Sui-Lee Wee and Elsie Chen
The New York Times
Originally published November 30, 2018
First it was a proposal to transplant a head to a new body. Then it was the world’s first cloned primates. Now it is genetically edited babies.
Those recent scientific announcements, generating reactions that went from unease to shock, had one thing in common: All involved scientists from China.
China has set its sights on becoming a leader in science, pouring millions of dollars into research projects and luring back top Western-educated Chinese talent. The country’s scientists are accustomed to attention-grabbing headlines by their colleagues as they race to dominate their fields.
But when He Jiankui announced on Monday that he had created the world’s first genetically edited babies, Chinese scientists — like those elsewhere — denounced it as a step too far. Now many are asking whether their country’s intense focus on scientific achievement has come at the expense of ethical standards.
The info is here.
Thursday, January 3, 2019
Why We Need to Audit Algorithms
James Guszcza, Iyad Rahwan Will, Bible Manuel Cebrian, & Vic Katyal
Harvard Business Review
Originally published November 28, 2018
Algorithmic decision-making and artificial intelligence (AI) hold enormous potential and are likely to be economic blockbusters, but we worry that the hype has led many people to overlook the serious problems of introducing algorithms into business and society. Indeed, we see many succumbing to what Microsoft’s Kate Crawford calls “data fundamentalism” — the notion that massive datasets are repositories that yield reliable and objective truths, if only we can extract them using machine learning tools. A more nuanced view is needed. It is by now abundantly clear that, left unchecked, AI algorithms embedded in digital and social technologies can encode societal biases, accelerate the spread of rumors and disinformation, amplify echo chambers of public opinion, hijack our attention, and even impair our mental wellbeing.
Ensuring that societal values are reflected in algorithms and AI technologies will require no less creativity, hard work, and innovation than developing the AI technologies themselves. We have a proposal for a good place to start: auditing. Companies have long been required to issue audited financial statements for the benefit of financial markets and other stakeholders. That’s because — like algorithms — companies’ internal operations appear as “black boxes” to those on the outside. This gives managers an informational advantage over the investing public which could be abused by unethical actors. Requiring managers to report periodically on their operations provides a check on that advantage. To bolster the trustworthiness of these reports, independent auditors are hired to provide reasonable assurance that the reports coming from the “black box” are free of material misstatement. Should we not subject societally impactful “black box” algorithms to comparable scrutiny?
The info is here.
Harvard Business Review
Originally published November 28, 2018
Algorithmic decision-making and artificial intelligence (AI) hold enormous potential and are likely to be economic blockbusters, but we worry that the hype has led many people to overlook the serious problems of introducing algorithms into business and society. Indeed, we see many succumbing to what Microsoft’s Kate Crawford calls “data fundamentalism” — the notion that massive datasets are repositories that yield reliable and objective truths, if only we can extract them using machine learning tools. A more nuanced view is needed. It is by now abundantly clear that, left unchecked, AI algorithms embedded in digital and social technologies can encode societal biases, accelerate the spread of rumors and disinformation, amplify echo chambers of public opinion, hijack our attention, and even impair our mental wellbeing.
Ensuring that societal values are reflected in algorithms and AI technologies will require no less creativity, hard work, and innovation than developing the AI technologies themselves. We have a proposal for a good place to start: auditing. Companies have long been required to issue audited financial statements for the benefit of financial markets and other stakeholders. That’s because — like algorithms — companies’ internal operations appear as “black boxes” to those on the outside. This gives managers an informational advantage over the investing public which could be abused by unethical actors. Requiring managers to report periodically on their operations provides a check on that advantage. To bolster the trustworthiness of these reports, independent auditors are hired to provide reasonable assurance that the reports coming from the “black box” are free of material misstatement. Should we not subject societally impactful “black box” algorithms to comparable scrutiny?
The info is here.
Wednesday, January 2, 2019
When Fox News staffers break ethics rules, discipline follows — or does it?
Margaret Sullivan
The Washington Post
Originally published November 29, 2018
There are ethical standards at Fox News, we’re told.
But just what they are, or how they’re enforced, is an enduring mystery.
