Guest post from Jo Kaybryn, an international development consultant currently directing evaluation frameworks, evaluation quality assurance services, and leading evaluations for UN agencies and INGOs.
“Upping the Ex Ante” is a series of articles aimed at evaluators in international development exploring how our work is affected by – and affects – digital data and technology. I’ve been having lots of exciting conversations with people from all corners of the universe about our brave new world. But I’ve also been conscious that for those who have not engaged a lot with the rapid changes in technologies around us, it can be a bit daunting to know where to start. These articles explore a range of technologies and innovations against the backdrop of international development and the particular context of evaluation. For readers not yet well versed in technology there are lots of sources to do further research on areas of interest.
The series is half way through, with 4 articles published.
So far in Part 1 the series has gone back to the olden days (1948!) to consider the origin story of cybernetics and the influences that are present right now in algorithms and big data. The philosophical and ethical dilemmas are a recurring theme in later articles.
Part 2 examines the problems of distance which is something that technology offers huge strides forwards in, and yet it remains never fully solved, with a discussion on what blockchains mean for the veracity of data.
Part 3 considers qualitative data and shines a light on the gulf between our digital data-centric and analogue-centric worlds and the need for data scientists and social scientists to cooperate to make sense of it.
Part 4 looks at quantitative data and the implications for better decision making, why evaluators really don’t like an algorithmic “black box”; and reflections on how humans’ assumptions and biases leak into our technologies whether digital or analogue.
The next few articles will see a focus on ethics, psychology and bias; a case study on a hypothetical machine learning intervention to identify children at risk of maltreatment (lots more risk and ethical considerations), and some thoughts about putting it all in perspective (i.e. Don’t Panic!).