How are music artists adjusting to algorithmic recommendation systems?

I have written a few times, here, on the blog, about algorithmic recommendation systems. Sometimes, I focus on the technology itself. Other times, I focus on the recipients of those recommendations. Occasionally, I have looked at the impact on advertisers. However, I don’t often consider the perspective of the producer - i.e., the person / company / brand … Continue reading How are music artists adjusting to algorithmic recommendation systems?

Marketing short-stories – a collection and podcast series

Early in 2020, Finola Kerrigan and Stephen Brown guest-edited a special issue of the Marketing Theory journal composed of short-stories about marketing scholarship. The call for papers had invited marketing scholars to “take their research and turn it into (1) a compelling work of short fiction, (2) a work that is more captivating than an orthodox … Continue reading Marketing short-stories – a collection and podcast series

The handful of datasets that rule our lives

There are numerous examples of how the datasets that are used to train the algorithms that rule our daily lives are biased. For instance, tools that automatically translate professional titles tends to follow gender stereotypes: males are doctors while nurses are females. There is also bias against faces of females and faces of people of colour. But if these biases are … Continue reading The handful of datasets that rule our lives

The automation of sexism and racism

Four years ago, while preparing for a presentation, I searched google for a generic image of a “person” to add to my slides. Of the first 25 results, one (4%) had long hair. Three (12%) images were of people with dark skin (1 woman and 2 men; all with short or no hair). And, overall, there … Continue reading The automation of sexism and racism

Understanding and solving opacity in algorithms

One of the key challenges presented by algorithms is its opacity – that is, the inability to see how the algorithm produced a specific output. For instance, the ability to see how a search engine algorithm ranks content; how credit rating algorithm ranks the characteristic of potential borrowers; or, how a self-driving algorithm ranks external … Continue reading Understanding and solving opacity in algorithms

The A-level algorithm debacle shows us that algorithms + poor data = myths with a veneer of legitimacy

UK students due to sit exams this Spring (for instance, A-level exams, for entrance into University), saw their examinations cancelled, as a result of the Covid-19 pandemic and the need to avoid group gatherings. Instead, as explained in the gov.uk website: “For each student, schools and colleges have provided a ‘centre assessment grade’ for each … Continue reading The A-level algorithm debacle shows us that algorithms + poor data = myths with a veneer of legitimacy

[Miscellany] Gender bias‬‬‬; lack of imagination in algorithms’ design; wanted Professor of Foresight

Possible gender discrimination in Apple Card Did you read about that story, that went viral on Twitter, about Apple credit cards offering a much higher credit limit to men than women, even when the latter have demonstrably the same or even better financial situations? [If not, read this or this] The person who posted … Continue reading [Miscellany] Gender bias‬‬‬; lack of imagination in algorithms’ design; wanted Professor of Foresight

Resource on the sociology of artificial intelligence and algorithmic decision making

In my process of learning about artificial intelligence, and reflecting on its implications for society (and marketing, as part of it), I came across the work of Zeynep Tufekci. She works on the sociology of technology, for instance the social consequences of algorithmic manipulation, or how people in power use artificial intelligence to manipulate us: … Continue reading Resource on the sociology of artificial intelligence and algorithmic decision making

TED talk: blind faith in big data must end

Super interesting and short talk by Cathy O'Neil about What is an algorithm Why they are subjective, flawed and unfair How they can have disastrous effects in people’s lives How they perpetuate the past and historical discrimination Why they are so difficult to scrutinise And, thus, why we can not have blind faith in big … Continue reading TED talk: blind faith in big data must end