Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care

Welcome to the nexus of ethics, psychology, morality, technology, health care, and philosophy

Saturday, January 12, 2019

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.

Friday, January 11, 2019

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.

Thursday, January 10, 2019

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.

Wednesday, January 9, 2019

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.

'Should we even consider this?' WHO starts work on gene editing ethics

Agence France-Presse
Originally published 3 Dec 2018

The World Health Organization is creating a panel to study the implications of gene editing after a Chinese scientist controversially claimed to have created the world’s first genetically edited babies.

“It cannot just be done without clear guidelines,” Tedros Adhanom Ghebreyesus, the head of the UN health agency, said in Geneva.

The organisation was gathering experts to discuss rules and guidelines on “ethical and social safety issues”, added Tedros, a former Ethiopian health minister.

Tedros made the comments after a medical trial, which was led by Chinese scientist He Jiankui, claimed to have successfully altered the DNA of twin girls, whose father is HIV-positive, to prevent them from contracting the virus.

His experiment has prompted widespread condemnation from the scientific community in China and abroad, as well as a backlash from the Chinese government.

The info is here.

Tuesday, January 8, 2019

The 3 faces of clinical reasoning: Epistemological explorations of disparate error reduction strategies.

Sandra Monteiro, Geoff Norman, & Jonathan Sherbino
J Eval Clin Pract. 2018 Jun;24(3):666-673.

Abstract

There is general consensus that clinical reasoning involves 2 stages: a rapid stage where 1 or more diagnostic hypotheses are advanced and a slower stage where these hypotheses are tested or confirmed. The rapid hypothesis generation stage is considered inaccessible for analysis or observation. Consequently, recent research on clinical reasoning has focused specifically on improving the accuracy of the slower, hypothesis confirmation stage. Three perspectives have developed in this line of research, and each proposes different error reduction strategies for clinical reasoning. This paper considers these 3 perspectives and examines the underlying assumptions. Additionally, this paper reviews the evidence, or lack of, behind each class of error reduction strategies. The first perspective takes an epidemiological stance, appealing to the benefits of incorporating population data and evidence-based medicine in every day clinical reasoning. The second builds on the heuristic and bias research programme, appealing to a special class of dual process reasoning models that theorizes a rapid error prone cognitive process for problem solving with a slower more logical cognitive process capable of correcting those errors. Finally, the third perspective borrows from an exemplar model of categorization that explicitly relates clinical knowledge and experience to diagnostic accuracy.

A pdf can be downloaded here.

Algorithmic governance: Developing a research agenda through the power of collective intelligence

John Danaher, Michael J Hogan, Chris Noone, Ronan Kennedy, et.al
Big Data & Society
July–December 2017: 1–21

Abstract

We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence.

The paper is here.