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Algorithms and the persuasion machine

In the current BBC series Secrets of Silicon Valley Jamie Bartlett (technology writer and Director of the Centre for Social Media Analysis at Demos) explores the ‘dark reality behind Silicon Valley’s glittering promise to build a better world.’ Episode 2, The Persuasion Machine, shines a spotlight on several of the issues we are investigating in UnBias.

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A Month of Conferences and Workshops

June was a month of conferences and workshops for UnBias. The 3rd UnBias project meeting on June 1st, hosted by our Edinburgh partners this time, was quickly followed by the Ethicomp and EuroDIG conferences which both took place from June 5th to 8th.

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UnBias project contribution to the 4th Winchester Conference on Trust, Risk, Information and the Law

The 4th Winchester Conference on Trust, Risk, Information and the Law took place at the University of Winchester on Wednesday 3rd May 2017. The overarching theme of the day was “Artificial and De-Personalised Decision-Making: Machine-Learning, A.I. and Drones”: offering a chance for multi-stakeholder and interdisciplinary discussion on the risks and opportunities presented by algorithms, machine learning and artificial intelligence.

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IEEE Standard for Algorithm Bias Considerations

As part of our stakeholder engagement work towards the development of algorithm design and regulation recommendations UnBias is engaging with the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems to develop an IEEE Standard for Algorithm Bias Considerations, designated P7003. The P7003 working group is chaired by Ansgar Koene and will have its first web-meeting on May 5th 2017.

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How hard is to be fair in multi-user combinatorial scenarios?

Many multi-user scenarios are characterised by a combinatorial nature, i.e., an algorithm can take meaningful decisions for the users only if all their requirements and preferences are considered at the same time to select a solution from a huge potential space of possible system decisions. Sharing economy application, where users aim to find peers to form teams with in order to accomplish a task, and situations in which a limited number of potentially different resources, e.g. hotel rooms,  must be distributed to users who have preferences over  them are examples of such scenarios.

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Algorithm Workshop, University of Strathclyde. February 2017

What are algorithms and how are they designed? Why are they used in commercial practice and what kinds of benefits can they bring? What are the potential harmful impacts of using algorithms and how can they be prevented?

On Wednesday 15th February 2017 some UnBias consortium members had the pleasure of attending an Algorithm Workshop hosted by the Law School, University of Strathclyde. During the workshop, we had the opportunity to consider, discuss and begin to address key issues and concerns surrounding the contemporary prevalence of algorithms. The workshop was also attended by students from the host University and an interdisciplinary group of experts from areas including Law, Computer Science and the Social Sciences. This mix of expertise made for a really great afternoon of talks and discussions surrounding the design, development and use of algorithms through various disciplinary perspectives.

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Fair machine learning techniques and the problem of transparency

An important topic considered this year at the International Conference on Neural Information Processing Systems (NIPS), one of the prime outlets for machine learning and Artificial Intelligence research in the world, is the connection between machine learning, law and ethics. In particular, a paper presented by Moritz Hardt, Eric Price, and Nathan Srebro focused on Equality of Opportunity in Supervised Learning.

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