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.
Thursday, January 10, 2019
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.
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.
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.
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.
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.
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.
Monday, January 7, 2019
Ethics of missionary work called into question after death of American missionary John Allen Chau
Holly Meyer
Nashville Tennessean
Originally published December 2, 2018
Christians are facing scrutiny for evangelizing in remote parts of the world after members of an isolated tribe in the Bay of Bengal killed a U.S. missionary who was trying to tell them about Jesus.
The death of John Allen Chau raises questions about the ethics of missionary work and whether he acted appropriately by contacting the Sentinelese, a self-sequestered Indian tribe that has resisted outside contact for thousands of years.
It is tragic, but figuring out what can be learned from Chau's death honors his memory and passion, said Scott Harris, the missions minister at Brentwood Baptist Church and a former trustee chairman of the Southern Baptist Convention's International Mission Board.
"In general, evaluation and accountability is so needed," Harris said. "Maturing fieldworkers that have a heart for the cultures of the world will welcome honest, hard questions."
The info is here.
Nashville Tennessean
Originally published December 2, 2018
Christians are facing scrutiny for evangelizing in remote parts of the world after members of an isolated tribe in the Bay of Bengal killed a U.S. missionary who was trying to tell them about Jesus.
The death of John Allen Chau raises questions about the ethics of missionary work and whether he acted appropriately by contacting the Sentinelese, a self-sequestered Indian tribe that has resisted outside contact for thousands of years.
It is tragic, but figuring out what can be learned from Chau's death honors his memory and passion, said Scott Harris, the missions minister at Brentwood Baptist Church and a former trustee chairman of the Southern Baptist Convention's International Mission Board.
"In general, evaluation and accountability is so needed," Harris said. "Maturing fieldworkers that have a heart for the cultures of the world will welcome honest, hard questions."
The info is here.
The Boundary Between Our Bodies and Our Tech
Kevin Lincoln
Pacific Standard
Originally published November 8, 2018
Here is an excerpt:
"They argued that, essentially, the mind and the self are extended to those devices that help us perform what we ordinarily think of as our cognitive tasks," Lynch says. This can include items as seemingly banal and analog as a piece of paper and a pen, which help us remember, a duty otherwise performed by the brain. According to this philosophy, the shopping list, for example, becomes part of our memory, the mind spilling out beyond the confines of our skull to encompass anything that helps it think.
"Now if that thought is right, it's pretty clear that our minds have become even more radically extended than ever before," Lynch says. "The idea that our self is expanding through our phones is plausible, and that's because our phones, and our digital devices generally—our smartwatches, our iPads—all these things have become a really intimate part of how we go about our daily lives. Intimate in the sense in which they're not only on our body, but we sleep with them, we wake up with them, and the air we breathe is filled, in both a literal and figurative sense, with the trails of ones and zeros that these devices leave behind."
This gets at one of the essential differences between a smartphone and a piece of paper, which is that our relationship with our phones is reciprocal: We not only put information into the device, we also receive information from it, and, in that sense, it shapes our lives far more actively than would, say, a shopping list. The shopping list isn't suggesting to us, based on algorithmic responses to our past and current shopping behavior, what we should buy; the phone is.
The info is here.
Pacific Standard
Here is an excerpt:
"They argued that, essentially, the mind and the self are extended to those devices that help us perform what we ordinarily think of as our cognitive tasks," Lynch says. This can include items as seemingly banal and analog as a piece of paper and a pen, which help us remember, a duty otherwise performed by the brain. According to this philosophy, the shopping list, for example, becomes part of our memory, the mind spilling out beyond the confines of our skull to encompass anything that helps it think.
"Now if that thought is right, it's pretty clear that our minds have become even more radically extended than ever before," Lynch says. "The idea that our self is expanding through our phones is plausible, and that's because our phones, and our digital devices generally—our smartwatches, our iPads—all these things have become a really intimate part of how we go about our daily lives. Intimate in the sense in which they're not only on our body, but we sleep with them, we wake up with them, and the air we breathe is filled, in both a literal and figurative sense, with the trails of ones and zeros that these devices leave behind."
This gets at one of the essential differences between a smartphone and a piece of paper, which is that our relationship with our phones is reciprocal: We not only put information into the device, we also receive information from it, and, in that sense, it shapes our lives far more actively than would, say, a shopping list. The shopping list isn't suggesting to us, based on algorithmic responses to our past and current shopping behavior, what we should buy; the phone is.
The info is here.
Sunday, January 6, 2019
Toward an Ethics of AI Assistants: an Initial Framework
John Danaher
Philosophy and Technology:1-25 (forthcoming)
Abstract
Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: searching, planning, messaging, scheduling and so on. Usage of such devices is effectively a form of algorithmic outsourcing: getting a smart algorithm to do something on your behalf. Many have expressed concerns about this algorithmic outsourcing. They claim that it is dehumanising, leads to cognitive degeneration, and robs us of our freedom and autonomy. Some people have a more subtle view, arguing that it is problematic in those cases where its use may degrade important interpersonal virtues. In this article, I assess these objections to the use of AI assistants. I will argue that the ethics of their use is complex. There are no quick fixes or knockdown objections to the practice, but there are some legitimate concerns. By carefully analysing and evaluating the objections that have been lodged to date, we can begin to articulate an ethics of personal AI use that navigates those concerns. In the process, we can locate some paradoxes in our thinking about outsourcing and technological dependence, and we can think more clearly about what it means to live a good life in the age of smart machines.
The paper is here.
Philosophy and Technology:1-25 (forthcoming)
Abstract
Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: searching, planning, messaging, scheduling and so on. Usage of such devices is effectively a form of algorithmic outsourcing: getting a smart algorithm to do something on your behalf. Many have expressed concerns about this algorithmic outsourcing. They claim that it is dehumanising, leads to cognitive degeneration, and robs us of our freedom and autonomy. Some people have a more subtle view, arguing that it is problematic in those cases where its use may degrade important interpersonal virtues. In this article, I assess these objections to the use of AI assistants. I will argue that the ethics of their use is complex. There are no quick fixes or knockdown objections to the practice, but there are some legitimate concerns. By carefully analysing and evaluating the objections that have been lodged to date, we can begin to articulate an ethics of personal AI use that navigates those concerns. In the process, we can locate some paradoxes in our thinking about outsourcing and technological dependence, and we can think more clearly about what it means to live a good life in the age of smart machines.
The paper is here.
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