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Research and Data Analysis
Critical Data Studies and Societal Impact of Data Training Course
Introduction
In today’s increasingly datafied world, the intersection of data, society, and ethics is more critical than ever. Critical Data Studies and Societal Impact of Data Training Course provides an in-depth exploration of how data shapes social, political, cultural, and economic systems. This course empowers participants with the analytical tools to interrogate data-driven technologies, data governance frameworks, and algorithmic decision-making processes that influence everyday life. Using real-world case studies and interactive learning methods, this course offers an interdisciplinary approach to understanding the implications of big data, surveillance, predictive analytics, and AI through a critical lens.
Participants will explore pressing topics such as algorithmic bias, surveillance capitalism, data privacy, digital inequality, and the ethical use of data in public policy and business. Grounded in theory and applied practice, the course prepares learners to recognize data injustices and advocate for equitable data practices across industries. Whether you're a policymaker, researcher, developer, or activist, this training will enhance your capacity to navigate and challenge the complex societal consequences of data-centric systems.
Programme Curriculum
Critical Data Studies and Societal Impact of Data Training Course
Introduction
In today’s increasingly datafied world, the intersection of data, society, and ethics is more critical than ever. Critical Data Studies and Societal Impact of Data Training Course provides an in-depth exploration of how data shapes social, political, cultural, and economic systems. This course empowers participants with the analytical tools to interrogate data-driven technologies, data governance frameworks, and algorithmic decision-making processes that influence everyday life. Using real-world case studies and interactive learning methods, this course offers an interdisciplinary approach to understanding the implications of big data, surveillance, predictive analytics, and AI through a critical lens.
Participants will explore pressing topics such as algorithmic bias, surveillance capitalism, data privacy, digital inequality, and the ethical use of data in public policy and business. Grounded in theory and applied practice, the course prepares learners to recognize data injustices and advocate for equitable data practices across industries. Whether you're a policymaker, researcher, developer, or activist, this training will enhance your capacity to navigate and challenge the complex societal consequences of data-centric systems.
Course Objectives
Define critical data studies and its role in the digital age
Identify the ethical and societal implications of data collection and use
Analyze algorithmic bias and discriminatory data practices
Evaluate the impact of big data and AI on marginalized communities
Understand data governance, open data, and digital rights
Assess the role of surveillance and data capitalism in modern society
Explore the relationship between data infrastructures and power
Examine case studies of real-world data misuse and public backlash
Apply data justice frameworks to real-world scenarios
Interpret policies on data privacy, consent, and ethical usage
Foster inclusive, equitable, and transparent data practices
Develop critical thinking on tech solutionism and digital ethics
Design actionable strategies for responsible data innovation
Target Audience
Data scientists and AI developers
Government policymakers and regulators
Academics and critical theorists
NGO and civil society leaders
Journalists and digital media professionals
Tech company executives and product managers
Students in data science, sociology, or law
Human rights and data justice advocates
Course Duration: 5 days
Course Modules
Module 1: Introduction to Critical Data Studies
Defining data and its sociotechnical context
History and evolution of data-driven systems
Interdisciplinary foundations of critical data studies
Key thinkers and concepts in data critique
Data as power: sociopolitical lenses
Case Study: Facebook–Cambridge Analytica scandal
Module 2: Data Ethics and Algorithmic Bias
Definitions and types of algorithmic bias
Ethical dilemmas in AI and machine learning
Discriminatory datasets and outcomes
Auditing algorithms for fairness
AI transparency and explainability
Case Study: Amazon’s biased recruitment algorithm
Module 3: Surveillance, Privacy & Consent
The rise of surveillance capitalism
The politics of data consent
Biometrics, tracking, and behavioral data
Legal frameworks: GDPR, CCPA
Corporate surveillance and user exploitation
Case Study: China’s Social Credit System
Module 4: Data and Inequality
Digital divide and socio-economic data gaps
Racialized and gendered data harms
Data colonialism and exploitation in the Global South
Representation and inclusion in datasets
Data bias in health, housing, and education
Case Study: Racial bias in healthcare algorithms
Module 5: Data Governance and Policy
Principles of open data and transparency
National and international data laws
Public vs. private sector responsibilities
Data ownership and intellectual property
Citizen data rights and advocacy
Case Study: India’s Aadhaar ID system
Module 6: AI, Automation, and Societal Disruption
AI’s impact on labor and employment
Automation and digital exclusion
Ethical challenges in predictive policing
Future of work and algorithmic control
Debates on technological determinism
Case Study: Predictive policing tools and community backlash
Module 7: Resistance, Activism, and Data Justice
Data justice movements and frameworks
Grassroots activism for data transparency
Community-based data projects
Data storytelling and counter-mapping
Ethical hacking and whistleblowing
Case Study: Black Lives Matter and police data transparency
Module 8: Designing Ethical Data Futures
Human-centered data design principles
Co-creating ethical tech tools
Building inclusive digital infrastructures
Civic tech and participatory design
Futures thinking and scenario planning
Case Study: Responsible AI in urban planning
Training Methodology
Interactive lectures with expert facilitators
Hands-on workshops on real-life ethical dilemmas
Group discussions for knowledge exchange and peer learning
Case study analysis for critical reflection and application
Capstone project to design a data justice intervention
Feedback sessions for continuous improvement and personalization
Register as a group from 3 participants for a Discount
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to FINESKILL TRAINING CENTER account, as indicated in the invoice so as to enable us prepare better for you.