Air Force Psychologist Found Guilty of Sexual Assault Under Guise of Exposure Therapy

Caitlin Foster
Business Insider
Originally published Dec. 10, 2018

A psychologist at Travis Air Force Base in California was found guilty on Friday of sexually assaulting military-officer patients who were seeking treatment for post-traumatic stress disorder, The Daily Republic reported.

Heath Sommer may face up to 11 years and eight months in prison after receiving a guilty verdict on six felony counts of sexual assault, according to the Republic.

Sommer used a treatment known as "exposure therapy" to lure his patients, who were military officers with previous sexual-assault experiences, into performing sexual activity, the Republic reported.

According to charges brought by Brian Roberts, the deputy district attorney who prosecuted the case, Sommer assaulted his patients through "fraudulent representation that the sexual penetration served a professional purpose when it served no professional purpose," the Republic reported.

The info is here.

The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Editing

Bernard Marr
Forbes.com
Originally posted November 16, 2018

Here is an excerpt:

Another thing experts are working to resolve in the process of gene editing is how to prevent off-target effects—when the tools mistakenly work on the wrong gene because it looks similar to the target gene.

Artificial intelligence and machine learning help make gene editing initiatives more accurate, cheaper and easier.

The future for AI and gene technology is expected to include pharmacogenomics, genetic screening tools for newborns, enhancements to agriculture and more. While we can't predict the future, one thing is for sure: AI and machine learning will accelerate our understanding of our own genetic makeup and those of other living organisms.

The info is here.

The bad news on human nature, in 10 findings from psychology

Christian Jarrett

aeon.co

Originally published 

Here is an excerpt:

We are vain and overconfident. Our irrationality and dogmatism might not be so bad were they married to some humility and self-insight, but most of us walk about with inflated views of our abilities and qualities, such as our driving skills, intelligence and attractiveness – a phenomenon that’s been dubbed the Lake Wobegon Effect after the fictional town where ‘all the women are strong, all the men are good-looking, and all the children are above average’. Ironically, the least skilled among us are the most prone to overconfidence (the so-called Dunning-Kruger effect). This vain self-enhancement seems to be most extreme and irrational in the case of our morality, such as in how principled and fair we think we are. In fact, even jailed criminals think they are kinder, more trustworthy and honest than the average member of the public.

We are moral hypocrites. It pays to be wary of those who are the quickest and loudest in condemning the moral failings of others – the chances are that moral preachers are as guilty themselves, but take a far lighter view of their own transgressions. In one study, researchers found that people rated the exact same selfish behaviour (giving themselves the quicker and easier of two experimental tasks on offer) as being far less fair when perpetuated by others. Similarly, there is a long-studied phenomenon known as actor-observer asymmetry, which in part describes our tendency to attribute other people’s bad deeds, such as our partner’s infidelities, to their character, while attributing the same deeds performed by ourselves to the situation at hand. These self-serving double standards could even explain the common feeling that incivility is on the increase – recent research shows that we view the same acts of rudeness far more harshly when they are committed by strangers than by our friends or ourselves.

The info is here.

Monitoring Moral Virtue: When the Moral Transgressions of In-Group Members Are Judged More Severely

Karim Bettache, Takeshi Hamamura, J.A. Idrissi, R.G.J. Amenyogbo, & C. Chiu
Journal of Cross-Cultural Psychology
First Published December 5, 2018
https://doi.org/10.1177/0022022118814687

Abstract

Literature indicates that people tend to judge the moral transgressions committed by out-group members more severely than those of in-group members. However, these transgressions often conflate a moral transgression with some form of intergroup harm. There is little research examining in-group versus out-group transgressions of harmless offenses, which violate moral standards that bind people together (binding foundations). As these moral standards center around group cohesiveness, a transgression committed by an in-group member may be judged more severely. The current research presented Dutch Muslims (Study 1), American Christians (Study 2), and Indian Hindus (Study 3) with a set of fictitious stories depicting harmless and harmful moral transgressions. Consistent with our expectations, participants who strongly identified with their religious community judged harmless moral offenses committed by in-group members, relative to out-group members, more severely. In contrast, this effect was absent when participants judged harmful moral transgressions. We discuss the implications of these results.

10 ways to detect health-care lies

Lawton R. Burns and Mark V. Pauly
thehill.com
Originally posted December 9, 2018

Here is an excerpt:

Why does this kind of behavior occur? While flat-out dishonesty for short-term financial gains is an obvious answer, a more common explanation is the need to say something positive when there is nothing positive to say.

This problem is acute in health care. Suppose you are faced with the assignment of solving the ageless dilemma of reducing costs while simultaneously raising quality of care. You could respond with a message of failure or a discussion of inevitable tradeoffs.

But you could also pick an idea with some internal plausibility and political appeal, fashion some careful but conditional language and announce the launch of your program. Of course, you will add that it will take a number of years before success appears, but you and your experts will argue for the idea in concept, with the details to be worked out later.

