The illusion that might cheat us: ethical data science vision and practice

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Anais Nin, wrote in her 1946 diary of the dangers she saw in the growth of technology to expand our potential for connectivity through machines, but diminish our genuine connectedness as people. She could hardly have been more contemporary for today:

“This is the illusion that might cheat us of being in touch deeply with the one breathing next to us. The dangerous time when mechanical voices, radios, telephone, take the place of human intimacies, and the concept of being in touch with millions brings a greater and greater poverty in intimacy and human vision.”
[Extract from volume IV 1944-1947]

Echoes from over 70 years ago, can be heard in the more recent comments of entrepreneur Elon Musk. Both are concerned with simulation, a lack of connection between the perceived, and reality, and the jeopardy this presents for humanity. But both also have a dream. A dream based on the positive potential society has.

How will we use our potential?

Data is the connection we all have between us as humans and what machines and their masters know about us. The values that masters underpin their machine design with, will determine the effect the machines and knowledge they deliver, have on society.

In seeking ever greater personalisation, a wider dragnet of data is putting together ever more detailed pieces of information about an individual person. At the same time data science is becoming ever more impersonal in how we treat people as individuals. We risk losing sight of how we respect and treat the very people whom the work should benefit.

Nin grasped the risk that a wider reach, can mean more superficial depth. Facebook might be a model today for the large circle of friends you might gather, but how few you trust with confidences, with personal knowledge about your own personal life, and the privilege it is when someone chooses to entrust that knowledge to you. Machine data mining increasingly tries to get an understanding of depth, and may also add new layers of meaning through profiling, comparing our characteristics with others in risk stratification.
Data science, research using data, is often talked about as if it is something separate from using information from individual people. Yet it is all about exploiting those confidences.

Today as the reach has grown in what is possible for a few people in institutions to gather about most people in the public, whether in scientific research, or in surveillance of different kinds, we hear experts repeatedly talk of the risk of losing the valuable part, the knowledge, the insights that benefit us as society if we can act upon them.

We might know more, but do we know any better? To use a well known quote from her contemporary, T S Eliot, ‘Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?’

What can humans achieve? We don’t yet know our own limits. What don’t we yet know?  We have future priorities we aren’t yet aware of.

To be able to explore the best of what Nin saw as ‘human vision’ and Musk sees in technology, the benefits we have from our connectivity; our collaboration, shared learning; need to be driven with an element of humility, accepting values that shape  boundaries of what we should do, while constantly evolving with what we could do.

The essence of this applied risk is that technology could harm you, more than it helps you. How do we avoid this and develop instead the best of what human vision makes possible? Can we also exceed our own expectations of today, to advance in moral progress?

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