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Bill Fulkerson

Burning embers: towards more transparent and robust climate-change risk assessments - 0 views

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    In this Review, we outline the history and evolution of the burning embers and associated reasons for concern framework, focusing on the methodological approaches and advances. While the assessment framework and figure design have been broadly retained over time, refinements in methodology have occurred, including the consideration of different risks, use of confidence statements, more formalized protocols and standardized metrics. Comparison across reports reveals that the risk level at a given temperature has generally increased with each assessment cycle, reflecting accumulating scientific evidence. For future assessments, an explicit, transparent and systematic process of expert elicitation is needed to enhance comparability, quality and credibility of burning embers.
Bill Fulkerson

We Will Fight Diseases of Our Networks By Realizing We Are Networks - 0 views

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    The systems we use to encode expertise tend to depend on relatively slow processes of collective computation and don't typically work as measures of an individual's actual extra-institutional networks or their ability to respond to novelty. Organizations and metrics tuned to slow and stable periods that favor efficiency and specialization tend to suppress generalists and improvisation.
Bill Fulkerson

COVID-19 Testing Dashboard for IU Bloomington - 0 views

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    It is important to note that no single number or metric is robust enough to adequately describe the trajectory of the pandemic.
Bill Fulkerson

Datafication and ideological blindness - Cennydd Bowles - 0 views

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    ""Our bodies break / And the blood just spills and spills / And here we sit debating math." -Retribution Gospel Choir, Breaker Design got its seat at the table, which is good because we can shut up about it now. What used to be seen as the territory of bespectacled Scandinavians is now a matter of HBR covers, consumer clamour, and 12-figure market caps. People in suits now talk about design as a way to differentiate products and unlock new markets."
Steve Bosserman

She Is a Gold Digger: Women Strike It Big in East Africa - 0 views

  • Tanzania alone sits on an estimated 2,222 metric tons of gold and boasts the third-highest reserves of the metal in Africa. But while the failure of these reserves to translate into wealth for ordinary people has led to populist moves – Tanzania’s President John Magufuli has demanded foreign mining firms pay higher taxes if they want to continue exporting — the problem may lie, in part, elsewhere. While women account for about 40 to 50 percent of Africa’s 8 million artisanal miners, their average income is significantly lower than that of their male counterparts, according to the African Center for Economic Transformation.
  • That has a spillover effect on communities. An established body of economic research, including by organizations like the Organization for Economic Cooperation and Development (OECD), has shown that economic empowerment of women translates into greater benefits for their families and communities than similar levels of earnings for men. That’s a phenomenon that groups working with gold miners in East Africa are witnessing also.
Steve Bosserman

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

  • 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.
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