Khalil Gibran Muhammad - Big Data

Summary

Khalil Gibran Mohammed, a professor at Harvard, explores the biases in big data and its historical roots, emphasizing that demographic data was initially used to justify discrimination against racial groups. He highlights how early 20th-century social scientists collected data to assess the suitability of immigrants like the Irish and Italians for American society. Focusing on African Americans, he points out that while extensive data exists on their societal challenges, it often reinforces stereotypes of inferiority rather than promoting solutions. Mohammed urges critical examination of how historical prejudices shape contemporary data interpretation and policy.

Takeaways

• 📊 Big data is not a new concept; its roots can be traced back to the early 20th century with the collection of demographic data.
• 👨‍🏫 The early use of demographic data was aimed at ranking racial groups based on their perceived fitness for participation in society.
• 📈 Historical biases in data collection continue to influence modern analyses, particularly regarding African Americans.
• 📰 Data collected about different racial groups is often selective, leading to skewed representations of crime and social issues.
• 🚫 There is a lack of statistical tracking for crimes committed by non-Black groups, which perpetuates systemic discrimination.
• 📉 Historical data on groups like the Irish and Italians was used to justify discrimination and social control.
• 📊 Modern data reveals significant achievement gaps among racial groups, yet the motivations behind data collection remain problematic.
• ⚖️ The interpretive frameworks we use to analyze data are shaped by both historical and contemporary political contexts.
• 🧠 Students and society must critically assess the biases inherent in the data collection process.
• 💡 Understanding the limitations of big data is essential for addressing social disparities and fostering equitable policies.

Q & A

What is Khalil Gibran Mohammed's professional background?

Khalil Gibran Mohammed is the Suzanne Young Murray Professor at the Radcliffe Institute for Advanced Study and a professor of history, race, and public policy at the Harvard Kennedy School.

How does Mohammed describe the historical use of big data?

Mohammed explains that the historical use of big data, particularly by demographers and social scientists, involved large sets of administrative data from sources like U.S. census reports and police agency records to measure societal health and welfare.

What was the original intent behind collecting demographic data about racial groups?

The original intent behind collecting demographic data was often to sort or rank various racial groups based on their perceived fitness for participation in America's industrial economy, particularly focusing on immigrants.

How does Mohammed illustrate the bias in crime statistics?

Mohammed points out that while crime statistics for groups like the Irish and Italians were once tracked, such scrutiny has largely shifted away from these groups to focus predominantly on African Americans, illustrating a bias in data collection.

What implications does Mohammed suggest arise from the data we collect today?

Mohammed suggests that the implications of current data collection practices are significant, as they often reinforce historical prejudices and justify discrimination rather than facilitating equity and support for marginalized groups.

Why does Mohammed emphasize the need for critical thinking about data?

He emphasizes critical thinking because the interpretive frames we use to analyze data are influenced by both historical contexts and contemporary political choices, which can lead to misrepresentation and misuse of information.

What example does Mohammed give regarding the tracking of crime statistics?

He gives the example that we can inquire about the crime rates of African Americans but not about the crime rates of specific groups like the Irish or Italians, highlighting selective data collection practices.

How does historical bias in data affect contemporary society according to Mohammed?

According to Mohammed, historical biases in data affect contemporary society by perpetuating stereotypes and reinforcing systemic inequalities, particularly in how we understand and address issues like mass incarceration.

What lesson does Mohammed convey to students about data collection?

He conveys that students should recognize that the information collected and the choices made in analysis are shaped by historical and political contexts, urging them to critically evaluate the data and its implications.

What does Mohammed mean by stating that data is not as objective as it seems?

He means that data collection and interpretation are influenced by biases and agendas, making it less about objective truth and more about the narratives and policies that the data supports.