1. Unraveling the data dilemma
Hi, and welcome to this course on conquering data bias! My name is Konstantinos, and I'll be your instructor.
2. Data driving decision-making
We live in an era where data drives our decisions. This data-driven revolution has transformed industries across the globe. From optimizing supply chains to personalizing customer experiences, data has become the driving force behind strategic business choices.
This widespread adoption of data and AI has extended its reach into critical areas like social justice, employment screening, and interactive interfaces such as virtual assistants.
3. The emergence of data bias
However, alongside these advancements, concerns have emerged regarding the presence of biases within these solutions. Reports have surfaced highlighting gender, race, and other forms of bias in data, analysis, and algorithms, raising questions about fairness and ethical implications.
For instance, Amazon shut down its model that scored suitable candidates for employment after it realized that it favored male candidates over female candidates. These instances highlight the complexities and challenges associated with navigating data bias.
4. What is data bias?
So, what exactly is data bias? Data bias is an error that arises when data or information is limited in some way, painting an inaccurate or unfair picture of the population. This inaccuracy or unfairness in the data can result from various factors such as systemic inequalities which include data imbalances, underrepresentation, and historical prejudices, or from cognitive tendencies.
Cognitive tendencies are essentially patterns of thinking or mental processes that influence human perception, judgment, and decision-making.
Overall, data bias compromises the accuracy and reliability of data, hindering its effectiveness in informing decision-making processes.
5. The Amazon case
Considering the Amazon example we mentioned earlier, the model was trained on resumes submitted over a 10-year period, during which male candidates were disproportionately favored by the hiring process.
Consequently, the model learned to associate certain characteristics or keywords more frequently found in resumes from male candidates with suitability for employment. This led to a bias favoring male candidates and it directly impacted the company’s reputation.
Addressing data bias requires scrutiny of both the dataset quality and the human processes involved, emphasizing transparency, ethical considerations, and the need for fair and unbiased practices.
6. About the course
In this course, we will dive deep into identifying and mitigating data bias in all the stages of data lifecycle. First, through the rest of chapter one, we will learn about the impact of data bias and why it is important to consider it when working with data. We will also review at a high level the specific types of data bias.
In chapter two, we will deep dive into the data bias in data collection. We will go through the various types of data bias and explore how to identify biases considering various use cases. More importantly, we will discover how to implement techniques to minimize bias, enhancing the fairness and accuracy of the data results
Lastly, in chapter three, we will cover the data bias types commonly present during data analysis and when sharing analysis results. By using a similar approach to chapter two, we will dive into how to identify bias and how to mitigate it.
7. Let's practice!
I'm excited to embark on this learning journey with you. There's much to explore and conquer, so let's dive in and tackle data bias head-on!