When Sean Hannity and Jeanine Pirro appeared onstage with President Trump at a Missouri campaign rally, the network publicly acknowledged that this ran counter to its practices.
“Fox News does not condone any talent participating in campaign events,” the network said in a statement. “This was an unfortunate distraction and has been addressed.”
Or take what happened this week.
When the staff of “Fox & Friends” was found to have provided a pre-interview script for Scott Pruitt, then the Environmental Protection Agency head, the network frowned: “This is not standard practice whatsoever and the matter is being addressed internally with those involved.”
“Not standard practice” is putting it mildly, as the Daily Beast’s Maxwell Tani — who broke the story — noted, quoting David Hawkins, formerly of CBS News and CNN, who teaches journalism at Fordham University...
The info is here.
The Washington Post
Originally published November 29, 2018
There are ethical standards at Fox News, we’re told.
But just what they are, or how they’re enforced, is an enduring mystery.
When Sean Hannity and Jeanine Pirro appeared onstage with President Trump at a Missouri campaign rally, the network publicly acknowledged that this ran counter to its practices.
“Fox News does not condone any talent participating in campaign events,” the network said in a statement. “This was an unfortunate distraction and has been addressed.”
Or take what happened this week.
When the staff of “Fox & Friends” was found to have provided a pre-interview script for Scott Pruitt, then the Environmental Protection Agency head, the network frowned: “This is not standard practice whatsoever and the matter is being addressed internally with those involved.”
“Not standard practice” is putting it mildly, as the Daily Beast’s Maxwell Tani — who broke the story — noted, quoting David Hawkins, formerly of CBS News and CNN, who teaches journalism at Fordham University...
The info is here.
The Intuitive Appeal of Explainable Machines
Andrew D. Selbst & Solon Barocas
Fordham Law Review -Volume 87
Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties.
Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible.
In most cases, intuition serves as the unacknowledged bridge between a descriptive account to a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself.
The info is here.
Fordham Law Review -Volume 87
Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties.
Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible.
In most cases, intuition serves as the unacknowledged bridge between a descriptive account to a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself.
The info is here.
Tuesday, January 1, 2019
AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations
Floridi, L., Cowls, J., Beltrametti, M. et al.
Minds & Machines (2018).
https://doi.org/10.1007/s11023-018-9482-5
Abstract
This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
Minds & Machines (2018).
https://doi.org/10.1007/s11023-018-9482-5
Abstract
This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
Monday, December 31, 2018
How free is our will?
Kevin Mitchell
Wiring The Brain Blog
Originally posted November 25, 2018
Here is an excerpt:
Being free – to my mind at least – doesn’t mean making decisions for no reasons, it means making them for your reasons. Indeed, I would argue that this is exactly what is required to allow any kind of continuity of the self. If you were just doing things on a whim all the time, what would it mean to be you? We accrue our habits and beliefs and intentions and goals over our lifetime, and they collectively affect how actions are suggested and evaluated.
Whether we are conscious of that is another question. Most of our reasons for doing things are tacit and implicit – they’ve been wired into our nervous systems without our even being aware of them. But they’re still part of us – you could argue they’re precisely what makes us us. Even if most of that decision-making happens subconsciously, it’s still you doing it.
Ultimately, whether you think you have free will or not may depend less on the definition of “free will” and more on the definition of “you”. If you identify just as the president – the decider-in-chief – then maybe you’ll be dismayed at how little control you seem to have or how rarely you really exercise it. (Not never, but maybe less often than your ego might like to think).
But that brings us back to a very dualist position, identifying you with only your conscious mind, as if it can somehow be separated from all the underlying workings of your brain. Perhaps it’s more appropriate to think that you really comprise all of the machinery of government, even the bits that the president never sees or is not even aware exists.
The info is here.
Wiring The Brain Blog
Originally posted November 25, 2018
Here is an excerpt:
Being free – to my mind at least – doesn’t mean making decisions for no reasons, it means making them for your reasons. Indeed, I would argue that this is exactly what is required to allow any kind of continuity of the self. If you were just doing things on a whim all the time, what would it mean to be you? We accrue our habits and beliefs and intentions and goals over our lifetime, and they collectively affect how actions are suggested and evaluated.
