• What is one of them?

Answer: “Do unto others as you would have them do unto you.”

Q2. Act in such a way that you treat humanity, whether in your own person or in the person of any other, never merely as a means to an end, but always at the same time as an end.”
― Immanuel Kant, Grounding for the Metaphysics of Morals/On a Supposed Right to Lie Because of Philanthropic Concerns

• Analyze that quote: what does it mean and do you agree?

Answer:

It seems like a version of the golden rule. It means treat people as if they have a certain inherent worth and dignity that you should respect.

Q3. In my observation, when people are in conflict, the Golden Rule gets neglected

• What is your observation? 

Answer:

That seems to be the case. People generally don’t act compassionate when they are in conflict. That’s the nature of conflict.

The US contains 2 popular attitudes:

(1) “Broadly cooperative”

(2) Broadly hostile

• Which generally leads people to treat others better?
  • (more specifically, per the golden rule and categorical imperative)

• Which generally leads people to violate the golden rule and categorical imperative?

Answer:

Broadly cooperative attitudes lead people to treat others better while broadly hostile attitudes cause people to treat others worse.

Q4. So, what do you think about the concept of “thoughtcrime”?

Answer:

It doesn’t exist. You shouldn’t punish people for attitudes, only the actions those attitudes can lead to.

Q5. dP/dT  –  calling all calculus students:

What does that mean??

Answer:

Essentially the change in the level of “P” (cooperation) with respect to time.

Q6. Why?

What if cultural differences are high? Low?

What if competition is high? Low?

Answer:

Cultural differences probably act as a barrier to cooperation.

1. Explain how you used ChatGPT or other AI tool(s) to help for this HW’s update to your project, or note if it wasn’t used.
2. Continue to develop the project, as we build it step by step over the semester so that it will be manageable rather than a crunch at the end, as follows. Write up another 350 words or more (per person if a group project) of new content if your project is a paper that must be written from scratch (for example if it is about yourself). If you are using an automatic text generator to help write it it would probably be quite a bit more than that amount of words. If the progress was code development or website creation, give the new code. If slides, paste the new text from the new slides into your blog and describe any images or graphics. If something else, explain specifically what you did, being sure to give examples if that makes sense. Label this HW consistently as in this example. List any sources you used, such as websites or pages, ChatGPT or other tools, books, or whatever it might be. This takes some effort and it counts as part of your work. If your project is not a paper, explain what you did on your blog. For team projects, focus on your own activities although you can also discuss the overall effort to provide some context. Explain and give evidence (for example, if a web site, you could provide a link to it; if software, give the code; if a skit, give some of the script or list rehearsal or meeting times; if artwork, provide an image showing its status, etc.).  If you’re not sure what to do, see me or send me an email and I will try to suggest something.

Criticism

Effective altruism has faced its share of criticism. Critics point out that cost-benefit calculations can neglect important factors that aren’t included in the analysis. In addition, they argue that the most effective institutions for social change usually aren’t charities but rather public services. The economist Daron Acemoglu points out that deemphasizing these public services in favor of charities can erode trust in governments and discourage participation in politics which can exacerbate these problems.

Furthermore, while EA concerns itself with optimizing the task of giving it does not explicitly address the systemic causes underlying problems. For instance, if I were to donate to a charity to alleviate poverty, I may find that I have caused positive changes in the world but it’s unlikely I’ve addressed the underlying causes of that poverty. This issue is further compounded when attempting to give money at scale. When large amounts of money are donated to a particular cause, such as anti-malaria mosquito nets, there are diminishing returns to that intervention since eventually everyone who can be helped by one already has one.

EAs respond to this by pointing out that they do promote systemic change via politics in numerous ways. They would also say that the choice between politics and charity is a false dichotomy and that one is capable of pursuing both. They also point out that people frequently cannot participate in politics if their basic needs aren’t being met so charity still makes sense in conjunction with political activity.  

