AI, Algorithms, and Awful Humans

A profound shift is occurring in the way many decisions are made, with machines taking greater roles in the decision-making process. Two arguments are often advanced to justify the increasing use of automation and algorithms in decisions. The “Awful Human Argument” asserts that human decision-making is often awful and that machines can decide better than humans. Another argument, the “Better Together Argument,” posits that machines can augment and improve human decision-making. These arguments exert a powerful influence on law and policy. In this Essay, we contend that in the context of making decisions about humans, these arguments are far too optimistic. We argue that machine and human decision-making are not readily compatible, making the integration of human and machine decision-making extremely complicated. It is wrong to view machines as deciding like humans do, except better because they are supposedly cleansed of bias. Machines decide fundamentally differently, and bias often persists. These differences are especially pronounced when decisions require a moral or value judgment or involve human lives and behavior. Making decisions about humans involves special emotional and moral considerations that algorithms are not yet prepared to make—and might never be able to make. Automated decisions often rely too much on quantifiable data to the exclusion of qualitative data, resulting in a change to the nature of the decision itself. Whereas certain matters might be readily reducible to quantifiable data, such as the weather, human lives are far more complex. Human and machine decision-making often do not mix well. Humans often perform badly when reviewing algorithmic output. We contend that algorithmic decision-making is being relied upon too eagerly and with insufficient skepticism. For decisions about humans, there are important considerations that must be better appreciated before these decisions are delegated in whole or in part to machines

The Prediction Society: Algorithms and the Problems of Forecasting the Future

Predictions about the future have been made since the earliest days of humankind, but today, we are living in a brave new world of prediction. Today’s predictions are produced by machine learning algorithms that analyze massive quantities of personal data. Increasingly, important decisions about people are being made based on these predictions. Algorithmic predictions are a type of inference. Many laws struggle to account for inferences, and even when they do, the laws lump all inferences together. But as we argue in this Article, predictions are different from other inferences. Predictions raise several unique problems that current law is ill-suited to address. First, algorithmic predictions create a fossilization problem because they reinforce patterns in past data and can further solidify bias and inequality from the past. Second, algorithmic predictions often raise an unfalsifiability problem. Predictions involve an assertion about future events. Until these events happen, predictions remain unverifiable, resulting in an inability for individuals to challenge them as false. Third, algorithmic predictions can involve a preemptive intervention problem, where decisions or interventions render it impossible to determine whether the predictions would have come true. Fourth, algorithmic predictions can lead to a self-fulfilling prophecy problem where they actively shape the future they aim to forecast. More broadly, the rise of algorithmic predictions raises an overarching concern: Algorithmic predictions not only forecast the future but also have the power to create and control it. The increasing pervasiveness of decisions based on algorithmic predictions is leading to a prediction society where individuals’ ability to author their own future is diminished while the organizations developing and using predictive systems are gaining greater power to shape the future. Privacy and data protection law do not adequately address algorithmic predictions. Many laws lack a temporal dimension and do not distinguish between predictions about the future and inferences about the past or present. Predictions about the future involve considerations that are not implicated by other types of inferences. Many laws provide correction rights and duties of accuracy that are insufficient to address problems arising from predictions, which exist in the twilight between truth and falsehood. Individual rights and anti-discrimination law also are unable to address the unique problems with algorithmic predictions. We argue that the use of algorithmic predictions is a distinct issue warranting different treatment from other types of inference. We examine the issues laws must consider when addressing the problems of algorithmic predictions

Nano-sized Mo- and Nb-doped TiO2 as anode materials for high energy and high power hybrid Li-ion capacitors

