No Child Left Alone: Moral Judgments about Parents Affect Estimates of Risk to Children

Thomas, A. J., Stanford, P. K., & Sarnecka, B. W. (2016).
Collabra, 2(1), 10.

Abstract

In recent decades, Americans have adopted a parenting norm in which every child is expected to be under constant direct adult supervision. Parents who violate this norm by allowing their children to be alone, even for short periods of time, often face harsh criticism and even legal action. This is true despite the fact that children are much more likely to be hurt, for example, in car accidents. Why then do bystanders call 911 when they see children playing in parks, but not when they see children riding in cars? Here, we present results from six studies indicating that moral judgments play a role: The less morally acceptable a parent’s reason for leaving a child alone, the more danger people think the child is in. This suggests that people’s estimates of danger to unsupervised children are affected by an intuition that parents who leave their children alone have done something morally wrong.

Here is part of the discussion:

The most important conclusion we draw from this set of experiments is the following: People don’t only think that leaving children alone is dangerous and therefore immoral. They also think it is immoral and therefore dangerous. That is, people overestimate the actual danger to children who are left alone by their parents, in order to better support or justify their moral condemnation of parents who do so.

This brings us back to our opening question: How can we explain the recent hysteria about unsupervised children, often wildly out of proportion to the actual risks posed by the situation? Our findings suggest that once a moralized norm of ‘No child left alone’ was generated, people began to feel morally outraged by parents who violated that norm. The need (or opportunity) to better support or justify this outrage then elevated people’s estimates of the actual dangers faced by children. These elevated risk estimates, in turn, may have led to even stronger moral condemnation of parents and so on, in a self-reinforcing feedback loop.

The article is here.

Official sends memo to agency leaders about ethical conduct

Avery Anapol
The Hill
Originally published October 10, 2017

The head of the Office of Government Ethics is calling on the leaders of government agencies to promote an “ethical culture.”

David Apol, acting director of the ethics office, sent a memo to agency heads titled, “The Role of Agency Leaders in Promoting an Ethical Culture.” The letter was sent to more than 100 agency heads, CNN reported.

“It is essential to the success of our republic that citizens can trust that your decisions and the decisions made by your agency are motivated by the public good and not by personal interests,” the memo reads.

Several government officials are under investigation for their use of chartered planes for government business.

One Cabinet official, former Health secretary Tom Price, resigned over his use of private jets. Treasury Secretary Steven Mnuchin is also under scrutiny for his travels.

“I am deeply concerned that the actions of some in Government leadership have harmed perceptions about the importance of ethics and what conduct is, and is not, permissible,” Apol wrote.

The memo includes seven suggested actions that Apol says leaders should take to strengthen the ethical culture in their agencies. The suggestions include putting ethics officials in senior leadership meetings, and “modeling a ‘Should I do it?’ mentality versus a ‘Can I do it?’ mentality.”

The article is here.

Who Sees What as Fair? Mapping Individual Differences in Valuation of Reciprocity, Charity,and Impartiality

Laura Niemi and Liane Young
Social Justice Research

When scarce resources are allocated, different criteria may be considered: impersonal allocation (impartiality), the needs of specific individuals (charity), or the relational ties between individuals (reciprocity). In the present research, we investigated how people’s perspectives on fairness relate to individual differences in interpersonal orientations. Participants evaluated the fairness of allocations based on (a) impartiality (b) charity, and (c) reciprocity. To assess interpersonal orientations, we administered measures of dispositional empathy (i.e., empathic concern and perspective-taking) and Machiavellianism. Across two studies, Machiavellianism correlated with higher ratings of reciprocity as fair, whereas empathic concern and perspective taking correlated with higher ratings of charity as fair. We discuss these findings in relation to recent neuroscientific research on empathy, fairness, and moral evaluations of resource allocations.

The article is here.

Moral Distress: A Call to Action

The Editor
AMA Journal of Ethics. June 2017, Volume 19, Number 6: 533-536.

During medical school, I was exposed for the first time to ethical considerations that stemmed from my new role in the direct provision of patient care. Ethical obligations were now both personal and professional, and I had to navigate conflicts between my own values and those of patients, their families, and other members of the health care team. However, I felt paralyzed by factors such as my relative lack of medical experience, low position in the hospital hierarchy, and concerns about evaluation. I experienced a profound and new feeling of futility and exhaustion, one that my peers also often described.

I have since realized that this experience was likely “moral distress,” a phenomenon originally described by Andrew Jameton in 1984. For this issue, the following definition, adapted from Jameton, will be used: moral distress occurs when a clinician makes a moral judgment about a case in which he or she is involved and an external constraint makes it difficult or impossible to act on that judgment, resulting in “painful feelings and/or psychological disequilibrium”. Moral distress has subsequently been shown to be associated with burnout, which includes poor coping mechanisms such as moral disengagement, blunting, denial, and interpersonal conflict.

Moral distress as originally conceived by Jameton pertained to nurses and has been extensively studied in the nursing literature. However, until a few years ago, the literature has been silent on the moral distress of medical students and physicians.

The article is here.

