Careers of commercially successful female entrepreneurs in context of underdeveloped markets and weak institutions

PurposeCareers have come to dominate contemporary discourse on gendered entrepreneurship. This paper aims to explore entrepreneurial careers as recounted by commercially successful female entrepreneurs to examine how they strategize to construct desirable careers in contexts characterized by underdeveloped markets and weak institutions.Design/methodology/approachUsing a qualitative research design, data for our inquiry come from publicly available life history accounts of 20 female entrepreneurs appearing on an enterprise focus television show in Nigeria. The authors supplemented the television interview data with archival data in the form of publicly available digital footprints of the entrepreneurs collected from their company websites, magazines, online newspapers featuring these entrepreneurs and their social media pages such as LinkedIn, Wikipedia, Facebook and Instagram.FindingsThe careers of female entrepreneurs operating in context of underdeveloped institution and markets, the authors found, are characterized by four heterogeneous ingrained dispositions and actions reflecting how they got in and got on with their entrepreneurial careers: (1) “Observing and playing business,” (2) traipsing the “path less traveled,” (3) a hook to the “Pierian spring” of entrepreneurship and (4) “Grace under pressure” in decision-making.Originality/valueThe authors contribute to the entrepreneurship literature by providing insight into the lived experiences, agency and careers of commercially successful female entrepreneurs as played out in the form of a contextual practice of “wayfinding” to starting up and managing their own business ventures.

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6,408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

Ethical problems in the use of algorithms in data management and in a free market economy

The problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market.

Waving away waivers: an obligation to contribute to ‘herd knowledge’ for data linkage research?

In today’s online data-driven world, people constantly shed data and deposit digital footprints. When individuals access health services, governments and health providers collect and store large volumes of health information about people that can later be retrieved, linked and analysed for research purposes. This can lead to new discoveries in medicine and healthcare. In addition, when securely stored and de-identified, the privacy risks are minimal and manageable. In many jurisdictions, ethics committees routinely waive the requirement for researchers to obtain consent from data subjects before using and linking these datasets in an effort to balance respect for individuals with research efficiency. In this paper, I explore the ethical justification for using routinely collected health data for research without consent. I conclude that, not only is this morally justified but also that data subjects have a moral obligation to contribute their data to such research, which would obviate the need for ethics committees to consider consent waivers. In justifying this argument, I look to the duty of easy rescue, distributive justice and draw analogies with vaccination ethics.

WebPGA: An Educational Technology That Supports Learning by Reviewing Paper-Based Programming Assessments

Providing feedback to students is one of the most effective ways to enhance their learning. With the advancement of technology, many tools have been developed to provide personalized feedback. However, these systems are only beneficial when interactions are done on digital platforms. As paper-based assessment is still a dominantly preferred evaluation method, particularly in large blended-instruction classes, the sole use of electronic educational systems presents a gap between how students learn the subject from the physical and digital world. This has motivated the design and the development of a new educational technology that facilitates the digitization, grading, and distribution of paper-based assessments to support blended-instruction classes. With the aid of this technology, different learning analytics can be readily captured. A retrospective analysis was conducted to understand the students’ behaviors in an Object-Oriented Programming and Data Structures class from a public university. Their behavioral differences and the associated learning impacts were analyzed by leveraging their digital footprints. Results showed that students made significant efforts in reviewing their examinations. Notably, the high-achieving and the improving students spent more time reviewing their mistakes and started doing so as soon as the assessment became available. Finally, when students were guided in the reviewing process, they were able to identify items where they had misconceptions.