Explore the vast range of titles available.

Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as “black teenagers” are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.

Background Numerous strategies have been applied to combat flat or declining social responsibility (SR) attitudes of undergraduate students in STEM fields. This paper presents the results of a five-year, mixed methods longitudinal study tracking a cohort of undergraduate students at the Georgia Institute of Technology. Drawing on the Professional Social Responsibility Development Model and the Generalized Professional Responsibility Assessment (GPRA), we analyze results from surveys ( n  = 124 students) and interviews ( n  = 19 students), each conducted twice at different time points during students’ undergraduate education. Our focus is on the influence of different kinds of SR-related activities and experiences on student SR development over time, measured both quantitatively and qualitatively along several dimensions. Based on regression analyses controlling for pre-college attitudes, subgroup and path analyses, and triangulation with qualitative interview data, we identify key influences and inhibitors shaping SR development among STEM undergraduates. Results Our results reinforce prior findings that student social awareness and professional development remain largely flat over time, and we observe a statistically significant decline in professional connectedness by graduation. We additionally observe a growing emphasis on salary as compared to helping others as a motivating factor in career decisions. Students report rare and unpersuasive efforts as part of in-major and STEM education to build SR, and other formal educational efforts like out-of-major SR activities or activities associated with institutional events have a modest impact as well. In contrast, the most common and positive influences occur outside of formal education. Peer interactions and personal exploration appear to be the most positive forces affecting SR development. Overall, while some students report an increase in basic social awareness, they also tend to report decreases in self-efficacy and few changes to their career orientations related to SR. Conclusions Despite measurement challenges that occur outside of simpler pre–post-analyses or limited-term interventions, we encourage continued focus on holistic and longitudinal analyses. These may be needed to account for the complex interrelationships between different SR experiences and their impacts on personal and professional SR development. Overall, greater efforts are needed to measure, understand, and improve SR development for undergraduate students. We suggest that it may be important to both enhance the quality of disciplinary SR education in STEM and foster benefits of strategies like peer-based learning.

The crash of two 737 MAX passenger aircraft in late 2018 and early 2019, and subsequent grounding of the entire fleet of 737 MAX jets, turned a global spotlight on Boeing’s practices and culture. Explanations for the crashes include: design flaws within the MAX’s new flight control software system designed to prevent stalls; internal pressure to keep pace with Boeing’s chief competitor, Airbus; Boeing’s lack of transparency about the new software; and the lack of adequate monitoring of Boeing by the FAA, especially during the certification of the MAX and following the first crash. While these and other factors have been the subject of numerous government reports and investigative journalism articles, little to date has been written on the ethical significance of the accidents, in particular the ethical responsibilities of the engineers at Boeing and the FAA involved in designing and certifying the MAX. Lessons learned from this case include the need to strengthen the voice of engineers within large organizations. There is also the need for greater involvement of professional engineering societies in ethics-related activities and for broader focus on moral courage in engineering ethics education.

To assess ethics pedagogy in science and engineering, we developed a new tool called the Engineering and Science Issues Test (ESIT). ESIT measures moral judgment in a manner similar to the Defining Issues Test, second edition, but is built around technical dilemmas in science and engineering. We used a quasi-experimental approach with pre- and post-tests, and we compared the results to those of a control group with no overt ethics instruction. Our findings are that several (but not all) stand-alone classes showed a significant improvement compared to the control group when the metric includes multiple stages of moral development. We also found that the written test had a higher response rate and sensitivity to pedagogy than the electronic version. We do not find significant differences on pre-test scores with respect to age, education level, gender or political leanings, but we do on whether subjects were native English speakers. We did not find significant differences on pre-test scores based on whether subjects had previous ethics instruction; this could suggest a lack of a long-term effect from the instruction.

Robots are becoming an increasingly pervasive feature of our personal lives. As a result, there is growing importance placed on examining what constitutes appropriate behavior when they interact with human beings. In this paper, we discuss whether companion robots should be permitted to “nudge” their human users in the direction of being “more ethical”. More specifically, we use Rawlsian principles of justice to illustrate how robots might nurture “socially just” tendencies in their human counterparts. Designing technological artifacts in such a way to influence human behavior is already well-established but merely because the practice is commonplace does not necessarily resolve the ethical issues associated with its implementation.

Suppose you are walking on a sidewalk and are nearing an intersection. A flashing sign indicates that you can cross the street. To cross safely, you have to rely on nearby cars to respect the rules of the road. Robots--whether on the road, such as in the case of self-driving cars, or working in buildings, such as in the case of emergency evacuation robots--are influencing human decision-making processes. Yet whether placing trust in the technology is warranted, especially when it can mean life or death, is an open question. As robots more fully interact with humans, the role of human-robot trust and of biases integrated into the technology must be carefully investigated. The focus on trust emerges because research shows that humans tend to trust robots similarly to the way they trust other humans; thus, the concern is that people may underappreciate or misunderstand the risk associated with handing over decisions to a robot.

The literature on self-driving cars and ethics continues to grow. Yet much of it focuses on ethical complexities emerging from an individual vehicle. That is an important but insufficient step towards determining how the technology will impact human lives and society more generally. What must complement ongoing discussions is a broader, system level of analysis that engages with the interactions and effects that these cars will have on one another and on the socio-technical systems in which they are embedded. To bring the conversation of self-driving cars to the system level, we make use of two traffic scenarios which highlight some of the complexities that designers, policymakers, and others should consider related to the technology. We then describe three approaches that could be used to address such complexities and their associated shortcomings. We conclude by bringing attention to the “Moral Responsibility for Computing Artifacts: The Rules”, a framework that can provide insight into how to approach ethical issues related to self-driving cars.

As we near a time when robots may serve a vital function by becoming caregivers, it is important to examine the ethical implications of this development. By applying the capabilities approach as a guide to both the design and use of robot caregivers, we hope that this will maximize opportunities to preserve or expand freedom for care recipients. We think the use of the capabilities approach will be especially valuable for improving the ability of impaired persons to interface more effectively with their physical and social environments.