Resisting maximalist data policies

Intro round: starting points into the discussion

• data minimization for litigation, for journalism, for CSO or movement practice
• needs more thoughtful design and implementation - you need to know what data you need
• idea that smaller, closed-off online spaces that minimize data actually serve user needs better than global, open spaces
• how can we build personalized experiences for learning and education in accordance with data minimization
• which role does data minimization play in the context of governments that want to deliver tailored services?

Case against data Minimization: Data based, evidence based policy decisions don't even happen, but we still collect massive amounts of data for that reason. Claim: More data - more knowledge - wiser decisions Claim: Data are facts, not of varying quality / a result of interpretation. Claim: We need more data to not lose the race in innovation and economy

Case against data maximiization: Data collection is often a result of lack of trust in public institutions and services. Lack of trust leads to bad data leads to worse data based decisions

• Data of symptoms, not causes – data don't necessarily explain causality
• correlation isn't causation
• justified / ethical data collection requires better design and oversight
• educate about data footprint; make the invisible data flows visible
• burden of data collection = operation costs
• there is no total solution for security
• Data collection as a useless activity within a system that is unable to make decisions; might lead to inaction / overly detailed but unneccessary analyses.
• Collecting and analysing data feels like at least we're doing something

Case for Data Minimization

• more data = more risk
• more data = more cost but no one talks about it
• TMI - too much information, data becomes overwhelming
• interpretation of data is more important than volume
• Data minimization is freedom from data based profiling
• Empower users to share their story instead of collecting data

Need to differentiate:

• What kind of data? is qualitative data / qualitative feedback more meaningful?
• what does really happen in practice?
• who decides to what end data gets used, and to what end?
• given inequitable social outcomes, what data "do we need" to understand and rectify the inequity
• types of data:

Big tech has more data about societies than national governments