Algorithms, ontology, and social progress

Abstract

Recently, media and communication researchers have shown an increasing interest in critical data studies and ways to utilize data for social progress. In this commentary, I highlight several useful contributions in the International Panel on Social Progress (IPSP) report toward identifying key data justice issues, before suggesting extra focus on algorithmic discrimination and implicit bias. Following my assessment of the IPSP’s report, I emphasize the importance of two emerging media and communication areas – applied ontology and semantic technology – that impact internet users daily, yet receive limited attention from critical data researchers. I illustrate two examples to show how applied ontologies and semantic technologies impact social processes by engaging in the hierarchization of social relations and entities, a practice that will become more common as the Internet changes states towards a ‘smarter’ version of itself.

Improved Direct Measurement of the 64.5 keV Resonance Strength in theO17(p,α)Bruno1, Scott2, Aliotta3 et al. 2016Phys. Rev. Lett.610450View full textAdd to dashboardCite

“…A new direct determination of the ER = 64.5 keV resonance strength performed at the Laboratory for Underground Nuclear Astrophysics accelerator has led to the most accurate value to date, ωγ = 10.0 ± 1.4stat ± 0.7syst neV, thanks to a significant background reduction underground and generally improved experimental conditions. The (bare) proton partial width of the corresponding state at Ex = 5672 keV in 18 F is Γp = 35 ± 5stat ± 3syst neV. This width is about a factor of 2 higher than previously estimated thus leading to a factor of 2 increase in the 17 O(p,α) 14 N reaction rate at astrophysical temperatures relevant to shell hydrogen-burning in red giant and asymptotic giant branch stars.…”

“…The 17 O(p,α) 14 N reaction plays a key role in various astrophysical scenarios, from asymptotic giant branch stars to classical novae. It affects the synthesis of rare isotopes such as 17 O and 18 F, which can provide constraints on astrophysical models. A new direct determination of the ER = 64.5 keV resonance strength performed at the Laboratory for Underground Nuclear Astrophysics accelerator has led to the most accurate value to date, ωγ = 10.0 ± 1.4stat ± 0.7syst neV, thanks to a significant background reduction underground and generally improved experimental conditions.…”

“…A subsequent study [15], under infinitely thick-target conditions, reported a value of Γ p = 22±3 neV for the proton partial arXiv:1610.00483v1 [nucl-ex] 3 Oct 2016 width, from which a strength ωγ = 5.5 +1.8 −1.5 neV (value adopted in NACRE compilation [16]) was inferred. Later re-analyses [17,18], however, led to slightly different values, including Γ p = 19 ± 2 neV [18], as adopted in the STARLIB compilation [19]. Indirect investigations by Sergi et al [12,20] based on the Trojan Horse Method (THM) arrived at a bare (i.e., free from electron screening effects) weighted average ωγ = 3.42 ± 0.60 neV.…”

Algorithmic accountability and digital justice: A critical assessment of technical and sociotechnical approaches

“…Algorithms can be used to enhance or hinder social progress and digital justice. The social life of algorithms is shaped in part by a pervasive technological frame that casts them as objective and neutral, meaning that their outputs are free from bias (Binns, , p. 546; Iliadis, , p. 3). This frame is supported by stakeholders with vested interests in preserving the status of algorithmic assemblages as neutral because it benefits them economically, legitimates them socially, and preserves power asymmetries.…”

“…What is the autonomy of algorithms, if any? ‐ it is the accountability and the responsibility of algorithms as socio‐technical artifacts that is examined, that of their creators and users, and ultimately, of the balance of power facilitated or caused by algorithms.At the center of this approach is the insight that there are a number of human influences embedded into algorithms, such as criteria choices, training data, semantics, and interpretation (Diakopoulos, , p. 9; Haarkens, , p. 25; Iliadis, , p. 3). Any investigation must therefore consider algorithms as objects of human creation and take into account intent, including that of any group or institutional processes that may have influenced their design.…”