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

[Miscellany] Three interesting podcast episodes

I want to share with you three interesting podcasts that I came across recently. Daniel Kahneman on noisy decision making, and the need for algorithms Sandra Peter interviews psychologist / behavioural economist and Noble prize winner, Daniel Kahneman, for the Sydney Business Insights podcast. He discusses his famous book, Thinking Fast and Slow. But, most … Continue reading [Miscellany] Three interesting podcast episodes

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

Algorithms are not all powerful, autonomous entities

Facebook contractors working on content moderation are, reportedly, being forced back into the office because Facebook’s attempt to use Artificial Intelligence for this difficult task failed. The open letter from those Facebook contractors states that: “Facebook tried using ‘AI’ to moderate content—and failed. At the start of the pandemic, both full-time Facebook staff and content … Continue reading Algorithms are not all powerful, autonomous entities

Podcast recommendations: Why we stockpile(d) toilet paper; Why tracing COVID-19 with an app is tricky; and Why automated recommendations technology is struggling

Today, I would like to share with you three podcast episodes. While discussing issues related to COVID-19, they actually offer great insight about consumer psychology, and about the limitations of technology. Why we stockpile(d) toilet paper In episode 34 the “It's all just a bunch of BS” podcast, Caroline Roux discusses decision making in … Continue reading Podcast recommendations: Why we stockpile(d) toilet paper; Why tracing COVID-19 with an app is tricky; and Why automated recommendations technology is struggling

AI is a system, not a technology

I have been reading a lot of about artificial intelligence (AI), lately, and reflecting on its implications for how organisations interact with its customers. I have noticed that, often, AI is equated with the algorithm that underpins it (which is, often, assumed to be a machine learning algorithm). That makes sense, because algorithms are fundamental … Continue reading AI is a system, not a technology

Types of machine learning – a crib sheet for marketers

According to a market study by advisory firm Dresner, reported in Forbes, marketing managers are more likely than any other organisational function to see machine learning as critically important for the achievement of their goals. As shown in the graph below, 40% think that machine learning is critically important, and nearly two-thirds (65%) think that it is … Continue reading Types of machine learning – a crib sheet for marketers

Biases in algorithms – the case of Hello Barbie

Sometime ago, I saw a presentation by Val Steeves, Professor of Criminology at the University of Ottawa (Canada), about her research on smart toys. The talk focused on Hello Barbie, a Barbie-branded doll which is advertised as “the first fashion doll that can have a two-way conversation with girls”, and featuring “speech recognition and progressive … Continue reading Biases in algorithms – the case of Hello Barbie

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

August and September 2016 round-up

August was taken up with conferences and time off with the family, so I decided to skip the usual monthly round up post and merge it with September’s. And, then, September flashed through, as well, with back to school matters, and dealing with various cold viruses and man-flu in the house. Here are the highlights. … Continue reading August and September 2016 round-up