1. Responsible data dimensions
Hi, I'm Maria Prokofieva, your instructor for this course on responsible data management.
2. Responsible data management
Today's landscape demands not only data modeling skills but also responsible artificial intelligence (AI) practices, emphasizing ethical data management throughout a project's lifecycle, from collection to implementation.
Traditionally, we evaluate AI models with technical metrics like accuracy score, mean squared error, and runtime for efficiency and optimization.
We'll be looking at responsible AI, which requires AI to consider lawfulness, fairness, diversity and inclusion, transparency and accountability, privacy and security.
3. In this course
We'll introduce these dimensions in this video, then look at responsible AI metrics and apply these concepts to the real world. Later on, we will take a deep dive into regulations and licensing, data governance, acquisition, and validation before wrapping up with bias mitigation strategies.
4. Lawfulness
The first dimension we'll introduce is lawfulness. It refers to AI systems complying with relevant laws and regulations to ensure that data is collected, processed, and used correctly. These regulations include but are not limited to data protection laws, human rights laws, and ethical regulations towards stakeholders, such as customers or data vendors. These regulations can differ depending on the governing body or country, so it's important always to confirm what applies to our projects.
5. Fairness
Next is fairness, meaning algorithms and data practices do not create or reinforce inequalities. AI systems must treat everyone equally, without discrimination based on protected characteristics like race, gender, age, or socioeconomic status.
6. Transparency and accountability
Transparency and accountability in data management refer to being clear about how the data is used, how the model is developed, and how decisions are made. It means that we need to be able to explain our AI systems and models to stakeholders so they can trust our AI technology and the results.
7. Diversity and inclusion
The diversity and inclusion dimension considers a wide range of demographic factors, including underrepresented groups in data, and embraces diverse perspectives and experiences. This approach is key to mitigating or reducing biases.
8. Privacy and security
The final dimension we will introduce is privacy and security. This refers to the safeguarding of personal and sensitive data in AI. Models often process personal information, such as email addresses, home addresses, account details, or even details on personal health. Individual data rights must be respected and protected, and businesses may also want to protect their data and models from unauthorized access, breaches, or malicious use.
9. Amazon AI hiring tool
Responsible data management requires a balanced approach between ethical considerations and technical performance. Amazon's attempt to automate its talent acquisition process between 2015 and 2017 illustrates the need for this balance. The company wanted to streamline its hiring process by using AI to rate job applicants. However, the project encountered issues and ultimately led to a public scandal, resulting in the abandoning of the initiative.
10. Challenges of AI models
So, what went wrong?
The system failed to evaluate candidates for technical roles in a gender-neutral manner.
The core issue was in the training data. The resumes submitted over a decade were predominantly from men, mirroring the gender imbalance in the tech industry. As a result, the AI inadvertently learned to favor male candidates.
This case highlights the challenges and limitations of using only technical metrics to evaluate AI models. An AI project needs to balance optimal technical performance while prioritizing fair and responsible AI practices. This case also shows the importance of high-quality datasets and gathering data responsibly. It reveals complexities, especially in sensitive areas, where biases can lead to far-reaching consequences, including legal and financial ramifications.
11. Let's practice!
This course aims to provide you with the necessary knowledge for achieving this balance. Let's start practicing!