Machine Ethics: Do Androids Dream of Being Good People?

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely "following a moral code". In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society.

Educational Encounters of the Third Kind.

An engineer who becomes an educator in a school of software engineering has the mission to teach how to design and construct software systems, therein applying his or her knowledge and expertise. However, due to their engineering background, engineers may forget that educating a person is not the same as designing a machine, since a machine has a well-defined goal, whilst a person is capable to self-propose his or her own objectives. The ethical implications are clear: educating a free person must leave space for creativity and self-determination in his or her own discovery of the way towards personal and professional fulfillment, which cannot consist only in achieving goals selected by others. We present here an argument that is applicable to most fields of engineering. However, the dis-analogy between educating students and programming robots may have a particular appeal to software engineers and computer scientists. We think the consideration of three different stages in the educational process may be useful to engineers when they act as educators. We claim that the three stages (instructing, training and mentoring) are essential to engineering education. In particular, education is incomplete if the third stage is not reached. Moreover, mentoring (the third stage aimed at developing creativity and self-determination) is incompatible with an educational assessment framework that considers the goals of the engineer are always given by others. In our view, then, an integral education is not only beyond programming the behavior of students, but also beyond having them reach those given goals.

Teaching Ethics to Engineers: A Socratic Experience.

In this paper we present the authors' experience of teaching a course in Ethics for Engineers, which has been delivered four times in three different universities in Spain and Chile. We begin by presenting the material context of the course (its place within the university program, the number of students attending, its duration, etc.), and especially the intellectual background of the participating students, in terms of their previous understanding of philosophy in general, and of ethics in particular. Next we set out the objectives of the course and the main topics addressed, as well as the methodology and teaching resources employed to have students achieve a genuine philosophical reflection on the ethical aspects of the profession, starting from their own mindset as engineers. Finally we offer some results based on opinion surveys of the students, as well as a more personal assessment by the authors, recapitulating the most significant achievements of the course and indicating its underlying Socratic structure.

Detection of ischemic penumbra using combined perfusion and T2* oxygen challenge imaging.

BACKGROUND: Acute ischemic stroke is common and disabling, but there remains a paucity of acute treatment options and available treatment (thrombolysis) is underutilized. Advanced brain imaging, designed to identify viable hypoperfused tissue (penumbra), could target treatment to a wider population. Existing magnetic resonance imaging and computed tomography-based technologies are not widely used pending validation in ongoing clinical trials. T2* oxygen challenge magnetic resonance imaging, by providing a more direct readout of tissue viability, has the potential to identify more patients likely to benefit from thrombolysis - irrespective of time from stroke onset - and patients within and beyond the 4·5 h thrombolysis treatment window who are unlikely to benefit and are at an increased risk of hemorrhage. AIMS: This study employs serial multimodal imaging and voxel-based analysis to develop optimal data processing for T2* oxygen challenge penumbra assessment. Tissue in the ischemic hemisphere is compartmentalized into penumbra, ischemic core, or normal using T2* oxygen challenge (single threshold) or T2* oxygen challenge plus cerebral blood flow (dual threshold) data. Penumbra defined by perfusion imaging/apparent diffusion coefficient mismatch (dual threshold) is included for comparison. METHODS: Permanent middle cerebral artery occlusion was induced in male Sprague-Dawley rats (n = 6) prior to serial multimodal imaging: T2* oxygen challenge, diffusion-weighted and perfusion imaging (cerebral blood flow using arterial spin labeling). RESULTS: Across the different methods evaluated, T2* oxygen challenge combined with perfusion imaging most closely predicted 24 h infarct volume. Penumbra volume declined from one to four-hours post-stroke: mean ± SD, 77 ± 44 to 49 ± 37 mm(3) (single T2* oxygen challenge-based threshold); 55 ± 41 to 37 ± 12 mm(3) (dual T2* oxygen challenge/cerebral blood flow); 84 ± 64 to 42 ± 18 mm(3) (dual cerebral blood flow/apparent diffusion coefficient), as ischemic core grew: 155 ± 37 to 211 ± 36 mm(3) (single apparent diffusion coefficient threshold); 178 ± 56 to 205 ± 33 mm(3) (dual T2* oxygen challenge/cerebral blood flow); 139 ± 30 to 168 ± 38 mm(3) (dual cerebral blood flow/apparent diffusion coefficient). There was evidence of further lesion growth beyond four-hours (T2-defined edema-corrected infarct, 231 ± 19 mm(3) ). CONCLUSIONS: In conclusion, T2* oxygen challenge combined with perfusion imaging has advantages over alternative magnetic resonance imaging techniques for penumbra detection by providing serial assessment of available penumbra based on tissue viability.

Penumbra detection using PWI/DWI mismatch MRI in a rat stroke model with and without comorbidity: comparison of methods.

Perfusion-diffusion (perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI)) mismatch is used to identify penumbra in acute stroke. However, limitations in penumbra detection with mismatch are recognized, with a lack of consensus on thresholds, quantification and validation of mismatch. We determined perfusion and diffusion thresholds from final infarct in the clinically relevant spontaneously hypertensive stroke-prone (SHRSP) rat and its normotensive control strain, Wistar-Kyoto (WKY) and compared three methods for penumbra calculation. After permanent middle cerebral artery occlusion (MCAO) (WKY n=12, SHRSP n=15), diffusion-weighted (DWI) and perfusion-weighted (PWI) images were obtained for 4 hours post stroke and final infarct determined at 24 hours on T(2) scans. The PWI/DWI mismatch was calculated from volumetric assessment (perfusion deficit volume minus apparent diffusion coefficient (ADC)-defined lesion volume) or spatial assessment of mismatch area on each coronal slice. The ADC-derived lesion growth provided the third, retrospective measure of penumbra. At 1 hour after MCAO, volumetric mismatch detected smaller volumes of penumbra in both strains (SHRSP: 31 ± 50 mm(3), WKY: 22 ± 59 mm(3), mean ± s.d.) compared with spatial assessment (SHRSP: 36 ± 15 mm(3), WKY: 43 ± 43 mm(3)) and ADC lesion expansion (SHRSP: 41 ± 45 mm(3), WKY: 65 ± 41 mm(3)), although these differences were not statistically significant. Spatial assessment appears most informative, using both diffusion and perfusion data, eliminating the influence of negative mismatch and allowing the anatomical location of penumbra to be assessed at given time points after stroke.

Ethical education in software engineering: responsibility in the production of complex systems.

Among the various contemporary schools of moral thinking, consequence-based ethics, as opposed to rule-based, seems to have a good acceptance among professionals such as software engineers. But naïve consequentialism is intellectually too weak to serve as a practical guide in the profession. Besides, the complexity of software systems makes it very hard to know in advance the consequences that will derive from professional activities in the production of software. Therefore, following the spirit of well-known codes of ethics such as the ACM/IEEE's, we advocate for a more solid position in the ethical education of software engineers, which we call 'moderate deontologism', that takes into account both rules and consequences to assess the goodness of actions, and at the same time pays an adequate consideration to the absolute values of human dignity. In order to educate responsible professionals, however, this position should be complemented with a pedagogical approach to virtue ethics.