Summary of Lecture 09: Ethics (FSDL 2022)

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00:00:00 - 01:00:00

This lecture covers a range of ethical concerns that can arise when designing or using technology. It discusses how to deal with these concerns using case-based reasoning and the principles of engineering. The lecture also provides examples of recent scandals in the technology industry, and points out the importance of maintaining public trust.

  • 00:00:00 Ethics is the study of morality, and in this lecture, professor discusses a case-based approach to ethics and how it can help illuminate moral truths. He also discusses how social biases can be a risk when relying on prominent cases to make ethical decisions.
  • 00:05:00 In this lecture, the speaker discusses the various ethical issues that can arise in situations involving conflict between what we want and what we get, as well as the problem of alignment. He discusses how to deal with these issues using case-based reasoning and the principles of engineering.
  • 00:10:00 This lecture discusses the ethical implications of technology, focusing on ways in which technology companies can improve their professionalism. The speaker provides examples of recent scandals in the technology industry, and points out the importance of maintaining public trust. He urges listeners to learn from other industries in order to avoid similar problems in the technology industry.
  • 00:15:00 In this lecture, Professor Fields discusses two areas of ethics - one pertaining to the culture of professional ethics in engineering in Canada, and the other pertaining to ethical standards for human subjects research. Field discusses the development of rituals and oaths taken by Canadian engineers upon completion of their education, and points out that these practices are meant to impress upon the engineers the weight of their responsibility. One of the major concerns raised with regard to engineering is the impact of anthropogenic climate change on CO2 emissions, and Field notes that we currently lack future generations who can advocate for themselves. The second area of ethical concern discussed is user-hostile designs in technology.
  • 00:20:00 The lecture discusses the ethical concerns of carbon dioxide emissions, deceptive design, and cloud computing. It points out that power is not always free, and that tracking compute spend and carbon dioxide emissions is a way to measure and manage these concerns. The lecture also provides a history of Google's advertising and points out how deceptive design has become a problem in the last decade.
  • 00:25:00 In this lecture, Professor John McMullen discusses ethics in technology design. He points out that many technology design practices are on shaky ethical and legal ground, including growth hacking. He suggests that designers learn from other disciplines in order to avoid a trust crisis.
  • 00:30:00 Ethics are important when designing or using machine learning systems, as machine learning involves being wrong pretty much all the time. There are several common ethical concerns that have arisen in the last couple of years, and which can be summed up as follows: Does the model being created reflect the true nature of the data being used? Is the system transparent and accountable so that anyone affected by it can understand it? Is the system used only if it is necessary and does not cause harm or unfairness.
  • 00:35:00 This lecture discusses the ethics of using a binary risk assessment algorithm, such as Compass, which can result in unfair treatment of certain groups. The algorithm is proprietary and uninterpretable, and does not provide answers for why a person is higher risk or not. This makes it difficult to understand and justify the algorithm's conclusions. Furthermore, because the algorithm is used to make decisions about people's lives, it is important to critically consider whether it should be built at all.
  • 00:40:00 A system that can predict whether someone will fail to appear in court has an accuracy of about 65 percent. This system is easier to feel comfortable with than a system that just says "if you've been arrested twice then you have a higher risk of being arrested again."
  • 00:45:00 The video discusses ethics and how to reduce bias in Discovery processes. It discusses how human rights might be at stake when decisions are made using automated systems. Various methods for introspecting deep neural networks are discussed, with Smooth Grad being particularly promising.
  • 00:50:00 This lecture discusses ways in which deep neural networks can be unaccountable, and how to build systems that are accountable. It also discusses concerns around the use of machine learning, and how data is often acquired illegally.
  • 00:55:00 This lecture covers the ethics of using artificial intelligence (AI) in art, with a focus on weapon development. It discusses the potential for autonomous weaponry to cause harm, and argues that the development of such technology should be halted until more is known about its long-term effects.

01:00:00 - 01:20:00

This lecture by Dr. Michael Lempert discusses the ethical concerns surrounding artificial intelligence, with a focus on machine learning. He highlights the importance of education and keeping in mind the potential consequences of our work when developing these technologies. The lecture finishes with a message encouraging people to use machine learning for good.

  • 01:00:00 The lecture discusses the ethics of autonomous weapons, and the benefits and challenges of researching and building such systems. It points out that, while machine learning does have some potential for improving medical care, much of the research on covid-19 was ineffective and lacked validation. It also notes that, in the context of a pandemic, medical professionals have a strong ethical foundation that can handle difficult problems.
  • 01:05:00 In this lecture, Dr. Michael Lempert discusses the importance of ethics in machine learning, focusing on the intersection of medicine and machine learning. He also highlights recent work in this area, including a review of clinical trials involving machine learning and the application of an algorithmic auditing framework.
  • 01:10:00 The speaker discusses the ethical implications of artificial intelligence, focusing on the potential for misuse of technology. He recommends separating out the areas where AI has made rapid progress from those where there is still much to be done.
  • 01:15:00 This lecture discusses the ethical concerns surrounding artificial intelligence, with a focus on machine learning. The main points raised are that there is disagreement about how far away we are from creating self-improving AI, and that there are concerns about what might happen if a self-improving AI becomes more intelligent than humans. The lecture ends with a discussion of the ethical implications of effective altruism, a movement focused on doing the most good with what we have.
  • 01:20:00 The lecture discusses ethics in the context of machine learning and artificial intelligence, highlighting the importance of education and keeping in mind the potential consequences of our work. The lecture finishes with a message encouraging people to use machine learning for good.

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