Effective Altruism is also criticized for its foundations in utilitarianism which has been said to lead to repugnant conclusions when taken to its logical conclusion. For instance, many people who would sacrifice the lives of 5 people to save one person as in the famous trolley problem. However, consider another scenario. Suppose instead that a doctor has 5 patients each suffering from different forms of organ failure. Now suppose he has a patient visit him who is a perfect match for each of the sick patients. Would it be ethical for the doctor to kill this patient, steal his organs, and transplant them into the sick people? It’s the same scenario as the trolley problem but most people would not believe this was ethical, suggesting that they cannot totally accept utilitarianism.

In response, utilitarians state that this scenario is not actually the best way to increase overall happiness which is the goal of the philosophy. Returning to the example, if people found out that this was happening in a hospital then people would be too afraid to seek medical care and many more people would suffer which would reduce overall wellbeing.  

This leads to a final criticism, that EA is paternalistic. The philosopher and EA advocate Will MacAskill criticized one particular charity which simply distributed its donations directly to the beneficiaries in the form of cash. MacAskill criticized this effort saying that direct cash transfers would be less efficient than spending on public health interventions. This assumes that regular people don’t know what’s best for them and require the guidance of experts.

However, this criticism is false. MacAskills statement is not an objective measurement and therefore would not be considered as part of the approach of effective altruism. In fact, there is much quantitative evidence that direct cash donations are an effective way to help poor people.

Conclusion

In summary, effective altruism provides a compelling framework for thinking about ethical issues. Scarce resources should be prioritized to the highest value altruistic acts as determined by quantitative measures of their benefit. Causes favoring those in developing countries should be prioritized since some of these tend to get neglected and charitable donations can accomplish more good there than in developed countries. Existential risks also deserve special consideration even if their likelihood is low.

With the recent revolution in AI, there has been a renewed focus on existential risk. This approach is sometimes called Longtermism and advocates an almost exclusive focus on the wellbeing of those in the future since they comprise almost all humans who will ever exist. This approach is controversial. Critics say that this leads to neglect of ethical concerns happening now since they don’t threaten the existence of humanity.  Funding has also become abundant in recent years so there is some concern that this disincentivizes rigor in determining the best causes. Finally, the crypto billionaire and alleged financial criminal Sam Bankman-Fried was an enormous advocate for EA and the collapse of his financial empire has somewhat damaged the reputation of the movement. Nevertheless, it still appears to be going strong and will have an impact for the foreseeable future.

References

·        https://econreview.berkeley.edu/do-better-but-empirically-how-effective-altruism-attempts-to-optimize-our-generosity/

·        https://erikhoel.substack.com/p/why-i-am-not-an-effective-altruist

·        https://www.abc.net.au/religion/why-effective-altruism-is-not-effective/13310708

·        https://www.givingwhatwecan.org/blog/dont-we-need-political-action-rather-than-charity

·        https://80000hours.org/2022/05/ea-and-the-current-funding-situation/

·        https://www.concernusa.org/story/cash-transfers-explained/

Q1. Are they clear categories, or a continuum?

What is the current state of the technology?

Is there a line to be drawn? Where?

Answer:

I think its obviously a continuum. Lots of military weapons have computer chips in them that guide their behavior. Right now its very sophisticated and we should only expect it to get better. I think we can draw a line where weapons don’t require any human oversight at all.

Q2. Do you think lethal fully autonomous robots should be banned?

Answer:

I don’t really see the point. Dead is dead regardless of how it happens. However, there should be some sort of ethical system guiding them. We don’t need robots committing war crimes.

Q3.  The ethics argument

“… we should not allow machines to make life-taking decisions for which others – or nobody – will be culpable.”

2. The pragmatic argument

“… lethal autonomous weapons, selecting and engaging targets without human intervention, would be dangerously destabilizing for every country and individual.”

What is your take on these arguments?

Answer:

I think the ethics argument is a bit hollow, quite often no one is held culpable for bad decisions made during a war. I think the pragmatic argument misses an important point about combat robots. Whichever country implements them is going to have an enormous advantage over those that don’t. This is going to create powerful incentives for others to develop them as well. So, I think we will have them at some point.

Q4. Is a mine or booby trap a military robot?

Answer:

I don’t think so. I think something has to move through the physical world in order to be considered a robot.

Q5. What are the ethics of each of these?

Answer:

I think these should be carefully deployed away from civilians. There should also be some kind of effort to dispose of them after the fighting.

