Data Scientist — Ethics & Data Privacy

Understanding Compliance and its Limitations - Description: This day is dedicated to a deep understanding of major data privacy regulations. You'll move beyond a superficial understanding of GDPR and CCPA, examining the nuances of data subject rights, consent mechanisms, data breach notification, and international data transfers. This includes analyzing the legal definitions, exemptions, and enforcement mechanisms of these and other related laws (e.g., CPRA, LGPD). You'll then discuss the limitations of these regulations. - Resources/Activities: - Expected Outcomes: Comprehensive knowledge of GDPR, CCPA, and similar regulations. Ability to assess the compliance requirements of a data science project, and identify practical issues of enforcement and limitations of regulations.

Beyond Simple Metrics - Description: This day dives deep into algorithmic bias. You'll learn about advanced techniques for detecting and mitigating bias in machine learning models, moving beyond simple demographic parity and equal opportunity. This includes exploring techniques like adversarial debiasing, fairness-aware pre-processing, and post-processing methods with theoretical grounding. The practical application of bias mitigation in different domains will be a core theme. - Resources/Activities: - Expected Outcomes: Proficiency in identifying and mitigating various types of algorithmic bias using state-of-the-art techniques. Ability to select and implement appropriate fairness metrics for evaluating the effectiveness of bias mitigation strategies.

Implementing Privacy at Scale - Description: This day is about understanding and implementing differential privacy (DP) and other PETs. You'll learn the theoretical underpinnings of DP, explore its various implementations (e.g., in databases, machine learning), and understand its strengths and limitations. You'll also learn about other PETs like secure multi-party computation (SMPC) and homomorphic encryption, and their applicability in data science. - Resources/Activities: - Expected Outcomes: Deep understanding of DP and other PETs. Ability to implement DP techniques in practice. Knowledge of the tradeoffs between privacy, utility, and computational cost.

Practical Implementation - Description: This day focuses on how to translate ethical principles and legal requirements into concrete data science practices. You'll learn about data governance frameworks, ethics review boards, and the creation of ethical data science pipelines. You will also examine case studies of organizations implementing these best practices. This includes examining data governance policies, training programs, and processes for ethical review of data science projects. - Resources/Activities: - Expected Outcomes: Understanding of the practical aspects of building ethical data science pipelines, and the ability to design and implement data governance frameworks. Ability to create documentation and risk assessment.

Addressing the Root Causes - Description: This day moves beyond algorithmic bias and looks at the earlier stages of data science: collection and annotation. You'll examine the sources of bias in data collection (e.g., selection bias, participation bias, reporting bias) and annotation (e.g., annotator bias, inter-annotator disagreement). You’ll explore strategies for mitigating these biases, including careful data source selection, diverse annotation teams, and techniques for measuring and addressing annotator disagreement. - Resources/Activities: - Expected Outcomes: Ability to identify and mitigate biases in data collection and annotation. Understanding of how biases in the data impact model outcomes. Ability to apply best practices for building representative datasets.

Preparing for the Future - Description: This final day explores the ethical implications of emerging technologies and future trends in data science. This includes discussing the ethics of AI in healthcare, autonomous vehicles, and facial recognition. The focus is on anticipating future ethical challenges, and the development of foresight and adaptability. - Resources/Activities: - Expected Outcomes: Understanding of the ethical implications of emerging technologies and the ability to anticipate future ethical challenges. Knowledge of resources for keeping abreast of developments in data science ethics. Development of foresight and adaptability in approach to data ethics.