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.

Algorithmic discrimination: are you IN or OUT?

chosenoneA lot has been said about algorithms working as gatekeepers and making decisions on our behalf, often without us noticing it. I can surely find an example in my daily life where I do notice it and benefit from it. This happens when I use the “Discover Weekly” Spotify play-list. By comparing my listening habits to that of other users with similar but not identical choices, Spotify allows information on the fringes to be shared. It is thus “tailored” to my music taste, and it is incredibly accurate in predicting things I would like. Besides, it lets me discover new music and bands and in many occasions can also take me back in time with some tunes I have probably not listened to for a long time.

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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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Emancipating Users Against Algorithmic Biases for a Trusted Digital Economy