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Lecture 7: Data-driven decision making and predictions - Coggle Diagram
Lecture 7: Data-driven decision making and predictions
Analytics and expert collaboration: How individuals navigate relationships when working with organizational data (Barbour)
Multidisciplinary knowledge work:
Saring and interpretating of data
among a variety of organizational experts
May complicate existing expert relationships
Negotiation of those relationships involve:
Trust
: Integral to collaboration among org. experts
Connection
: Realtionships should enable opportuinties to engage in rich conversations
Acces
: Being able to reach each other and exchange info and ideas
Optioning
Reinterpretation and reconceptualization
Processes that are dependent on dialogue
Genertating multiple interpretations of data or problems
Interaction with experts across the organization
redefine and evolve problems
abiliy of individuals to engage in optioning was influenced by relationships with others
Increased size and complexity of data in orgenizations:
problems of collaboration among people in different units
Push leaders to change existing realtions and create new ones
Strategies to overcome challengs with accesing data: Optioning & project autonomy
Challenges in their work with data:
The volume
The complexity
Accessibility of data
Pace of their work with data
Dilemmas with
autonomy
:
Hierachies and data ownership
legal and reulatory frameworks
Exisiting agenda for data collection
Project autonomy
less relevant when requesting
It positively affects optioning
more relevant when collaborating
Acces
Asking existing contacts
Targetting the right person with the data
Existing relationships
Go beyond regulal requests for data, richer engagement with expert
Big data surveillance: the case of policing (Brayne)
Surveillance:
Traditional
Close observation
suspected person
Inductive
New
Deductive
low visibility
Categorically
Routine activity
....
Broad & deep surveillance
Why would police use data based decision makeing?
Prevent and reduce crime rates
potential as an
accountability mechanism:
response to discrimination practices
5 ways in which the adoption of big data anaytics is associated with shifts in practice
Automated alerts: surveil a large number of people
Info on people (who never been in contact with the police
Data used for predictive purposes
Data systems are merged
Assesment of risk
Features of data environment:
Fast
Disparate
Vast
Digital
Intended & unintended consequences
Social inequalities
Ameliorate persistent inequalities
Rely less on stereotypes
More complete info
Reproduction of inequality
Widening the criminal justice dragnet unequally
System avoidance = People avoiding "surveilling" institutions fundamental for social integration
Deepening surveillnce on suspected people
Implications for law