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Steve Bosserman

Are You Creditworthy? The Algorithm Will Decide. - 0 views

undark.org/...redit-scoring-machine-learning

algorithms discrimination bias credit scoring

shared by Steve Bosserman on 12 May 18 - No Cached
  • The decisions made by algorithmic credit scoring applications are not only said to be more accurate in predicting risk than traditional scoring methods; its champions argue they are also fairer because the algorithm is unswayed by the racial, gender, and socioeconomic biases that have skewed access to credit in the past.
  • Algorithmic credit scores might seem futuristic, but these practices do have roots in credit scoring practices of yore. Early credit agencies, for example, hired human reporters to dig into their customers’ credit histories. The reports were largely compiled from local gossip and colored by the speculations of the predominantly white, male middle class reporters. Remarks about race and class, asides about housekeeping, and speculations about sexual orientation all abounded.
  • By 1935, whole neighborhoods in the U.S. were classified according to their credit characteristics. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status.
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  • These systems are fast becoming the norm. The Chinese Government is now close to launching its own algorithmic “Social Credit System” for its 1.4 billion citizens, a metric that uses online data to rate trustworthiness. As these systems become pervasive, and scores come to stand for individual worth, determining access to finance, services, and basic freedoms, the stakes of one bad decision are that much higher. This is to say nothing of the legitimacy of using such algorithmic proxies in the first place. While it might seem obvious to call for greater transparency in these systems, with machine learning and massive datasets it’s extremely difficult to locate bias. Even if we could peer inside the black box, we probably wouldn’t find a clause in the code instructing the system to discriminate against the poor, or people of color, or even people who play too many video games. More important than understanding how these scores get calculated is giving users meaningful opportunities to dispute and contest adverse decisions that are made about them by the algorithm.
Steve Bosserman

Falling through the gaps: insecure work and the social safety net | openDemocracy - 0 views

www.opendemocracy.net/...ure-work-and-social-safety-net

work value labor market

shared by Steve Bosserman on 10 May 17 - No Cached
  • There is scope, in better delivery of work support, to challenge the atomisation and isolation of workers and the loss of social capital and networks of new working models. The precarity of insecure work needs to be addressed, rather than exacerbated, by the systems set up to support people through their working lives. In roles where working hours are flexible or unpredictable, the division between private and public lives can be complex. The interaction between the individual and the state needs to understand that complexity and support people to navigate through their working lives rather than leaving them without a compass or adequate map.
  • Steve Bosserman on 10 May 17
     
    Rationale for a time-based economy...
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