Nano-sized Mo-doped titania (Mo0.1Ti0.9O2) and Nb-doped titania (Nb0.25Ti0.75O2) were directly synthesized via a continuous hydrothermal flow synthesis process. Materials characterization was conducted using physical techniques such as transmission electron microscopy, powder x-ray diffraction, x-ray photoelectron spectroscopy, Brunauer–Emmett–Teller specific surface area measurements and energy dispersive x-ray spectroscopy. Hybrid Li-ion supercapacitors were made with either a Mo-doped or Nb-doped TiO2 negative electrode material and an activated carbon (AC) positive electrode. Cells were evaluated using electrochemical testing (cyclic voltammetry, constant charge discharge cycling). The hybrid Li-ion capacitors showed good energy densities at moderate power densities. When cycled in the potential window 0.5–3.0 V, the Mo0.1Ti0.9O2/AC hybrid supercapacitor showed the highest energy densities of 51 Wh kg−1 at a power of 180 W kg−1 with energy densities rapidly declining with increasing applied specific current. In comparison, the Nb0.25Ti0.75O2/AC hybrid supercapacitor maintained its energy density of 45 Wh kg−1 at 180 W kg−1 better, showing 36 Wh g−1 at 3200 W kg−1, which is a very promising mix of high energy and power densities. Reducing the voltage window to the range 1.0–3.0 V led to an increase in power density, with the Mo0.1Ti0.9O2/AC hybrid supercapacitor giving energy densities of 12 Wh kg−1 and 2.5 Wh kg−1 at power densities of 6700 W kg−1 and 14 000 W kg−1, respectively

Atmospheric chemistry of cyclohexanone: UV spectrum and kinetics of reaction with chlorine atoms

Absolute and relative rate techniques were used to study the reactivity of Cl atoms with cyclohexanone in 6 Torr of argon or 800–950 Torr of N 2 at 295 ± 2 K. The absolute rate experiments gave k (Cl + cyclohexanone) = (1.88 ± 0.38) × 10 −10 , whereas the relative rate experiments gave k (Cl + cyclohexanone) = (1.66 ± 0.26) × 10 −10 cm 3 molecule −1 s −1 . Cyclohexanone has a broad UV absorption band with a maximum cross section of (4.0 ± 0.3) × 10 −20 cm 2 molecule −1 near 285 nm. The results are discussed with respect to the literature data. © 2008 Wiley Periodicals, Inc. Int J Chem Kinet 40: 223–229, 2008Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58072/1/20291_ftp.pd

TiO2/MoO2 nanocomposite as anode materials for high power Li-ion batteries with exceptional capacity

Nanoparticles of molybdenum(IV) oxide (MoO 2 ) and a TiO 2 /MoO 2 nanocomposite were synthesised via a continuous hydrothermal synthesis process. Both powders were analysed using XRD, XPS, TEM, and BET and evaluated as active materials in anodes for Li-ion half-cells. Cyclic voltammetry and galvanostatic charge/discharge measurements were carried out in the potential window of 0.1 to 3.0 V vs. Li/Li+. Specific capacities of ca. 350 mAh g -1 were obtained for both materials at low specific currents (0.1 A g -1 ); TiO 2 /MoO 2 composite electrodes showed superior rate behaviour & stability under cycling (compared to MoO 2 ), with stable specific capacities of ca. 265 mAh g -1 at a specific current of 0.5 A g -1 and ca. 150 mAh g -1 after 350 cycles at a specific current of 2.5 A g -1 . The improved performance of the composite material, compared to MoO 2 , was attributed to a smaller particle size, improved stability to volume changes (during cycling), and lower charge transfer resistance during cycling. Li-ion hybrid electrochemical capacitors using TiO 2 /MoO 2 composite anodes and activated carbon (AC) cathodes were evaluated and showed excellent performance with an energy density of 44 Wh kg -1 at a power density of 600 W kg -1

Two cases of variceal haemorrhage during living-donor liver transplantation

Some patients with cirrhosis experience rupture of venous varices before operation, and liver transplantation is a therapy of last resort for these patients. However, we have experienced two cases of intraoperative rupture in whom no abnormalities of the venous varices were seen on endoscopy before operation. One patient with ruptured gastrointestinal varices was treated by direct surgical ligation and the other with ruptured oesophageal gastric varices, spontaneously recovered with a Sengstaken–Blakemore tube. These cases suggest that acute variceal haemorrhage should always be considered as a possibility during living-donor liver transplantation in patients with a history of upper gastrointestinal bleeding. Careful observation of the nasogastic tube is important during clamping of the hepatic portal vein

Sur la p-dimension des corps

Let A be an excellent integral henselian local noetherian ring, k its residue field of characteristic p>0 and K its fraction field. Using an algebraization technique introduced by the first named author, and the one-dimension case already proved by Kazuya KATO, we prove the following formula: cd_p(K) = dim(A) + p-rank(k), if k is separably closed and K of characteristic zero. A similar statement is valid without those assumptions on k and K