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1. Resume Format, LinkedIn URLs and Other Unexpected Influences on AI Personality Prediction in Hiring: Results of an Audit

   https://doi.org/10.1145/3514094.3534189  

   Rhea, Alene ; Markey, Kelsey ; D'Arinzo, Lauren ; Schellmann, Hilke ; Sloane, Mona ; Squires, Paul ; Stoyanovich, Julia ( July 2022 , AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society)

   Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the reliability of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. We develop a methodology for an external audit of stability of algorithmic personality tests, and instantiate this methodology in an audit of two systems, Humantic AI and Crystal. Rather than challenging or affirming the assumptions made in psychometric testing --- that personality traits are meaningful and measurable constructs, and that they are indicative of future success on the job --- we frame our methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. In our audit of Humantic AI and Crystal, we find that both systems show substantial instability on key facets of measurement, and so cannot be considered valid testing instruments. For example, Crystal frequently computes different personality scores if the same resume is given in PDF vs. in raw text, violating themore »assumption that the output of an algorithmic personality test is stable across job-irrelevant input variations. Among other notable findings is evidence of persistent --- and often incorrect --- data linkage by Humantic AI. An open-source implementation of our auditing methodology, and of the audits of Humantic AI and Crystal, is available at https://github.com/DataResponsibly/hiring-stability-audit.« less
2. Assessing Drawing Self-efficacy: A Validation Study Using Exploratory Factor Analysis (EFA) for the Drawing Self-efficacy Instrument (DSEI)

   https://doi.org/10.18260/1-2--36704  

   Jaison, Donna ; Merzdorf, Hillary ; Williford, Blake ; White, Lance ; Watson, Karan ; Douglas, Kerrie ; Hammond, Tracy ( July 2021 , 2021 ASEE Virtual Annual Conference)

   Drawing, as a skill, is closely tied to many creative fields and it is a unique practice for every individual. Drawing has been shown to improve cognitive and communicative abilities, such as visual communication, problem-solving skills, students’ academic achievement, awareness of and attention to surrounding details, and sharpened analytical skills. Drawing also stimulates both sides of the brain and improves peripheral skills of writing, 3-D spatial recognition, critical thinking, and brainstorming. People are often exposed to drawing as children, drawing their families, their houses, animals, and, most notably, their imaginative ideas. These skills develop over time naturally to some extent, however, while the base concept of drawing is a basic skill, the mastery of this skill requires extensive practice and it can often be significantly impacted by the self-efficacy of an individual. Sketchtivity is an AI tool developed by Texas A&M University to facilitate the growth of drawing skills and track their performance. Sketching skill development depends in part on students’ self-efficacy associated with their drawing abilities. Gauging the drawing self-efficacy of individuals is critical in understanding the impact that this drawing practice has had with this new novel instrument, especially in contrast to traditional practicing methods. It may alsomore »be very useful for other researchers, educators, and technologists. This study reports the development and initial validation of a new 13-item measure that assesses perceived drawing self efficacy. The13 items to measure drawing self efficacy were developed based on Bandura’s guide for constructing Self-Efficacy Scales. The participants in the study consisted of 222 high school students from engineering, art, and pre-calculus classes. Internal consistency of the 13 observed items were found to be very high (Cronbach alpha: 0.943), indicating a high reliability of the scale. Exploratory Factor Analysis was performed to further investigate the variance among the 13 observed items, to find the underlying latent factors that influenced the observed items, and to see if the items needed revision. We found that a three model was the best fit for our data, given fit statistics and model interpretability. The factors are: Factor 1: Self-efficacy with respect to drawing specific objects; Factor 2: Self-efficacy with respect to drawing practically to solve problems, communicating with others, and brainstorming ideas; Factor 3: Self-efficacy with respect to drawing to create, express ideas, and use one’s imagination. An alternative four-factor model is also discussed. The purpose of our study is to inform interventions that increase self-efficacy. We believe that this assessment will be valuable especially for education researchers who implement AI-based tools to measure drawing skills.This initial validity study shows promising results for a new measure of drawing self-efficacy. Further validation with new populations and drawing classes is needed to support its use, and further psychometric testing of item-level performance. In the future, this self-efficacy assessment could be used by teachers and researchers to guide instructional interventions meant to increase drawing self-efficacy.« less
3. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
4. Differences Between Science and Engineering Undergraduate Students’ Perceived Support: Exploring the Potential of College Profiles

   https://doi.org/10.1109/FIE43999.2019.9028454  

   Hall, Janice L. ; Verdin, Dina ; Lee, Walter C. ; Godwin, Allison ; Knight, David ( October 2019 , Frontiers in Education Conference)

   This work-in-progress research paper stems from a larger project where we are developing and gathering validity evidence for an instrument to measure undergraduate students' perceptions of support in science, technology, engineering, and mathematics (STEM). The refinement of our instrument functions to extend, operationalize, and empirically test the model of co-curricular support (MCCS). The MCCS is a conceptual framework of student support that explains how a student's interactions with the professional, academic and social systems within a college could influence their success more broadly in an undergraduate STEM degree program. Our goal is to create an instrument that functions diagnostically to help colleges effectively allocate resources for the various financial, physical, and human capital support provided to undergraduate students in STEM. While testing the validity of our newly developed instrument, an analysis of the data revealed differences in perceived support among College of Engineering (COE) and College of Science (COS) students. In this work-in-progress paper, we examine these differences at one institution using descriptive statistics and Welch's t-tests to identify trends and patterns of support among different student groups.
5. Rediscovering the human in AI design for fairness

   De Assis Nunes, Ana Carolina ; Zhang, Shaozeng ( January 2011 , SSSS Newsletter of the Society for Social Studies of Science)

   This paper is an initial report of our fair AI design project by a small research team made up of anthropologists and computer scientists. Our collaborative project was developed in response to the recent debates on AI's ethical and social issues (Elish and boyd 2018). We share this understanding that "numbers don't speak for themselves," but data enters into research projects already "fully cooked" (D'Ignazio and Klein 2020). Therefore, we take an anthropological approach to observing, recording, understanding, and reflecting upon the process of machine learning algorithm design from the first steps of choosing and coding datasets for training and building algorithms. We tease apart the encoding of social-cultural paradigms in the generation and use of datasets in algorithm design and testing. By doing so, we rediscover the human in data to challenge the methodological and social assumptions in data use and then to adjust the model and parameters of our algorithms. This paper centers on tracing the social trajectory of the Correctional Offender Management Profiling for Alternative Sanctions, known as the COMPAS dataset. This dataset contains data of over 10,000 criminal defendants in Broward County in Florida, the U.S. Since its publication, it has become a benchmark dataset inmore »the study of algorithmic fairness and was also used to design and train our algorithm for recidivism prediction. This paper presents our observation that data results from a complex set of social, political, and historical assumptions and circumstances and demonstrates how the social trajectory of data can be taken into the design of AI as automated systems become more intricate into our daily lives.”« less