Past Events

Friday, October 18, 2019; 3:45 – 5:45 PM
5 Washington Place, 202
New York, NY, 10003

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Abstract: Elizabeth Anscombe and Peter Geach both had the view that engaging with certain thought experiments in the course of moral theorizing was immoral. They had several reasons for viewing such engagement as immoral, but this presentation focuses on the claim that engaging with certain thought experiments is intrinsically wrong. Their claim is diagnosed and criticized, and then contrasted with the views of others, such as Susan Wolf, who holds a similar view but for different reasons. The common thread is that what one represents as a practical possibility, that is, as a possibility for the purposes of practical deliberation, is itself something that is morally evaluable. This is then shown to be relevant to understanding some results in empirical moral psychology with purport to show surprising features of how we represent possibilities.

Join us Thursday, September 12th, 2019, for a colloquium with Travis Rieder entitled “In Pain: A Bioethicist’s Personal (and Professional) Struggle with Opioids”.

More than 72,000 Americans died from drug overdose in 2017, which is more than were killed at the height of the HIV/AIDS epidemic. This public health crisis continues to be driven by opioids, which were involved in more than 49,000 of those deaths. Despite the scale of this problem, the opioid crisis has received fairly little sustained attention in the bioethics literature. In this talk, Travis Rieder argues that this is a serious oversight. Combining his own experience of both the benefits and burdens of opioids with his expertise as a bioethicist, Travis canvasses the broad landscape of moral problems raised at the intersection of pain, opioids, and addiction—as well as a few potential solutions.

Thursday, September 12th, 2019
5:30PM
Pless Hall Lounge
82 Washington Square East, New York, NY 10003

Snacks will be served

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Join us on November 19th

The NYU College of Global Public Health and its Center for Bioethics are partnering with The Greenwall Foundation to sponsor the 2019 William C. Stubing Memorial Lecture. Dr. Amy Gutmann, President of the University of Pennsylvania and Chair of the Presidential Commission for the Study of Bioethical Issues (2009-2017), will discuss “Can Democratic Deliberation Help Us to Resolve Difficult Issues? The Case of Physician Aid-in-Dying.” The conversation will be moderated by Susan Dentzer, Senior Policy Fellow at the Duke-Margolis Center for Health Policy and former On-Air Health Correspondent for PBS NewsHour.

Program Details

• 5:30 p.m. Reception
• 6:30 p.m. Lecture

Seats are limited, so please be sure to RSVP.

If you previously RSVPed but can no longer attend, please notify gph.events@nyu.edu as soon as possible to free your seat for another.

Join us Thursday, April 4th, 2019, for a lunchtime lecture with Gunnar Björnsson entitled “Ways to be Implicated”.

Abstract:
An agent can be morally implicated in a harm even though the harm would have occurred without that agent’s contribution. Some such cases of agential redundancy seem to be well understood if we take blameworthiness to require, not that the agent controlled whether harm happens, but that the harm happened because of the agent, explained by the agent’s lack of regard for what is morally important, say. Causal-explanatory relations famously do not require counterfactual dependence.

The idea that blameworthiness for an outcome requires a causal-explanatory relation to that outcome is familiar and promising, but encounters problems with a variety of cases of collective harm and complicity. For example, a great many people share blame for the current level of climate threat, but we can’t say of each of them that the threat level is what it is because that person didn’t care enough. Likewise, when an addict gets the drug with which he ODs from one of several available dealers that would have supplied him with the very same drug had he asked them, it seems that the dealer shares at least some blame for the outcome, but it seems problematic to say that the addict’s life was ruined because of this dealer’s willingness to provide the drugs. Generally, as a range of cases show, switching a harmful process from one track to another that leads to the same outcome does not ground causal responsibility for that harm.

In this talk, Björnsson argues that both these problem cases can be accounted for without giving up the requirement of a causal-explanatory connection once we recognize that the relevant explanations can invoke the quality of will of a group of individuals. Björnsson has previously defended this idea for collective harm cases; here he argues that it extends to cases where only one member of the group is at all causally involved, and where the harm cannot be said to have happened because of that member.

Biography:

Gunnar Björnsson is Professor of Practical Philosophy at Stockholm University. He works on issues in metaethics, moral responsibility, collective moral agency, and philosophy of language, with recent work appearing in, among other places, Mind, Noûs, Ethics, and Philosophy and Phenomenological Research.

Thursday, April 4th, 2019
12:30 – 2:00 PM
Room 1221
715-719 Broadway, New York, NY 10003

Lunch will be served

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On April 26, 2019, the NYU Center for Bioethics will host a workshop on “The Ethics of Artificial Intelligence and Healthcare”.

Artificial intelligence (AI) systems have the potential to dramatically improve health outcomes for patients by analyzing enormous amounts of data, identifying patterns and predicting results. To succeed, though, these systems need access to personal and group health data and use complex algorithms that are difficult, and sometimes impossible, to understand. This creates a potential conflict with current ethical standards for the treatment of patients, which emphasize fairness, consent, and privacy.  In this workshop hosted by the NYU Center for Bioethics, leading AI scientists, philosophers, and bioethicists will explore the following issues:

a) Bias in machine learning algorithms has led to discrimination in areas such as criminal sentencing, predictive policing, and hiring decisions. While healthcare poses some similar dangers, it often presents very different challenges. For example, data from clinical trials can discriminate against women, racial minorities, and the elderly because these groups are less likely to participate in trials, potentially leading to AI algorithms privileging treatment options optimized for specific demographics.  If so, what are some ways to minimize harmful algorithmic biases specifically in healthcare?

b) In a number of contexts such as criminal sentencing, the predictive capacity of AI is not the only metric for success; we must also know how a decision was made in order to be able to explain those decisions. But while explainability may be required in such contexts, is it a necessary requirement for gaining the informed consent of patients? If it is, does the explanation have to include the causal relationship between symptoms and diagnosis/treatment, or can the connection be merely correlational? Does the explanation have to be intelligible to patients, to doctors, or even just to machine learning technicians?  If certain diagnoses/treatment tools cannot meet the required standard of explanation, should we revert to more explainable but less predictive machine learning tools?

c) As with other applications of AI, realizing the benefits of these technologies in healthcare requires access to the vast quantities of data, raising concerns of data privacy. Respecting patients’ privacy remains one of the core values in health care. At the same time, as AI systems continue to develop, we may need to balance privacy concerns with novel medical innovations that can potentially save many lives. Can machine learning help us achieve both objectives? Or must there inevitably be a tradeoff?

Space is limited. Please RSVP to express interest. Priority will be given to those who have demonstrated an interest in the area. Confirmations will follow in coming weeks.

Speakers and panelists will include:

Glenn Cohen (Harvard), Tina Eliassi-Rad (Northeastern), S. Matthew Liao (NYU), Alex John London (Carnegie Mellon), Francesca Rossi (IBM), Walter Sinnott-Armstrong (Duke), Effy Vayena (ETH Zurich), Serena Yeung (Stanford)

Click below to see speaker abstracts and bios.