Q6. Should killer robots be banned also?

Answer:

Again, I don’t think they will be. The advantages for using them would be too great.

Q7. What about autonomous military vehicles?

Personnel transport vehicles

Is human override ethically required?

Answer:

Someone should have the capability to override the vehicle for pure safety reasons. It would be like an autonomous car that was impossible for the user to control.

Q8. Tanks

Planes (i.e. drones)

Armed and personnel carrying

Ships

Submarines

Is human override ethically required?

Any differences?

Answer: Once again, I think for pure pragmatism you need to have any robotically controlled vehicle to have some sort of override for pure safety reasons.

Q9. What about military rescue operations?

Are there ethical considerations in automated rescue?

Deciding who to evacuate or treat first:

Is this an ethical issue or a software design issue?

Is this the same as civilian rescue operations?

Answer: I think there are ethical issues around this. Situations like those may require difficult decisions to be made so they should probably be guided by some sort of ethics of triage.

Q10. Robot ethics can be categorized as

Operational ethics

Everything is pre-programmed

Ethical agency

Robot needs to reason from principles

     Is one of these harder than the other?

Answer:

Reasoning from first principles is probably harder. You would theoretically have to cover a lot of ambiguous cases which would be difficult for the programmers.

Q11. One expert claims military robots can be more ethical than humans

Can you think of an argument for that?

Or a counterargument?

Answer:

I think its true. Its always possible for a human soldier to display unethical behavior, you can never really be sure. Robot soldiers would have their ethics designed into them so you know there could be no deviation. However, if the rules weren’t carefully designed then edge cases might still cause them to behave unethically.

1. Explain how you used ChatGPT or other AI tool(s) to help for this HW’s update to your project, or note if it wasn’t used.
2. Continue to develop the project, as we build it step by step over the semester so that it will be manageable rather than a crunch at the end, as follows. Write up another 350 words or more (per person if a group project) of new content if your project is a paper that must be written from scratch (for example if it is about yourself). If you are using an automatic text generator to help write it it would probably be quite a bit more than that amount of words. If the progress was code development or website creation, give the new code. If slides, paste the new text from the new slides into your blog and describe any images or graphics. If something else, explain specifically what you did, being sure to give examples if that makes sense. Label this HW consistently as in this example. List any sources you used, such as websites or pages, ChatGPT or other tools, books, or whatever it might be. This takes some effort and it counts as part of your work. If your project is not a paper, explain what you did on your blog. For team projects, focus on your own activities although you can also discuss the overall effort to provide some context. Explain and give evidence (for example, if a web site, you could provide a link to it; if software, give the code; if a skit, give some of the script or list rehearsal or meeting times; if artwork, provide an image showing its status, etc.).  If you’re not sure what to do, see me or send me an email and I will try to suggest something.

Causes

Effective altruism advocates prioritization of charity towards goals that can be demonstrated to be the most effective. Given that goal, it’s unsurprising that much of the effort is directed toward the developing world, especially pertaining to health. Some of these causes include deworming, water sanitation, malaria nets, and vitamin supplementation.

Parasitic worm infections are a serious problem affecting roughly 835 million children worldwide. These infections can interfere with nutrient uptake and adversely impair their physical and mental development. Deworm the World is one of the top deworming charities that has delivered over 1.5 billion treatments globally at a cost of about 50 cents per treatment although they are delivered for free to affected children. As such a tremendous amount of effective altruist advocacy and support has been directed towards them.

One of the top ways to prevent the spread of Malaria is with insecticide treated mosquito nets. Malaria nets can often be somewhat costly and there are challenges with getting them to people, but they have been shown to significantly prevent the likelihood of malaria infection. Malaria can lead to reduced IQs and poorer health as an adult, so prevention of malaria has been shown to lead to better health as an adult and better economic outcomes. Overall, its been shown to be highly cost effective and so is probably the cause most synonymous with effective altruism.

Finally, there is water sanitation which is another major problem for the developing world. A cost-benefit analysis of water and sanitation interventions indicates that some interventions are highly cost-effective for the control of diarrhea among under-5-year-olds, on par with oral rehydration therapy. The benefits of the interventions include gain in productive time and reduced health care costs saved due to less illness and prevented deaths. The results show that all water and sanitation improvements are cost-beneficial in all developing world sub-regions.