Whether we are conscious of that is another question. Most of our reasons for doing things are tacit and implicit – they’ve been wired into our nervous systems without our even being aware of them. But they’re still part of us – you could argue they’re precisely what makes us us. Even if most of that decision-making happens subconsciously, it’s still you doing it.
Ultimately, whether you think you have free will or not may depend less on the definition of “free will” and more on the definition of “you”. If you identify just as the president – the decider-in-chief – then maybe you’ll be dismayed at how little control you seem to have or how rarely you really exercise it. (Not never, but maybe less often than your ego might like to think).
But that brings us back to a very dualist position, identifying you with only your conscious mind, as if it can somehow be separated from all the underlying workings of your brain. Perhaps it’s more appropriate to think that you really comprise all of the machinery of government, even the bits that the president never sees or is not even aware exists.
The info is here.
Sunday, December 30, 2018
AI thinks like a corporation—and that’s worrying
Jonnie Penn
The Economist
Originally posted November 26, 2018
Here is an excerpt:
Perhaps as a result of this misguided impression, public debates continue today about what value, if any, the social sciences could bring to artificial-intelligence research. In Simon’s view, AI itself was born in social science.
David Runciman, a political scientist at the University of Cambridge, has argued that to understand AI, we must first understand how it operates within the capitalist system in which it is embedded. “Corporations are another form of artificial thinking-machine in that they are designed to be capable of taking decisions for themselves,” he explains.
“Many of the fears that people now have about the coming age of intelligent robots are the same ones they have had about corporations for hundreds of years,” says Mr Runciman. The worry is, these are systems we “never really learned how to control.”
After the 2010 BP oil spill, for example, which killed 11 people and devastated the Gulf of Mexico, no one went to jail. The threat that Mr Runciman cautions against is that AI techniques, like playbooks for escaping corporate liability, will be used with impunity.
Today, pioneering researchers such as Julia Angwin, Virginia Eubanks and Cathy O’Neil reveal how various algorithmic systems calcify oppression, erode human dignity and undermine basic democratic mechanisms like accountability when engineered irresponsibly. Harm need not be deliberate; biased data-sets used to train predictive models also wreak havoc. It may be, given the costly labour required to identify and address these harms, that something akin to “ethics as a service” will emerge as a new cottage industry. Ms O’Neil, for example, now runs her own service that audits algorithms.
The info is here.
The Economist
Originally posted November 26, 2018
Here is an excerpt:
Perhaps as a result of this misguided impression, public debates continue today about what value, if any, the social sciences could bring to artificial-intelligence research. In Simon’s view, AI itself was born in social science.
David Runciman, a political scientist at the University of Cambridge, has argued that to understand AI, we must first understand how it operates within the capitalist system in which it is embedded. “Corporations are another form of artificial thinking-machine in that they are designed to be capable of taking decisions for themselves,” he explains.
“Many of the fears that people now have about the coming age of intelligent robots are the same ones they have had about corporations for hundreds of years,” says Mr Runciman. The worry is, these are systems we “never really learned how to control.”
After the 2010 BP oil spill, for example, which killed 11 people and devastated the Gulf of Mexico, no one went to jail. The threat that Mr Runciman cautions against is that AI techniques, like playbooks for escaping corporate liability, will be used with impunity.
Today, pioneering researchers such as Julia Angwin, Virginia Eubanks and Cathy O’Neil reveal how various algorithmic systems calcify oppression, erode human dignity and undermine basic democratic mechanisms like accountability when engineered irresponsibly. Harm need not be deliberate; biased data-sets used to train predictive models also wreak havoc. It may be, given the costly labour required to identify and address these harms, that something akin to “ethics as a service” will emerge as a new cottage industry. Ms O’Neil, for example, now runs her own service that audits algorithms.
The info is here.
Saturday, December 29, 2018
Woman who inherited fatal illness to sue doctors in groundbreaking case
Robin McKie
The Guardian
Originally published November 25, 2018
Lawyers are bringing a case against a London hospital trust that could trigger major changes to the rules governing patient confidentiality. The case involves a woman who is suing doctors because they failed to tell her about her father’s fatal hereditary disease before she had her own child.
