When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices

Understanding User Perceptions of Trustworthiness in E-recruitment Systems

Algorithmic systems are increasingly deployed to make decisions that people used to make. Perceptions of these systems can significantly influence their adoption, yet, broadly speaking, users’ understanding of the internal working of these systems is limited. To explore users’ perceptions of algorithmic systems, we developed a prototype e-recruitment system called Algorithm Playground where we offer the users a look behind the scenes of such systems, and provide “how” and “why” explanations on how job applicants are ranked by their algorithms. Using an online study with 110 participants, we measured perceived fairness, transparency and trustworthiness of e-recruitment systems. Our results show that user understanding of the data and reasoning behind candidates’ rankings and selection evoked some positive attitudes as participants rated our platform to be fairer, more reliable, transparent and trustworthy than the e-recruitment systems they have used in the past

Fair Engineering of Machine Learning Systems – Lessons Learned from a Literature Review

With the growing prevalence of AI algorithms and their use to prepare and even execute decisions, there is increasing debate about whether the results of machine learning systems tend to be fairer or more unfair. When faced with engineering a fair machine learning solution in practice, trade-offs arise between conflicting fairness notions. We conduct a literature review on this topic. The results of our review indicate that a slight consensus exists that the human concept of fairness is much broader than what lies in the scope of current fairness metrics. We discuss the context of judging fairness metrics. We also find that, albeit much research already has been done, there is room for improvement when seeking to generalize the findings across different scenarios

Technical education teachers' perception of higher-order thinking skills and their ability to implement it in Indonesia

World Economic Forum’s report (2020) reported that the top five out of 10 skills needed by employers in 2025 are (1) analytical thinking and innovation, (2) active learning and learning strategies, (3) complex problem solving, (4) critical thinking and analysis, and (5) creativity, originality, and initiative. These skills thrive workers entering the Fourth Industrial Revolution (4IR) and are the core of Higher Order Thinking Skills (HOTS). Parallelly, educationists conclude that teaching students with HOTS is a must, but the challenge is how to do it effectively. This study’s objectives were to know vocational and technical teachers’ perception of HOTS and their ability to teach HOTS in their classrooms. The study population was State Vocational and Technical Senior High School (SMKN) in Yogyakarta Special Region (DIY) and Central Java Province (CJP) in Indonesia. The sample was determined by quota technique sampling and came up with SMKN 2 Yogyakarta in DIY and SMKN 2 Klaten, and SMKN Magelang in CJP. Collecting data technique used closed- and open- questionnaires and documentation. Data analysis used statistical descriptive and qualitative description. Research findings revealed that teachers’ perception of HOTS was very positive, while their ability to integrate HOTS concepts in their lesson plans and to implement them in the classroom still has significant difficulties