Fear, fearlessness and environmental activism: The Lock the Gate Alliance

This event is the fourth part of a four part series: Governance and the power of fear.

While the political right may harness fear to resist change, fear (and fearlessness) may play very different roles for environmental activists. This panel explores the role of emotions in galvanising or inhibiting community action.

Misplaced and misused fear

This event is the third part of a four part series: Governance and the power of fear.

The relationship between fear and regulation is not well understood. Fear may be misplaced – think of our outsized fear of sharks, or it may be misused – as when fears of terrorism are used to justify curtailing civil liberties and invading privacy. This panel examines how fear has been misused and misplaced to justify either regulation or inaction.

Fear not, Papa is here

This event is the second part of a four part series: Governance and the power of fear.

Fear is a common currency of authoritarian political power and is being wielded by a growing cadre of international leaders, including Putin, Duterte, Erdoğan and Trump.

There is also the less confronting but just as insidious political sibling of paternalism, which cloaks the denial of political, economic and social agency by disadvantaged peoples in the respectability of ‘meaning well’.

Fear in a rapidly changing world

This event is the first part of a four part series: Governance and the power of fear.

Governance and the power of fear

We have made significant progress as a society toward teaching individuals the craft of managing fear productively. We have been less successful, however, in designing regulatory systems that recognize fear as both an enabler and disabler of behavioural and social change.

This series, taking place over four weeks in Nov-Dec, will bring together regulatory scholars and practitioners to explore the positive and negative manifestations of fear and reflect on the strengths and weaknesses of a range of regulatory and governance approaches.

Teaching machines to play fair

Machine learning is increasingly used to make decisions about people’s lives, such as whether to give someone a loan or whether to interview someone for a job. This brings with it the risk of discrimination, particularly if the data used for training the machines contains bias.

One strategy for ensuring such systems are fair is to modify the training data they learn from. Such approaches have been successful empirically, but typically lack strong theoretical fairness guarantees.


Updated:  10 August 2017/Responsible Officer:  Director, RegNet/Page Contact:  Director, RegNet