The woman discovered – after giving birth – that her father carried the gene for Huntington’s disease, a degenerative, incurable brain condition. Later she found out she had inherited the gene and that her own daughter, now eight, has a 50% chance of having it.
The woman – who cannot be named for legal reasons – says she would have had an abortion had she known about her father’s condition, and is suing the doctors who failed to tell her about the risks she and her child faced. It is the first case in English law to deal with a relative’s claim over issues of genetic responsibility.
“This could really change the way we do medicine, because it is about the duty that doctors have to share genetic test results with relatives and whether the duty exists in law,” said Anna Middleton, head of society and ethics research at the Wellcome Genome Campus in Cambridge.
The info is here.
The Guardian
Originally published November 25, 2018
Lawyers are bringing a case against a London hospital trust that could trigger major changes to the rules governing patient confidentiality. The case involves a woman who is suing doctors because they failed to tell her about her father’s fatal hereditary disease before she had her own child.
The woman discovered – after giving birth – that her father carried the gene for Huntington’s disease, a degenerative, incurable brain condition. Later she found out she had inherited the gene and that her own daughter, now eight, has a 50% chance of having it.
The woman – who cannot be named for legal reasons – says she would have had an abortion had she known about her father’s condition, and is suing the doctors who failed to tell her about the risks she and her child faced. It is the first case in English law to deal with a relative’s claim over issues of genetic responsibility.
“This could really change the way we do medicine, because it is about the duty that doctors have to share genetic test results with relatives and whether the duty exists in law,” said Anna Middleton, head of society and ethics research at the Wellcome Genome Campus in Cambridge.
The info is here.
Friday, December 28, 2018
The Theory of Dyadic Morality: Reinventing Moral Judgment by Redefining Harm
Chelsea Schein & Kurt Gray
Personality and Social Psychology Review
Volume: 22 issue: 1, page(s): 32-70
Article first published online: May 14, 2017; Issue published: February 1, 2018
Abstract
The nature of harm—and therefore moral judgment—may be misunderstood. Rather than an objective matter of reason, we argue that harm should be redefined as an intuitively perceived continuum. This redefinition provides a new understanding of moral content and mechanism—the constructionist Theory of Dyadic Morality (TDM). TDM suggests that acts are condemned proportional to three elements: norm violations, negative affect, and—importantly—perceived harm. This harm is dyadic, involving an intentional agent causing damage to a vulnerable patient (A→P). TDM predicts causal links both from harm to immorality (dyadic comparison) and from immorality to harm (dyadic completion). Together, these two processes make the “dyadic loop,” explaining moral acquisition and polarization. TDM argues against intuitive harmless wrongs and modular “foundations,” but embraces moral pluralism through varieties of values and the flexibility of perceived harm. Dyadic morality impacts understandings of moral character, moral emotion, and political/cultural differences, and provides research guidelines for moral psychology.
The review is here.
Personality and Social Psychology Review
Volume: 22 issue: 1, page(s): 32-70
Article first published online: May 14, 2017; Issue published: February 1, 2018
Abstract
The nature of harm—and therefore moral judgment—may be misunderstood. Rather than an objective matter of reason, we argue that harm should be redefined as an intuitively perceived continuum. This redefinition provides a new understanding of moral content and mechanism—the constructionist Theory of Dyadic Morality (TDM). TDM suggests that acts are condemned proportional to three elements: norm violations, negative affect, and—importantly—perceived harm. This harm is dyadic, involving an intentional agent causing damage to a vulnerable patient (A→P). TDM predicts causal links both from harm to immorality (dyadic comparison) and from immorality to harm (dyadic completion). Together, these two processes make the “dyadic loop,” explaining moral acquisition and polarization. TDM argues against intuitive harmless wrongs and modular “foundations,” but embraces moral pluralism through varieties of values and the flexibility of perceived harm. Dyadic morality impacts understandings of moral character, moral emotion, and political/cultural differences, and provides research guidelines for moral psychology.
The review is here.
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