Automation on our own terms

Benedict Dellot and Fabian Wallace-Stephens
Medium.com
Originally published September 17, 2017

Here is an excerpt:

There are three main risks of embracing AI and robotics unreservedly:

1. A rise in economic inequality — To the extent that technology deskills jobs, it will put downward pressure on earnings. If jobs are removed altogether as a result of automation, the result will be greater returns for those who make and deploy the technology, as well as the elite workers left behind in firms. The median OECD country has already seen a decrease in its labour share of income of about 5 percentage points since the early 1990s, with capital’s share swallowing the difference. Another risk here is market concentration. If large firms continue to adopt AI and robotics at a faster rate than small firms, they will gain enormous efficiency advantages and as a result could take excessive share of markets. Automation could lead to oligopolistic markets, where a handful of firms dominate at the expense of others.
2. A deepening of geographic disparities — Since the computer revolution of the 1980s, cities that specialise in cognitive work have gained a comparative advantage in job creation. In 2014, 5.5 percent of all UK workers operated in new job types that emerged after 1990, but the figure for workers in London was almost double that at 9.8 percent. The ability of cities to attract skilled workers, as well as the diverse nature of their economies, makes them better placed than rural areas to grasp the opportunities of AI and robotics. The most vulnerable locations will be those that are heavily reliant on a single automatable industry, such as parts of the North East that have a large stock of call centre jobs.
3. An entrenchment of demographic biases — If left untamed, automation could disadvantage some demographic groups. Recall our case study analysis of the retail sector, which suggested that AI and robotics might lead to fewer workers being required in bricks and mortar shops, but more workers being deployed in warehouse operative roles. Given women are more likely to make up the former and men the latter, automation in this case could exacerbate gender pay and job differences. It is also possible that the use of AI in recruitment (e.g. algorithms that screen CVs) could amplify workplace biases and block people from employment based on their age, ethnicity or gender.

The article is here.

The Data Scientist Putting Ethics In AI

By Poornima Apte
The Daily Dose
Originally published SEPT 25 2017

Here is an excerpt:

Chowdhury’s other personal goal — to make AI accessible to everyone — is noble, but if the technology’s ramifications are not yet fully known, might it not also be dangerous? Doomsday scenarios — AI as the rapacious monster devouring all our jobs — put forward in the media may not be in our immediate futures, but Alexandra Whittington does worry that implicit human biases could make their way into the AI of the future — a problem that might be exacerbated if not accounted for early on, before any democratization of the tools occurs. Whittington is a futurist and foresight director at Fast Future. She points to a recent example of AI in law where the “robot-lawyer” was named Ross, and the legal assistant had a woman’s name, Cara. “You look at Siri and Cortana, they’re women, right?” Whittington says. “But they’re assistants, not the attorney or the accountant.” It’s the whole garbage-in, garbage-out theory, she says, cautioning against an overly idealistic approach toward the technology.

The article is here.

New Theory Cracks Open the Black Box of Deep Learning

Natalie Wolchover
Quanta Magazine
Originally published September 21, 2017

Here is an excerpt:

In the talk, Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, presented evidence in support of a new theory explaining how deep learning works. Tishby argues that deep neural networks learn according to a procedure called the “information bottleneck,” which he and two collaborators first described in purely theoretical terms in 1999. The idea is that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts. Striking new computer experiments by Tishby and his student Ravid Shwartz-Ziv reveal how this squeezing procedure happens during deep learning, at least in the cases they studied.

Tishby’s findings have the AI community buzzing. “I believe that the information bottleneck idea could be very important in future deep neural network research,” said Alex Alemi of Google Research, who has already developed new approximation methods for applying an information bottleneck analysis to large deep neural networks. The bottleneck could serve “not only as a theoretical tool for understanding why our neural networks work as well as they do currently, but also as a tool for constructing new objectives and architectures of networks,” Alemi said.

Some researchers remain skeptical that the theory fully accounts for the success of deep learning, but Kyle Cranmer, a particle physicist at New York University who uses machine learning to analyze particle collisions at the Large Hadron Collider, said that as a general principle of learning, it “somehow smells right.”

The article is here.

Moral programming will define the future of autonomous transportation

Josh Althauser
Venture Beat
Originally published September 24, 2017

Here is an excerpt:

First do no harm?

Regardless of public sentiment, driverless cars are coming. Giants like Tesla Motors and Google have already poured billions of dollars into their respective technologies with reasonable success, and Elon Musk has said that we are much closer to a driverless future than most suspect. Robotics software engineers are making strides in self-driving AI at an awe-inspiring (and, for some, alarming) rate.

Beyond our questions of whether we want to hand over the wheel to software, there are deeper, more troubling questions that must be asked. Regardless of current sentiment, driverless cars are on their way. The real questions we should be asking as we edge closer to completely autonomous roadways lie in ethically complex areas. Among these areas of concern, one very difficult question stands out. Should we program driverless cars to kill?

At first, the answer seems obvious. No AI should have the ability to choose to kill a human. We can more easily reconcile death that results from a malfunction of some kind — brakes that give out, a failure of the car’s visual monitoring system, or a bug in the AI’s programmatic makeup. However, defining how and when AI can inflict harm isn’t that simple.

The article is here.

The guide psychologists gave carmakers to convince us it’s safe to buy self-driving cars

Olivia Goldhill

Quartz.com

Originally published September 17, 2017

Driverless cars sound great in theory. They have the potential to save lives, because humans are erratic, distracted, and often bad drivers. Once the technology is perfected, machines will be far better at driving safely.

But in practice, the notion of putting your life into the hands of an autonomous machine—let alone facing one as a pedestrian—is highly unnerving. Three out of four Americans are afraid to get into a self-driving car, an American Automobile Association survey found earlier this year.

Carmakers working to counter those fears and get driverless cars on the road have found an ally in psychologists. In a paper published this week in Nature Human Behavior, three professors from MIT Media Lab, Toulouse School of Economics, and the University of California at Irvine discuss widespread concerns and suggest psychological techniques to help allay them:

Who wants to ride in a car that would kill them to save pedestrians?

First, they address the knotty problem of how self-driving cars will be programmed to respond if they’re in a situation where they must either put their own passenger or a pedestrian at risk. This is a real world version of an ethical dilemma called “The Trolley Problem.”

The article is here.