The influence of moral stories on kindergarteners’ sharing behaviour

Zhuojun Yao and Robert Enright
Early Child Development and Care
July 19, 2018

Abstract

The current study investigated the effect of moral stories in promoting kindergarteners’ sharing behaviour. One hundred eight children were randomly assigned to one of three conditions: two experimental conditions (a moral story with a sharing model and good consequences and a moral story with a selfish model and bad consequences) and a control condition (a nonmoral story). The results showed that children in the experimental groups shared more than children in the control group. In addition, comparing the two experimental groups, children in the sharing-good consequences condition shared more than children in the selfish-bad consequences condition. Further, interviews were conducted to provide in-depth understanding of common and different influences of the two moral stories on children’s sharing behaviour. The implications for research and practice were discussed.

The article is here.

Why is suicide on the rise in the US – but falling in most of Europe?

Steven Stack
The Conversation
Originally published June 28, 2018

Here is an excerpt:

There is evidence that rising suicide rates are associated with a weakening of the social norms regarding mutual aid and support.

In one study on suicide in the U.S., the rising rates were closely linked with reductions in social welfare spending between 1960 and 1995. Social welfare expenditures include Medicaid, a medical assistance program for low income persons; Temporary Assistance for Needy Families, which replaced Aid to Families with Dependent Children; the Supplemental Security Income program for the blind, disabled and elderly; children’s services including adoption, foster care and day care; shelters; and funding of public hospitals for medical assistance other than Medicaid.

Later studies found a similar relationship between suicide and social welfare for the U.S. in the 1980s and between 1990 and 2000, as well as for nations in the Organization for Economic Cooperation and Economic Development.

When it comes to spending on social welfare, the U.S. is at the low end of the spectrum relative to Western Europe. For example, only 18.8 percent of the U.S. GDP is spent on social welfare, while most of the OECD nations spend at least 25 percent of their GDP. Our rates of suicide are increasing while their rates fall.

The information is here.

Health Insurers Are Vacuuming Up Details About You — And It Could Raise Your Rates

Marshall Allen
ProPublica.org
Originally posted July 17, 2018

Here are two excerpts:

With little public scrutiny, the health insurance industry has joined forces with data brokers to vacuum up personal details about hundreds of millions of Americans, including, odds are, many readers of this story. The companies are tracking your race, education level, TV habits, marital status, net worth. They’re collecting what you post on social media, whether you’re behind on your bills, what you order online. Then they feed this information into complicated computer algorithms that spit out predictions about how much your health care could cost them.

(cut)

At a time when every week brings a new privacy scandal and worries abound about the misuse of personal information, patient advocates and privacy scholars say the insurance industry’s data gathering runs counter to its touted, and federally required, allegiance to patients’ medical privacy. The Health Insurance Portability and Accountability Act, or HIPAA, only protects medical information.

“We have a health privacy machine that’s in crisis,” said Frank Pasquale, a professor at the University of Maryland Carey School of Law who specializes in issues related to machine learning and algorithms. “We have a law that only covers one source of health information. They are rapidly developing another source.”

The information is here.

The Road to Pseudoscientific Thinking

Julia Shaw
Scientific American
Originally published January 16, 2017

Here is the conclusion:

So, where to from here? Are there any cool, futuristic, applications of such insights? According to McColeman “I expect that category learning work from human learning will help computer vision moving forward, as we understand the regularities in the environment that people are picking up on. There’s still a lot of room for improvement in getting computer systems to notice the same things that people notice.” We need to help people, and computers, to avoid being distracted by unimportant, attention-grabbing, information.

The take-home message from this line of research seems to be: When fighting the post-truth war against pseudoscience and misinformation, make sure that important information is eye-catching and quickly understandable.

The information is here.

Thousands of leading AI researchers sign pledge against killer robots

Ian Sample
The Guardian
Originally posted July 18, 2018

Here is an excerpt:

The military is one of the largest funders and adopters of AI technology. With advanced computer systems, robots can fly missions over hostile terrain, navigate on the ground, and patrol under seas. More sophisticated weapon systems are in the pipeline. On Monday, the defence secretary Gavin Williamson unveiled a £2bn plan for a new RAF fighter, the Tempest, which will be able to fly without a pilot.

UK ministers have stated that Britain is not developing lethal autonomous weapons systems and that its forces will always have oversight and control of the weapons it deploys. But the campaigners warn that rapid advances in AI and other fields mean it is now feasible to build sophisticated weapons that can identify, track and fire on human targets without consent from a human controller. For many researchers, giving machines the decision over who lives and dies crosses a moral line.

“We need to make it the international norm that autonomous weapons are not acceptable. A human must always be in the loop,” said Toby Walsh, a professor of AI at the University of New South Wales in Sydney who signed the pledge.

The info is here.

