Edin - Data Ethics, AI and Responsible Innovation
- Offered byCoursera
Data Ethics, AI and Responsible Innovation at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Ethics, AI and Responsible Innovation at Coursera Highlights
- Earn a certificate from Coursera
- Add to your LinkedIn profile
- 5 assignments
Data Ethics, AI and Responsible Innovation at Coursera Course details
- Our future is here and it relies on data. Medical robots, smart homes and cities, predictive policing, artificial intelligences – all are fuelled by data and all promise new benefits to society
- But will these innovations benefit everyone? Who stands to gain and who is put at risk? How can we ensure that data is part of a just and sustainable world?
- This story-driven course is taught by the leading experts in data science, AI, information law, science and technology studies, and responsible research and innovation, and informed by case studies supplied by digital business frontrunners and tech companies
- We will look at real-world controversies and ethical challenges to introduce and critically discuss the social, political, legal and ethical issues surrounding data-driven innovation, including those posed by big data, AI systems, and machine learning systems
- We will drill down into case studies, structured around core concerns being raised by society, governments and industry, such as bias, fairness, rights, data re-use, data protection and data privacy, discrimination, transparency and accountability
- Throughout the course, we will emphasise the importance of being mindful of the realities and complexities of making ethical decisions in a landscape of competing interests
Data Ethics, AI and Responsible Innovation at Coursera Curriculum
Week 1: Law and Ethics
The challenge of data-driven innovation
What are ethics?
Making moral decisions
Ethical considerations
Jury manipulation
Common ethical issues
Interview 1: Oliver Smith, Telefónica
Interview 2: Roland Verhaaf
Welcome
Course team
Tell us about you!
Your learning community
Key information
Ambiguous ethical issues
Introduction to data-driven innovation
Values
Case study: Project Maven
Case study: Cambridge Analytica
Rules and consequences
The trolley problem
Introduction to making moral decisions
Moral decisions applied to data ethics
Data and the law
Ethics and the law
Ethics guidelines
Data science and law
Data ethics and the law: a summary
Further reading
Introduction to ethical considerations
Voices from industry
Week 1 summary
Test your knowledge
Help and support
Your experience of ethical 'issues'
Make your own thought experiment
Ethics in the time of pandemic
Week 2: Crime and Justice
Predictive policing
Statistical fairness
Group fairness measures
Individual fairness
Welcome to Week 2
It's not fair... or is it?
How does predictive policing work?
Predictive mapping
Individual risk assessment
Algorithmic sentencing
COMPAS and ProPublica
Defining 'bias'
Data mining and discrimination
Statistical bias in data mining
What are the problems with bias in the model itself?
2. Sampling bias in data collection
3. Labelling
What are the problems with labelling?
4. Proxy
Finding biased algorithms
Can algorithms be de-biased?
Three types of algorithmic fairness
Introduction to fairness
Introduction to data justice
What is data justice?
Redressing the power balance
The digital 'welfare' state
Watchdogs
Week 2 summary
Test your knowledge
What have you heard about predictive policing?
Automating poverty
1. Bias in the model itself
How does Facebook target ads?
What are the limitations of debiasing techniques?
Week 3: Home and City
A conversation on IoT
Welcome to Week 3
Smart homes
Social concerns in smart homes
Governance
How smart is a 'smart' city?
Pros and cons of realtime traffic routing
Social and governance concerns in smart cities
Finding solutions
Designing for values
Week 3 summary
Test your knowledge
AI in smart cities - what do you think?
Your smart home
Share examples of data privacy issues
Cities fighting pandemic
Smart tech: who's responsible?
Making improvements
Week 4: Money and Markets
The algorithmic economy
Utilitarian ethics
Distributive fairness
Social welfare and equity
Maximin and Pareto efficiency
Advanced fairness criteria
Welcome to week 4
What the experts say
Introduction
The fairness toolkit
Algorithms in society
Understanding value
Eliciting preferences in the housing scenario
Example house allocation algorithm
Housing scenario: revisited
Week 4 summary
Test your knowledge
A future worth building?
Value sensitive design examples
Design a housing allocation algorithm
Fairness beyond money: design a COVID-19 app
Week 5: Life and Health
Ethical benefits and risks of medical data
Neonatal genetic databanks
Robots and empathy
The datafication of work
Course summary
Welcome to Week 5
Curing the pandemic with data
Rank the use cases
Data vs humans
More is not better
Genetic testing
Consent and privacy
Chat with a chatbot
Incidental data
Who controls your data?
Health, work and the future
Week 5 summary
Podcast: Responsible Innovation
Thank you
Test your knowledge
Virtual assistants
Your experience of genetic testing
Gene database debate
Introducing medical robots
Could medical bots exhibit bias?
NHS Alexa: revisited
Your health data
Write your own manifesto
Data Ethics, AI and Responsible Innovation at Coursera Admission Process
Important Dates
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Student Forum
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