AI and Existential Risk

One other area of focus for EA concerns what is known as Existential Risk. Existential risks are those risks that threaten human survival. As mentioned earlier, if we look at existing theories of expected value then existential risks are highly important concerns ethically even if their likelihood is small because their impact would be so large. One category of existential risk that is especially interesting is risk from rogue AI. Essentially, if an AI could gain the ability to improve its own cognitive abilities, then it could quickly move to a state beyond human comprehension, an idea known as the Singularity. If these AI do not share human values, then it is very likely they could do something that would be against human interests and therefore threaten human survival. The problem of how to give it human values and make it safe is known as the alignment problem.

AI alignment is a large subject that consists of several different issues. The first is known as the black box problem. Essentially, we can easily specify the inputs and the outputs of an AI system, but we don’t know the process for moving from one to another since it so complicated and not explicitly programmed. This means we can’t evaluate the system and determine where it is potentially going wrong. Solving this problem will be crucial to evaluating the safety of specific AI models.

Another issue is the concern of consistency. Wixhom et al. identify three consistency concerns for AI alignment, these are consistency of the AI with reality, the consistency of the AI solution with the model, and the consistency of the AI with stakeholder needs.

Honesty is another interesting issue. AI researchers distinguish between “truthfulness” and “honesty”. Truthfulness refers to the AI making objectively true statements whereas honesty refers to the AI stating what it believes to be true. Unfortunately, research indicates that AI hold no stable beliefs so it may not be possible to evaluate honesty.

Auditing AI models is also an important part of AI Alignment. This involves ensuring that AI models are behaving as intended. However, it’s difficult for humans to evaluate the behavior of AI when the system outperforms the human in a given area. This problem is known as scalable oversight. Examples of this include summarizing books, writing secure and defect-free code, and predicting long term outcomes. This problem has led to some AI systems attempting to deceive researchers into thinking that the measured task was achieved.

Finally, it’s worth considering tackling the problem of emergent behaviors. AI are governed by complex rules that can lead to behavior that wasn’t explicitly programmed. Countering harmful behavior that could emerge spontaneously is therefore an important concern. An example of this is power seeking behavior. This occurs when AI attempt to gain control over their environment in an attempt to find the most optimal way to accomplish its task. Preventing power seeking behavior is going to be extremely important as AI become progressively more powerful.

Overall, the task of AI alignment requires us to be very specific about what human's value. So not only must we solve ethical issues, but we must be so specific about the solution that we can express it as computer code. So, at least in the context of AI, ethics must ultimately become a branch of engineering and not just philosophy. Effective Altruism argues that this is an extremely important task to focus on since it may determine the future of billions of lives. 

References

·       https://pubmed.ncbi.nlm.nih.gov/10191558/

·        https://www.academia.edu/5494398/Global_cost_benefit_analysis_of_water_supply_and_sanitation

·        https://effectivealtruism.nz/deworm-the-world/

·        https://www.evidenceaction.org/dewormtheworld/

·        Cognitive performance of children living in endemic areas for Plasmodium vivax | Malaria Journal | Full Text (biomedcentral.com)

·        https://www.effectivealtruism.org/articles/ea-global-2018-amf-rob-mather

·        https://existential-risk.org/concept.pdf

·        https://en.wikipedia.org/wiki/Existential_risk_from_artificial_general_intelligence

·        https://en.wikipedia.org/wiki/AI_alignment

·        https://en.wikipedia.org/wiki/The_Alignment_Problem

·        https://cisr.mit.edu/publication/2020_1101_AI-Alignment_WixomSomehGregory

·        https://spyscape.com/article/do-ais-seek-power

Please discuss the video with your group, if you have one, based on this form. You can hand it in at the end of class, or put your answers to Qs 1 & 2 in your blog – use the title ” ‘Last Lecture’ Discussion Questions.” (If late, email it to jdberleant@ualr.edu.)

Your name (in-class students only; online students leave it blank):_____________________________

1. The video was intended as life advice to whom?

Answer:

It was intended for his children

2. List the advice items that you/your group can recall below. (Many, but not all, are related to ethics.) For each, note whether you agree or not.

