The ethics of autonomous cars and automated driving have been a subject of discussion in research for a number of years :28–58, 2016). As levels of automation progress, with partially automated driving already becoming standard in new cars from a number of manufacturers, the question of ethical and legal standards becomes virulent. For exam-ple, while automated and autonomous cars, being equipped with appropriate detection sensors, processors, and intelligent mapping material, have a chance of being much safer than human-driven cars in (...) many regards, situations will arise in which accidents cannot be completely avoided. Such situations will have to be dealt with when programming the software of these vehicles. In several instances, internationally, regulations have been passed, based on legal considerations of road safety, mostly. However, to date, there have been few, if any, cases of a broader ethics code for autonomous or automated driving preceding actual regulation and being based on a broadly composed ethics committee of independent experts. In July 2016, the German Federal Minister of Transport and Digital Infrastructure, Alexander Dobrindt, appointed a national ethics committee for automated and connected driving, which began its work in September 2016. In June 2017, this committee presented a code of ethics which was published in German and in English. It consists of 20 ethical guidelines. Having been a member of this committee, I will present the main ethical topics of these guidelines and the discussions that lay behind them. (shrink)

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To (...) achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’. (shrink)

The following values have no corresponding Zotero field: PB - JSTOR.

One of the reasons why most of us feel puzzled about the problem of abortion is that we want, and do not want, to allow to the unborn child the rights that belong to adults and children. When we think of a baby about to be born it seems absurd to think that the next few minutes or even hours could make so radical a difference to its status; yet as we go back in the life of the fetus we (...) are more and more reluctant to say that this is a human being and must be treated as such. No doubt this is the deepest source of our dilemma, but it is not the only one. For we are also confused about the general question of what we may and may not do where the interests of human beings conflict. We have strong intuitions about certain cases; saying, for instance, that it is all right to raise the level of education in our country, though statistics allow us to predict that a rise in the suicide rate will follow, while it is not all right to kill the feeble-minded to aid cancer research. It is not easy, however, to see the principles involved, and one way of throwing light on the abortion issue will be by setting up parallels involving adults or children once born. So we will be able to isolate the “equal rights” issue and should be able to make some advance... (shrink)

In the international bestseller, Thinking, Fast and Slow, Daniel Kahneman, the renowned psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive (...) biases on everything from playing the stock market to planning our next vacation—each of these can be understood only by knowing how the two systems shape our judgments and decisions. -/- Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives—and how we can use different techniques to guard against the mental glitches that often get us into trouble. Winner of the National Academy of Sciences Best Book Award and the Los Angeles Times Book Prize and selected by The New York Times Book Review as one of the ten best books of 2011, Thinking, Fast and Slow is destined to be a classic. (shrink)

Trolley cases are widely considered central to the ethics of autonomous vehicles. We caution against this by identifying four problems. Trolley cases, given technical limitations, rest on assumptions that are in tension with one another. Furthermore, trolley cases illuminate only a limited range of ethical issues insofar as they cohere with a certain design framework. Furthermore, trolley cases seem to demand a moral answer when a political answer is called for. Finally, trolley cases might be epistemically problematic in several ways. (...) To put forward a positive proposal, we illustrate how ethical challenges arise from mundane driving situations. We argue that mundane situations are relevant because of the specificity they require and the scale they exhibit. We then illustrate some of the ethical challenges arising from optimizing for safety, balancing safety with other values such as mobility, and adjusting to incentives of legal frameworks. (shrink)

Self-driving cars hold out the promise of being safer than manually driven cars. Yet they cannot be a 100 % safe. Collisions are sometimes unavoidable. So self-driving cars need to be programmed for how they should respond to scenarios where collisions are highly likely or unavoidable. The accident-scenarios self-driving cars might face have recently been likened to the key examples and dilemmas associated with the trolley problem. In this article, we critically examine this tempting analogy. We identify three important ways (...) in which the ethics of accident-algorithms for self-driving cars and the philosophy of the trolley problem differ from each other. These concern: the basic decision-making situation faced by those who decide how self-driving cars should be programmed to deal with accidents; moral and legal responsibility; and decision-making in the face of risks and uncertainty. In discussing these three areas of disanalogy, we isolate and identify a number of basic issues and complexities that arise within the ethics of the programming of self-driving cars. (shrink)

Self-driving cars currently face a lot of technological problems that need to be solved before the cars can be widely used. However, they also face ethical problems, among which the question of crash-optimization algorithms is most prominently discussed. Reviewing current debates about whether we should use the ethics of the Trolley Dilemma as a guide towards designing self-driving cars will provide us with insights about what exactly ethical research does. It will result in the view that although we need the (...) ethics of the Trolley Dilemma as important input for self-driving cars, the route towards simply implementing it into automated cars is blocked. (shrink)