At minimum, unqualified acceptance of such proposed ideas, even (and especially) by apparently qualified people, will waste resources and will lead to enormous frustration for your audience of politicians and outraged critics of the current system. The incentives to generate falsehoods are not likely to diminish — if anything, rising spending and stagnant health outcomes strengthen them — so it is all the more important to have an accurate and fast way to detect and deter lies in health care.

The info is here.

The Seductive Diversion of ‘Solving’ Bias in Artificial Intelligence

Julia Powles
Medium.com
Originally posted December 7, 2018

Here is an excerpt:

There are three problems with this focus on A.I. bias. The first is that addressing bias as a computational problem obscures its root causes. Bias is a social problem, and seeking to solve it within the logic of automation is always going to be inadequate.

Second, even apparent success in tackling bias can have perverse consequences. Take the example of a facial recognition system that works poorly on women of color because of the group’s underrepresentation both in the training data and among system designers. Alleviating this problem by seeking to “equalize” representation merely co-opts designers in perfecting vast instruments of surveillance and classification.

When underlying systemic issues remain fundamentally untouched, the bias fighters simply render humans more machine readable, exposing minorities in particular to additional harms.

Third — and most dangerous and urgent of all — is the way in which the seductive controversy of A.I. bias, and the false allure of “solving” it, detracts from bigger, more pressing questions. Bias is real, but it’s also a captivating diversion.

The info is here.

China Uses "Ethics" as Censorship

China sets up a video game ethics panel in its new approval process

Owen S. Good
www.polygon.com
Originally posted December 8, 2018

In China, it’s about ethics in video games.

The South China Morning Post reports that the nation now has an “Online Game Ethics Committee,” as a part of the government’s laborious process for game censorship approvals. China Central Television, the state’s broadcaster, said this ethics-in-games committee was formed to address national concerns over internet addiction, “unsuitable content” and childhood myopia (nearsightedness, apparently with video games as a cause?)

The state TV report said the committee has already looked at 20 games, rejecting nine and ruling that the other 11 have to change “certain content.” The titles of the games were not revealed.

The info is here.

Every Leader’s Guide to the Ethics of AI

Thomas H. Davenport and Vivek Katyal
MIT Sloan Management Review Blog
Originally published

Here is an excerpt:

Leaders should ask themselves whether the AI applications they use treat all groups equally. Unfortunately, some AI applications, including machine learning algorithms, put certain groups at a disadvantage. This issue, called algorithmic bias, has been identified in diverse contexts, including judicial sentencing, credit scoring, education curriculum design, and hiring decisions. Even when the creators of an algorithm have not intended any bias or discrimination, they and their companies have an obligation to try to identify and prevent such problems and to correct them upon discovery.

Ad targeting in digital marketing, for example, uses machine learning to make many rapid decisions about what ad is shown to which consumer. Most companies don’t even know how the algorithms work, and the cost of an inappropriately targeted ad is typically only a few cents. However, some algorithms have been found to target high-paying job ads more to men, and others target ads for bail bondsmen to people with names more commonly held by African Americans. The ethical and reputational costs of biased ad-targeting algorithms, in such cases, can potentially be very high.

Of course, bias isn’t a new problem. Companies using traditional decision-making processes have made these judgment errors, and algorithms created by humans are sometimes biased as well. But AI applications, which can create and apply models much faster than traditional analytics, are more likely to exacerbate the issue. The problem becomes even more complex when black box AI approaches make interpreting or explaining the model’s logic difficult or impossible. While full transparency of models can help, leaders who consider their algorithms a competitive asset will quite likely resist sharing them.

The info is here.

Why It’s Easier to Make Decisions for Someone Else

Evan Polman
Harvard Business Review
Originally posted November 13, 2018

Here is an excerpt:

What we found was two-fold: Not only did participants choose differently when it was for themselves rather than for someone else, but the way they chose was different. When choosing for themselves, participants focused more on a granular level, zeroing in on the minutiae, something we described in our research as a cautious mindset. Employing a cautious mindset when making a choice means being more reserved, deliberate, and risk averse. Rather than exploring and collecting a plethora of options, the cautious mindset prefers to consider a few at a time on a deeper level, examining a cross-section of the larger whole.

But when it came to deciding for others, study participants looked more at the array of options and focused on their overall impression. They were bolder, operating from what we called an adventurous mindset. An adventurous mindset prioritizes novelty over a deeper dive into what those options actually consist of; the availability of numerous choices is more appealing than their viability. Simply put, they preferred and examined more information before making a choice, and as my previous research has shown, they recommended their choice to others with more gusto.

These findings align with my earlier work with Kyle Emich of University of Delaware on how people are more creative on behalf of others. When we are brainstorming ideas to other people’s problems, we’re inspired; we have a free flow of ideas to spread out on the table without judgment, second-guessing, or overthinking.

The info is here.