack of means to prove their needs.
• Local residents launched Map Kibera, a community-driven project using open-source mapping tools to document their neighborhood.
• They collected real-time data on roads, schools, healthcare centers, and water points, creating the first detailed map of Kibera.
• The project was completely transparent, allowing residents to update and access data freely.
• Government and aid organizations started recognizing Kibera as a real community with real needs.
• Roads were built, healthcare services improved, and water access was expanded.
• Residents took control of their own data, using it to demand better services from the government.
• The success of Map Kibera led to similar projects in other marginalized communities worldwide.