1.)    If you do anything pioneering you will get arrows in the back: Yes, I agree. From my experience it seems like most pioneers do get attacked before they receive any recognition.

2.)    The best gift an educator can give someone is to make them self reflective: I’m not sure I agree. It’s certainly valuable but the best gift they can give them is a deep understanding of the subject.

3.)    Respect authority while questioning it: Yes, I agree. Nothing wrong with respecting people who know more than you.

4.)    Decide if you’re tigger or eyeore: I don’t really agree. You can’t decide that kind of orientation. Either you are one way or another.

5.)    Never lose the childlike wonder: It would be good to never lose it but again I don’t think that’s something someone can control.

6.)    Help others: Definitely can’t argue with that. Helping others is usually a good idea.

7.)    Loyalty is a two way street:  I would say loyalty “should” be a two way street. It’s no guarantee that the other person will be loyal.

8.)    Never give up: I definitely agree. It’s a requirement for any kind of achievement.

9.)    Focus on others and not yourself: Yes, I agree. Life is more rewarding that way.

10.) Brick walls let us show our dedication: This is a great way to reframe difficulties so yes I agree with it.

Main Ideas

As mentioned earlier, effective altruism states that we should utilize reason to figure out how to do the most good with the resources we have. A contrary attitude many people share is to donate resources to causes they feel passionate about regardless of how much good it may do objectively. This approach can lead to a lot of missed opportunity to do good. An example from the philosopher Peter Singer may help to illustrate this point. Suppose we are concerned about helping people afflicted with blindness. One possible way is with seeing eye dogs. These cost approximately $40,000 to train. However, there is a disease called trachoma which blinds people most often in developing countries. This disease can be prevented with a simple surgery that costs between$20-$50. If we just assume these are the only ways to help people with blindness then we have the choice between helping one person for $40,000 or helping between 800-2000 people for the same amount of money. Effective altruists would argue that we should focus more on helping people with trachoma since this leads to more people being helped.

This concept of prioritization is the most important, but others are worth enumerating. Effective altruists care about all people equally, not just those in their own country or culture. This is the main reason why they so often focus on the developing world. They are also open to any cause which has the potential to be benefit, for instance they are often concerned with AI safety which will be discussed later. Finally, while effective altruism is frequently concerned with charitable donations it is open to examining other kinds of assistance such as volunteering time.

In order to measure the effectiveness of charitable interventions, effective altruism advocates the use of various quantitative measures such as:

·        Cost effectiveness estimates: These cover how efficient certain interventions are given a certain amount of money. In the previous example, this would be how many cases of blindness from trachoma could be prevented for each dollar invested.

·        Disability-adjusted life years (DALYs): This measures the burden of disease on health. One DALY represents a year of life lost to illness  or disability.

·        Quality adjusted life years (QALYs): This measures the value of each year of healthy life. One QALY represents one year in perfect health.

These measures can be used in conjunction with another tool called decision theory. Decision theory evaluates different choices by examined expected utility which is the utility of an outcome multiplied by the probability of that outcome. In this case, utility might be measured in DALYs or QALYs. We then simply make the choice with the highest expected utility. One interesting consequence of this approach is that it provides a way to make the utilitarian perspective in ethics mathematical, at least in theory. There are still difficulties with this approach such as how to measure utility, how to aggregate utility across individuals, or how to deal with ignorance and bias.

References:

·        https://www.ted.com/talks/peter_singer_the_why_and_how_of_effective_altruism/transcript?language=en

·        https://en.wikipedia.org/wiki/Disability-adjusted_life_year#:~:text=The%20disability%2Dadjusted%20life%20year,life%20expectancy%20of%20different%20countries.

·        https://en.wikipedia.org/wiki/Quality-adjusted_life_year

·        https://plato.stanford.edu/entries/rationality-normative-utility/

·        https://en.wikipedia.org/wiki/Expected_utility_hypothesis

Code of ethics for banking

1.       Transparency: It is essential for those in the banking industry to promote transparency by disclosing all relevant information about their products and services to their valued customers.