Google’s AI ethics won't curb war by algorithm

Phoebe Braithwaite
Wired.com
Originally published July 5, 2018

Here is an excerpt:

One of these programmes is Project Maven, which trains artificial intelligence systems to parse footage from surveillance drones in order to “extract objects from massive amounts of moving or still imagery,” writes Drew Cukor, chief of the Algorithmic Warfare Cross-Functional Team. The programme is a key element of the US army’s efforts to select targets. One of the companies working on Maven is Google. Engineers at Google have protested their company’s involvement; their peers at companies like Amazon and Microsoft have made similar complaints, calling on their employers not to support the development of the facial recognition tool Rekognition, for use by the military, police and immigration control. For technology companies, this raises a question: should they play a role in governments’ use of force?

The US government’s policy of using armed drones to hunt its enemies abroad has long been controversial. Gibson argues that the CIA and US military are using drones to strike “far from the hot battlefield, against communities that aren't involved in an armed conflict, based on intelligence that is quite frequently wrong”. Paul Scharre, director of the technology and national security programme at the Center for a New American Security and author of Army of None says that the use of drones and computing power is making the US military a much more effective and efficient force that kills far fewer civilians than in previous wars. “We actually need tech companies like Google helping the military to do many other things,” he says.

The article is here.

Why Should We Be Good?

Matt McManus
Quillette.com
Originally posted July 7, 2018

Here are two excerpts:

The negative motivation arises from moral dogmatism. There are those who wish to dogmatically assert their own values without worrying that they may not be as universal as one might suppose. For instance, this is often the case with religious fundamentalists who worry that secular society is increasingly unmoored from proper values and traditions. Ironically, the dark underside of this moral dogmatism is often a relativistic epistemology. Ethical dogmatists do not want to be confronted with the possibility that it is possible to challenge their values because they often cannot provide good reasons to back them up.

(cut)

These issues are all of considerable philosophical interest. In what follows, I want to press on just one issue that is often missed in debates between those who believe there are universal values, and those who believe that what is ethically correct is relative to either a culture or to the subjective preference of individuals. The issue I wish to explore is this: even if we know which values are universal, why should we feel compelled to adhere to them? Put more simply, even if we know what it is to be good, why should we bother to be good? This is one of the major questions addressed by what is often called meta-ethics.

The information is here.

False Equivalence: Are Liberals and Conservatives in the U.S. Equally “Biased”?

Jonathan Baron and John T. Jost
Invited Revision, Perspectives on Psychological Science.

Abstract

On the basis of a meta-analysis of 51 studies, Ditto, Liu, Clark, Wojcik, Chen, et al. (2018) conclude that ideological “bias” is equivalent on the left and right of U.S. politics. In this commentary, we contend that this conclusion does not follow from the review and that Ditto and colleagues are too quick to embrace a false equivalence between the liberal left and the conservative right. For one thing, the issues, procedures, and materials used in studies reviewed by Ditto and colleagues were selected for purposes other than the inspection of ideological asymmetries. Consequently, methodological choices made by researchers were systematically biased to avoid producing differences between liberals and conservatives. We also consider the broader implications of a normative analysis of judgment and decision-making and demonstrate that the “bias” examined by Ditto and colleagues is not, in fact, an irrational bias, and that it is incoherent to discuss bias in the absence of standards for assessing accuracy and consistency. We find that Jost’s (2017) conclusions about domain-general asymmetries in motivated social cognition, which suggest that epistemic virtues are more prevalent among liberals than conservatives, are closer to the truth of the matter when it comes to current American politics. Finally, we question the notion that the research literature in psychology is necessarily characterized by “liberal bias,” as several authors have claimed.

Here is the end:

 If academics are disproportionately liberal—in comparison with society at large—it just might
be due to the fact that being liberal in the early 21st century is more compatible with the epistemic standards, values, and practices of academia than is being conservative.

The article is here.

See Your Surgeon Is Probably a Republican, Your Psychiatrist Probably a Democrat as an other example.

How Do Expectations Shape Perception?

Floris P. de Lange, Micha Heilbron, & Peter Kok
Trends in Cognitive Sciences
Available online 29 June 2018

Abstract

Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.

Highlights

• Expectations play a strong role in determining the way we perceive the world.

• Prior expectations can originate from multiple sources of information, and correspondingly have different neural sources, depending on where in the brain the relevant prior knowledge is stored.

• Recent findings from both human neuroimaging and animal electrophysiology have revealed that prior expectations can modulate sensory processing at both early and late stages, and both before and after stimulus onset. The response modulation can take the form of either dampening the sensory representation or enhancing it via a process of sharpening.

• Theoretical computational frameworks of neural sensory processing aim to explain how the probabilistic integration of prior expectations and sensory inputs results in perception.

The research is here.