Oxford Martin School series — “Data work: the hidden talent and secret logic fuelling artificial intelligence” — Professor Gina Neff

Gina Neff with several handy trios of insight

Recording available here.

3x priority topics for companies implementing AI systems

• transparency: is the system transparent intra-enterprise & inter-enterprise
• integration
• reliance

3x primary typs of work that are necessary to make AI systems function (currently less visible & recognised than they need to be)

• intelligibility & transparency
• ongoing optimisation of resources
• the work of context & information meta-data

3x research-enabled policy recommendations

• who does the work of making AI systems viable in their data sources
• peoples understanding of their own privcay & data; we need organising and stakeholder engagement to help citizens understand & interrogate
• upskilling people and policy-makers in this field; we can’t leave it simply to large tech companies with commercial interest

3x ways to engage to learn how we are already being evolved by AI

• map
• track
• measure

Questions

What does equity of opportunity in AI look like and how do we know when we’ve got there?

• equity of opportunity is necessary but it’s not sufficient, for better tech
• the global south is unleveraged and if we expect it to simply adopt tools & data-sets supplied by global North then we’re in for a tricky future

And on “technological determinism”

• we hear it in how industry leaders push the inevitability of AI
• the pathway that new technologies is not pre-determined at all
• we as educators have our work cut out for us in terms of holding to account the way in which tech leaders talk about their achievements, applications, capabilities