2.       Fairness: Banking professionals should treat their customers with fairness and equity, without any discrimination based on race, gender, socioeconomic status, or any other characteristics.

3.       Privacy and Security: The privacy and security of customers should be safeguarded by those in the banking industry by adopting robust security policies, adhering to relevant data protection laws, and ensuring that their customers' sensitive information remains confidential.

4.       Responsiveness: Banking professionals should be responsive to their customers' concerns and proactive in anticipating their needs. They should be courteous and respectful while dealing with them and take prompt action to address their issues.

5.       Integrity: Banking professionals must uphold the highest standards of professionalism and honesty. They should follow ethical practices such as disclosing conflicts of interest, avoiding bribes, and maintaining accurate records of all financial transactions.

6.       Social Responsibility: Banking professionals should recognize that they are part of a broader community and take action to improve it. They can do this by promoting financial literacy, supporting financial inclusion, and investing in socially responsible initiatives.

7.       Innovation: Bankers should embrace innovation and adapt to emerging technologies. They should explore new ideas that enhance their customers' experience and improve efficiency within the banking industry.

My case is derived from a scenario in the interactive video “The Lab”. I played as Aaron Hutchins, a Principal Investigator in a lab who is supervising several scientists. He has to find time to mentor his scientists while handling his research, teaching, and personal life. He discovers that one of his postdocs, Greg, has committed research fraud and he has to find a way to handle the accusation.

Analysis:

1.)     Greg didn’t set out to produce fraudulent research at first, he simply justified cutting corners when things got tough. Is this usually how unethical behavior starts? Do people just want to take the easy way out?

Answer: Yes, I think this is how unethical behavior frequently starts. I think most people want to think of themselves as good people and so don’t start out deliberately planning to do something wrong. I think its more likely that they rationalize what would be beneficial to themselves in the short term when they get tempted.

2.) Aaron helped his scientists act correctly in his lab by taking the time to mentor them. Do personal relationships with good people help us to remain ethical?

Answer: Yes, there is evidence that personal relationships can shape how we act under certain circumstances. They can help us gain feedback about our personal perceptions but also just act as a source of strength when things get tough. In the video we don’t see Aaron speaking with Greg much about his problems. It’s possible that might’ve contributed to Greg’s fraud.

3.) Scientists are under a lot of pressure to publish frequently in order to further their careers which may pressure them to behave unethically. Is a better system possible?

Answer: There probably is but I’m not aware of what the answer might be. There is a vibrant field called Metascience which asks how can we find better ways to do science. If we could somehow come up with a metric to measure research quality then that might be a better way to evaluate scientists than looking at the number of publications.

4.)  Aaron tried to foster a culture of openness and transparency in his lab in order to prevent problems from occurring. Can fostering this culture in other kinds of organizations prevent security problems from occurring?

Answer: I think so. The book “Engineering Trustworthy Systems” by O. Sami Saydjari speaks a lot about this topic He specifically calls out the harms that can come from keeping secrets and how secrecy can foster unethical behavior and impair security.

My conclusions:

While this scenario is fictional I think it provides a great illustration about the importance of culture in an organization and the kinds of working relationships people can develop. By staying involved in what was going on with his scientists he helped them to act correctly when they encountered difficulties. While the fraud Greg committed was bad he identified it before the damage could’ve gotten much worse. This kind of openness can be beneficial for security because it lets people identify problems in an organization much more easily.

Future environment:

Right now, the AI system ChatGPT has made a big impact for anyone involved in knowledge work. There is evidence that it will have an especially big impact for researchers. Over time its likely that it will get better and enable more aspects of research to be automated. While this would vastly improve productivity and enhance scientific discovery it might also lead to scientists taking credit for work they did not do.

Future scenario:

It’s likely that AI will be much more productive at scientific research that the scientists themselves. How then should we measure the effectiveness of researchers? This probably doesn’t seem serious to those outside the field but this is an important question for how governments and universities choose to allocate scarce resources for funding research. We will need to find some new ways to evaluate scientists and figure out new ways to support them. Generative AI systems will likely be able to generate more sophisticated fraud so it will probably be more difficult to identify misconduct as well. 

