{"id":408,"date":"2018-12-02T23:41:22","date_gmt":"2018-12-02T23:41:22","guid":{"rendered":"http:\/\/sigai.acm.org\/aimatters\/blog\/?p=408"},"modified":"2018-12-02T23:53:35","modified_gmt":"2018-12-02T23:53:35","slug":"follow-the-data","status":"publish","type":"post","link":"https:\/\/sigai.acm.org\/aimatters\/blog\/2018\/12\/02\/follow-the-data\/","title":{"rendered":"Follow the Data"},"content":{"rendered":"

The Ethical Machine<\/a> — Big Ideas for Designing Fairer AI and Algorithms \u2013 is a \u201cproject that presents ideas to encourage a discussion about designing fairer algorithms\u201d of the Shorenstein Center on Media, Politics, and Public Policy, Harvard Kennedy School. The November 27, 2018, publication is \u201cFollow the Data! Algorithmic Transparency Starts with Data Transparency<\/a>\u201d by Julia Stoyanovich and Bill Howe. Their focus is local and municipal governments and NGOs that deliver vital human services in health, housing, and mobility. In the article, they give a welcome emphasis on the role of data instead of the common focus these days on just algorithms. They write, \u201cdata is used to customize generic algorithms for specific situations\u2014that is to say that algorithms are trained<\/em> using data. The same algorithm may exhibit radically different behavior\u2014make different predictions; make a different number of mistakes and even different kinds of mistakes\u2014when trained on two different data sets. In other words, without access to the training data, it is impossible to know how an algorithm would actually behave.\u201d See their article for more discussion on designing systems for data transparency.<\/strong><\/p>\n

US and European Policy
\n<\/strong>Adam Eisgrau, ACM Director of Global Policy and Public Affairs, published an update on the <\/em>ACM US and Europe Policy Committees in the November 29\u00a0ACM MemberNet<\/a>.\u00a0<\/strong>Key points are
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