We are pleased to announce that UnBias won one of the three 2017 RCUK Digital Economy Theme ‘Telling Tales of Engagement’ awards. The evaluation process for this award considered both the impact of our previous work and a proposed new activity to “tell the story” of our research.
Our submission was titled “building and engaging with multi-stakeholder panels for developing policy recommendations”, highlighting the importance to our research of engaging with our stakeholder panel and with organizations that are shaping the policy and governance space for algorithmic systems.
On 16th April the House of Select Committee on Artificial Intelligence published a report called ‘AI in the UK: ready, willing and able?”. The report is based on an inquiry by the House of Lords conducted to consider the economic, ethical and social implications of advances in artificial intelligence. UnBias team member Ansgar Koene submitted written evidence based on the combined work of the UnBias investigations and our involvement with the development of the IEEE P7003 Standard for Algorithmic Bias Considerations.
In the spirit of recent events surrounding the revelations about Cambridge Analytica and the breaches of trust regarding Facebook and personal data, ISOC UK and the Horizon Digital Economy Research institute held a panel discussion on “Multi Sided Trust for Multi Sided Platforms“. The panel brought together representatives from different sectors to discuss the topic of trust on the Internet, focusing on consumer to business trust; how users trust online services that are offered to them. Such services include, but are not limited to, online shopping, social media, online banking and search engines.
On March 5th and 6th UnBias had the pleasure of participating in a workshop that was organized to signal the launch of the European Commission’s Joint Research Center’s HUMAINT (HUman behaviour and MAchine INTelligence ) project.
The HUMAINT project is a multidisciplinary research project that aims to understand the potential impact of machine intelligence on human behaviour. A particular focus of the project lies on human cognitive capabilities and decision making. The project recognizes that machine intelligence may provide cognitive help to people, but that algorithms can also affect personal decision making and raise privacy issues.
From 21st to 22nd February the Royal Society and the Royal Netherlands Academy of Arts and Sciences (KNAW) held a UK – Netherlands bilateral international meeting to explore common research interests in the fields of Quantum Physics and Technology, Nanochemistry and Responsible Data Science. UnBias was pleased to participate as part of the Responsible Data Science stream.
On September 14th the US ACM organized a panel on Algorithmic Transparency and Accountability in Washington DC to discuss the importance of the Statement on Algorithmic Transparency and Accountability and opportunities for cooperation between academia, government and industry around these principles. Also part of this panel was Ansgar, representing the IEEE Global Initiative on Ethical Considerations for Artificial Intelligence and Autonomous Systems, its P7000 series of Standards activities, and UnBias.
On September 7th the Guardian published an article drawing attention to a study from Stanford University which had applied Deep Neural Networks (a form of machine learning AI) to test if they could distinguish peoples’ sexual orientation from facial images. After reading both the original study and the Guardian’s report about it, there were so many problematic aspects about the study that I immediately had to write a response, which was published in the Conversation on September 13th under the title “Machine gaydar: AI is reinforcing stereotypes that liberal societies are trying to get rid of“.
Prior to the June 8th snap election there were two Commons Select Committee inquiries that both touched directly on our work at UnBias and for which we submitted written evidence. One on “Algorithms in decision-making” and another on “Fake News”.
As part of our work to contribute to the development of the IEEE P7003 Standard for Algorithm Bias Considerations we are reaching out to the community of stakeholders to ask for use cases highlighting real-world instances of unjustified and/or inappropriate bias in algorithmic decisions.
