Ethical implications of text generation in the age of artificial intelligence

We are at a turning point in the debate on the ethics of Artificial Intelligence (AI) because we are witnessing the rise of general-purpose AI text agents such as GPT-3 that can generate large-scale highly refined content that appears to have been written by a human. Yet, a discussion on the ethical issues related to the blurring of the roles between humans and machines in the production of content in the business arena is lacking. In this conceptual paper, drawing on agenda setting theory and stakeholder theory, we challenge the current debate on the ethics of AI and aim to stimulate studies that develop research around three new challenges of AI text agents: automated mass manipulation and disinformation (i.e., fake agenda problem), massive low-quality content production (i.e., lowest denominator problem) and the creation of a growing buffer in the communication between stakeholders (i.e., the mediation problem).     

Mind the gender gap: Inequalities in the emergent professions of artificial intelligence (AI) and data science

The emergence of new prestigious professions in data science and artificial intelligence (AI) provide a rare opportunity to explore the gendered dynamics of technical careers as they are being formed. In this paper, we contribute to the literature on gender inequality in digital work by curating and analysing a unique cross-country data set. We use innovative data science methodology to investigate the nature of work and skills in these under-researched fields. Our research finds persistent disparities in jobs, qualifications, seniority, industry, attrition and even self-confidence in these fields. We identify structural inequality in data and AI, with career trajectories of professionals differentiated by gender, reflecting the broader history of computing. Our work is original in illuminating gendering processes within elite high-tech jobs as they are being configured. Paying attention to these nascent fields is crucial if we are to ensure that women take their rightful place at forefront of technological innovation.     

基于动态调整惯性权重粒子群算法的冷热电联供系统运行优化

为实现能源梯级利用、冷热电联供系统优化运行，将基本粒子群算法、改进粒子群法、动态调整惯性权重的粒子群（IDWPSO）算法进行对比。表明相较于其它两种算法IDWPSO算法在收敛速度与精度方面都有更好的表现。建立以运行成本和环保成本最小为目标的冷热电联供CCHP (combined cooling, heating and power)系统模型，并采用IDWPSO算法优化。结果表明，在满足系统负荷与约束条件下，IDWPSO算法优化后的系统综合成本有所降低，对CCHP系统优化运行具有指导意义。