An algorithm is set of steps that perform calculations, process data, or automate tasks. Algorithms are everywhere we look (and even places we don’t look) controlling what we see, do, and where we go. They’re great for solving our problems and helping us make better and quicker decisions, or taking the decision-making out of our hands. Their guidance is perfect in their objective and unbiased calculation. Except they are not, actually. Like everything else, they are created by people, and people have biases that get encoded into the algorithms they create. Algorithms learn from data, which is also created by people, so the algorithms also learn biases from data. This can be a problem when algorithms encode these biases into their calculations and go on to perpetuate the bias.
In this talk you will hear why we should care about algorithmic accountability, and details on a case study on how computational journalism can be used to investigate algorithms and advocate the need for transparency and accountability.