• A link or other citation to the case you are using, or if it is from personal experience, point that out.
• A list of 8 or more important facts about the case, in your own words. You can refer to these as reminders when you tell your group members about the case.
• A list of questions (3 or more) you could ask your group members in order to get an interesting and enlightening discussion going (for in-class students), or that you could consider yourself or ask someone else about (for online students); see the “Questions to ask during discussion” tab on the course web page for some suggestions in developing your discussion questions.
• A 4th discussion question about how computer security relates to or could relate to the case. The computer security question could be about hacking, viruses or worms, theft of information, piracy, abuse of privileges, destruction of assets, information privacy, disruption of operations, unauthorized access, corporate abuse of information or computing, government abuse of information, physical harm, or any other issue in the general area of computer security.

Answer: My source is https://ori.hhs.gov/TheLab/TheLab.shtml                                                                            

Eight important facts are:

1.)     This case is fictional, and I played the role of Aaron Hutchins, the PI in a lab which has made several recent high profile discoveries.

2.)     Being the PI of a lab is hectic and Aaron frequently has to juggle all the responsibilities that come with it.

3.)     Aaron frequently has grad students interrupt him by asking for help when he’s in the middle of important work.

4.)     In this scenario, he has the option of brushing off these requests or taking time to try to resolve them.

5.)     One of Aaron’s postdoc’s Greg, has been having a lot of positive results with his experiments recently and Aaron has been pretty happy about that.

6.)     One of his grad students, Kim comes to him with discrepancies between Greg’s paper and what was actually recorded in the lab. She doesn’t want to accuse Greg of misconduct but feels obligated to report this problem.

7.)     Aaron has the option to handle this matter himself or report it to the Research Integrity Officer (RIO).

8.)     It turns out Greg was actually producing fraudulent research. Although this negatively impacts his lab it would have been far more damaging had he not had Kim’s accusations investigated.

Three questions to ask about the case:

1.)     Greg didn’t set out to produce fraudulent research at first, he simply justified cutting corners when things got tough. Is this usually how unethical behavior starts? Do people just want to take the easy way out?

2.)     Greg helped his scientists act correctly in his lab by taking the time to mentor them. Do personal relationships with good people help us to remain ethical?

3.)     Scientists are under a lot of pressure to publish frequently in order to further their careers which may pressure them to behave unethically. Is a better system possible?

A fourth question related to computer security:

4.)     Aaron tried to foster a culture of openness and transparency in his lab in order to prevent problems from occurring. Can fostering this culture in other kinds of organizations prevent security problems from occurring?

Three additional standard questions:

1.)     What does virtue ethics say about this case?

Answer: I think virtue ethics really highlights the role of personal qualities in producing ethical behavior. Aaron takes the time to mentor his scientists which helps them behave correctly but also identify misconduct when it occurred. In the case with Greg, his personal qualities also led him to cut corners rather than work through his experiments problems.

2.)     What does utilitarianism say about this case?

Answer: There was definitely a cost/benefit analysis to be made by some of the people involved. Kim was aware that she was ruining Greg’s career by coming to Aaron with what she knew. But she also understood that the more important good was the integrity of the lab’s research. Utilitarianism views this as ethical even though this decision ended up harming Greg since a more important outcome was obtained.

3.)     What does deontology say about this case?

Answer: Deontology probably makes the simplest and most direct statement on this case. Greg lied about his research results and lying is wrong. Every ethical system in history views lying as wrong. Furthermore, Kim had the choice of being honest about what she knew or lying by omission. I think Deontology would view Kim’s actions as ethical since she was behaving according to the rule of honesty.

1.  I played as the postdoc Rao.
2.  I chose to ask for clarification when the PI asked if I was doing something wrong.
3. When Rao’s wife asked him to come home from the lab, I decided to not skip the time point.
4. When asked to go to the RCR training I decided not to go.
5. I chose not to get some food and kept working on my experiment.
6. When Rao was asked to join his wife for dinner with her parents, I decided to perform the cell harvest on time and join them later.
7. I decided to go to the lab in time to do the harvest.
8. When given the chance to go to the RCR training again I decided to take it.
9. I like how this example demonstrates how to successfully navigate all the difficulties associated with being a postdoc.