All posts by Ansgar Koene

Publication of 1st WP4 workshop report

We are please to announce that the report summarizing the outcomes of the first UnBias project stakeholder engagement workshop is now available for public dissemination.

The workshop took place on February 3rd 2017 at the Digital Catapult centre in London, UK. It brought together participants from academia, education, NGOs and enterprises to discuss fairness in relation to algorithmic practice and design. At the heart of the discussion were four case studies highlighting fake news, personalisation, gaming the system, and transparency.

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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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2016, an eventful year for algorithms

For algorithm based systems, as with many other topics, 2016 turned out to be an eventful year. As we close the year and look back on events, the course of 2016 brought many of the issues we intend to address in the UnBias project to the attention of people and organizations who previously perhaps had not considered these things before.

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Algorithms Transparency and Accountability in the Digital Economy event at the European Parliament

page-shot-2016-11-10-event-07-11-algorithmic-accountability-and-transparencyOn November 7th I attended the Algorithms Transparency and Accountability in the Digital Economy roundtable event that was organized by MEP Marietje Schaake for the purpose of “discussing which options the European Union has to improve the accountability and/or the transparency of the algorithms that underpin many business models and platforms in the digital single market.”

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Invitation to participate in stakeholder engagement workshops

unbias-logo2We invite stakeholders from academia, education, government/regulatory oversight organizations, civil society, media, industry and entrepreneurs to  contribute to our ongoing research study by taking part in a small number of stakeholder engagement workshops. These workshops will explore the implications of algorithm-mediated interactions on online platforms. They provide an opportunity for relevant stakeholders to put forward their perspectives and discuss the ways in which algorithms shape online behaviours, in particular in relation to access and the dissemination of information to users. The workshops will provide an excellent opportunity for participants to exchange ideas and explore solutions with perspectives from a wide range of stakeholders. Following each workshop the participants will receive an anonymized report of the outcomes, which will contribute to the production of policy recommendations as well as the design of a ‘fairness toolkit’ for users, online providers and other stakeholders.

Further information and details about the workshops is available at the WP4 Invitation for stakeholder engagement page.

News, algorithms bias and editorial responsibility

unbias_conversationIn an almost suspiciously conspiracy-like fashion the official launch of UnBias at the start of September was immediately accompanied by a series of news articles providing examples of problems with algorithms that are making recommendations or controlling the flow of information. Cases like the unintentional racial bias in a machine learning based beauty contest algorithm, meant to remove bias of human judges; a series of embarrassing news recommendations on the Facebook trending topics feed, as a results of an attempt to avoid (appearance of) bias by getting rid of human editors; and controversy about Facebook’s automated editorial decision to remove the Pulitzer prize-winning “napalm girl”  photograph because the image was identifies as containing nudity. My view of these events? “Facebook’s algorithms give it more editorial responsibility – not less (published today in the Conversation).

 

Introducing: UnBias

Businesswoman idea concept on blackboard

In an age of ubiquitous data collecting, analysis and processing, how can citizens judge the trustworthiness and fairness of systems that heavily rely on algorithms? News feeds, search engine results and product recommendations increasingly use personalization algorithms to help us cut through the mountains of available information and find those bits that are most relevant, but how can we know if the information we get really is the best match for our interests?

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