Data Ethics, AI and Responsible Innovation

Ewa Luger, Michael Rovatsos, Burkhard Schafer, Morgan Currie, Robin Williams, Claudia Pagliari, Sarah Chan, James Stewart, Lachlan Urquhart, Simon Fokt, Shannon Vallor, EdinburghX

Our future is here and it relies on data. Predictive policing, medical robots, smart homes and cities, artificial intelligences - we can all think about how any of those could go wrong. Discover how we can build a future where they are done right.

How much would you like your smart home to know about you? Has your data been harvested and used for political advertising on social media? Would you be happy to be profiled by a predictive policing AI?

As we create more data-driven technologies, those issues become increasingly urgent. We must begin to ask not only ‘what can we do?’, but also ‘what should we do?’ How should we design new technologies to make sure they are used for good, not bad purposes?

The ‘good’, the ‘bad’, and the ‘should’ are a domain of ethics, and a basis for other important concepts such as justice, fairness, rights, respect. They further inform the law and what is legal. Finally, they are at the roots of an extremely important currency in the modern economy: trust.

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 the 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.

We will engage with data-based contexts such as facial recognition, predictive policing, medical screening, smart homes and cities, banking, and AI, to explore their social implications and the tools required to minimise harm, promote fairness, and safeguard and increase human autonomy and well-being. We address cutting edge issues being grappled with by practitioners and new approaches emerging in industry and offer the opportunity for participants to develop and feedback solutions.

Completing this course will help you understand the challenges we are facing, and inspire you to design, criticise, and develop better intelligent systems to shape our future.

What will you learn

  • Understand and articulate the critical, social, legal, political and ethical issues arising throughout the data lifecycle.
  • Understand relevant concepts, including: ethics/morality, responsibility, digital rights, data governance, human-data interaction, responsible research and innovation.
  • Identify and assess current ethical issues in data science and industry.
  • Apply professional critical judgement and reflexivity to moral problems with no clear solutions.
  • Evaluate ethical issues you face in your current professional practice.
  • Identify and apply ethically driven solutions to those issues.

Dates:
  • 15 June 2020
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
  • Audio:
  • Email-course:
  • Language: English Gb

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