OUR FUTURE INTERNET: FROM BIAS TO TRUST
DIGITAL CATAPULT: OCTOBER 1ST 10.30 AM TO 5.00 PM
On October 1st the UnBias project team will be showcasing the outcomes of our work. We are looking forward to welcoming an audience of 70 stakeholders from research, law, policy, education and industry.
In addition to reporting on our major findings we will also highlight key outputs such as policy guidelines and demonstrate our exciting fairness toolkit. This engaging and interactive event will also include presentations from external speakers and opportunities for networking. Furthermore, we will announce plans for our follow-on project, ReEnTrust, which will identify mechanisms to rebuild and enhance trust in algorithmic systems.
Continue reading Looking forward to the UnBias Showcase! October 1st 2018, London
On the week-end of June 30th and July 1st, the UnBias team hosted a two-day hackathon at Codebase in Edinburgh, with support from local outfit Product Forge, whose experience organizing such events is unrivalled in Scotland.
The hackathon challenge was formulated as follows:
“Artificial Intelligence shapes digital services that have become central to our everyday lives. Online platforms leverage the power of AI to monetize our attention, with often unethical side-effects: our privacy is routinely breached, our perception of the world is seriously distorted, and we are left with unhealthy addictions to our screens and devices. The deep asymmetry of power between users and service providers, the opacity and unaccountability of the algorithms driving these services, and their exploitation by trolls, bullies and propagandists are serious threats to our well-being in the digital era.
Continue reading Unbias Hackathon
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.
Continue reading How hard is to be fair in multi-user combinatorial scenarios?