Module 3: Data and Bias

Welcome to the module "Data and Bias." We will explore the crucial interconnection between data and bias, shedding light on how the information we collect can inadvertently introduce biases into various processes. As data increasingly shapes decision-making in the realms of artificial intelligence and technology, it becomes imperative to understand the nuances of bias within datasets. Join us as we unravel the complexities of this interplay, examining real-world examples and strategies to mitigate biases, ensuring a more accurate and equitable use of data in diverse applications.

In Module 3, we cover the following Lessons:

Lesson 3.1: Bias in Data Collection

Lesson 3.2: Data Sampling Methods

Lesson 3.3: Ethical Data Sourcing

Lesson 3.4: Data Pre-processing and Bias Reduction

Lesson 3.5: Real-world Data Bias Case Studies


Our final lesson, Lesson 3.5, brings us to Real-world Data Bias Case Studies. In this lesson, we'll examine concrete examples of data biases affecting AI applications in various domains. By delving into these case studies, we gain valuable insights into the real challenges faced and solutions implemented to address bias in diverse scenarios. Join us as we analyze and learn from real-world experiences to better understand the complexities of mitigating bias in AI systems. Several real-world data bias case studies provide valuable insights into the impact of biases in AI applications. These examples highlight the importance of addressing bias to ensure fair and equitable outcomes.

Facial Recognition Bias
Case Study: Gender and Racial Bias in Facial Recognition Systems
Overview: Facial recognition systems have been found to exhibit gender and racial bias, with higher error rates for certain demographic groups, particularly women and people with darker skin tones. This bias can lead to inaccurate and unfair outcomes, especially in surveillance and law enforcement applications.

Credit Scoring Disparities
Case Study: Biases in Credit Scoring Algorithms
Overview: Credit scoring algorithms have faced scrutiny for exhibiting biases that disproportionately impact certain groups. Studies have shown that these algorithms may result in lower credit scores for individuals from marginalized communities, affecting their access to financial opportunities.

Criminal Justice Bias

Case Study: Predictive Policing and Racial Bias
Overview: Predictive policing algorithms have been criticized for perpetuating racial bias in law enforcement. These systems, when trained on biased historical crime data, may lead to over-policing in specific communities, reinforcing existing disparities in the criminal justice system.

Healthcare Disparities
Case Study: Bias in Healthcare Algorithms
Overview: Healthcare algorithms, such as those used for predicting patient outcomes or treatment recommendations, can reflect biases in historical healthcare data. This bias may result in unequal healthcare outcomes, with certain demographic groups receiving suboptimal care.

Recruitment Algorithms
Case Study: Gender Bias in Hiring Algorithms
Overview: Algorithms used in recruitment processes have been found to exhibit gender bias, favoring male candidates over equally or more qualified female candidates. This bias reflects and perpetuates gender disparities in the workforce.

These case studies offer tangible examples of how biases can manifest in AI systems and underscore the importance of addressing such biases to build fair and inclusive technology.

Good job! You have completed Module 3. Now let's move on to Quiz 3 to complete this section. Good luck!