The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements

Prior work has established the importance of integrating AI ethics topics into computer and data sciences curricula. We provide evidence suggesting that one of the critical objectives of AI Ethics education must be to raise awareness of AI harms. While there are various sources to learn about such harms, The AI Incident Database (AIID) is one of the few attempts at offering a relatively comprehensive database indexing prior instances of harms or near harms stemming from the deployment of AI technologies in the real world. This study assesses the effectiveness of AIID as an educational tool to raise awareness regarding the prevalence and severity of AI harms in socially high-stakes domains. We present findings obtained through a classroom study conducted at an R1 institution as part of a course focused on the societal and ethical considerations around AI and ML. Our qualitative findings characterize students' initial perceptions of core topics in AI ethics and their desire to close the educational gap between their technical skills and their ability to think systematically about ethical and societal aspects of their work. We find that interacting with the database helps students better understand the magnitude and severity of AI harms and instills in them a sense of urgency around (a) designing functional and safe AI and (b) strengthening governance and accountability mechanisms. Finally, we compile students' feedback about the tool and our class activity into actionable recommendations for the database development team and the broader community to improve awareness of AI harms in AI ethics education.Comment: 37 pages, 11 figures; To appear in the proceedings of EAAMO 202

Moral Machine or Tyranny of the Majority?

With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations where even humans lack consensus. In the Moral Machine project, researchers crowdsourced answers to "Trolley Problems" concerning autonomous vehicles. Subsequently, Noothigattu et al. (2018) proposed inferring linear functions that approximate each individual's preferences and aggregating these linear models by averaging parameters across the population. In this paper, we examine this averaging mechanism, focusing on fairness concerns in the presence of strategic effects. We investigate a simple setting where the population consists of two groups, with the minority constituting an {\alpha} < 0.5 share of the population. To simplify the analysis, we consider the extreme case in which within-group preferences are homogeneous. Focusing on the fraction of contested cases where the minority group prevails, we make the following observations: (a) even when all parties report their preferences truthfully, the fraction of disputes where the minority prevails is less than proportionate in {\alpha}; (b) the degree of sub-proportionality grows more severe as the level of disagreement between the groups increases; (c) when parties report preferences strategically, pure strategy equilibria do not always exist; and (d) whenever a pure strategy equilibrium exists, the majority group prevails 100% of the time. These findings raise concerns about stability and fairness of preference vector averaging as a mechanism for aggregating diverging voices. Finally, we discuss alternatives, including randomized dictatorship and median-based mechanisms.Comment: To appear in the proceedings of AAAI 202

Personalized machine learning for facial expression analysis

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 35-36).For this MEng Thesis Project, I investigated the personalization of deep convolutional networks for facial expression analysis. While prior work focused on population-based ("one-size-fits-all") models for prediction of affective states (valence/arousal), I constructed personalized versions of these models to improve upon state-of-the-art general models through solving a domain adaptation problem. This was done by starting with pre-trained deep models for face analysis and fine-tuning the last layers to specific subjects or subpopulations. For prediction, a "mixture of experts" (MoE) solution was employed to select the proper outputs based on the given input. The research questions answered in this project are: (1) What are the effects of model personalization on the estimation of valence and arousal from faces? (2) What is the amount of (un)supervised data needed to reach a target performance? Models produced in this research provide the foundation of a novel tool for personalized real-time estimation of target metrics.by Michael A. Feffer.M. Eng

Moral Machine or Tyranny of the Majority?

With artificial intelligence systems increasingly applied in consequential domains, researchers have begun to ask how AI systems ought to act in ethically charged situations where even humans lack consensus. In the Moral Machine project, researchers crowdsourced answers to "Trolley Problems" concerning autonomous vehicles. Subsequently, Noothigattu et al. (2018) proposed inferring linear functions that approximate each individual's preferences and aggregating these linear models by averaging parameters across the population. In this paper, we examine this averaging mechanism, focusing on fairness concerns and strategic effects. We investigate a simple setting where the population consists of two groups, the minority constitutes an α < 0.5 share of the population, and within-group preferences are homogeneous. Focusing on the fraction of contested cases where the minority group prevails, we make the following observations: (a) even when all parties report their preferences truthfully, the fraction of disputes where the minority prevails is less than proportionate in α; (b) the degree of sub-proportionality grows more severe as the level of disagreement between the groups increases; (c) when parties report preferences strategically, pure strategy equilibria do not always exist; and (d) whenever a pure strategy equilibrium exists, the majority group prevails 100% of the time. These findings raise concerns about stability and fairness of averaging as a mechanism for aggregating diverging voices. Finally, we discuss alternatives, including randomized dictatorship and median-based mechanisms

A Pharmacokinetic and Pharmacodynamic Investigation of an ε-Aminocaproic Acid Regimen Designed for Cardiac Surgery With Cardiopulmonary Bypass

Objective: To investigate the pharmacokinetics and pharmacodynamics of an ε-aminocaproic acid (EACA) regimen designed for cardiac surgery with cardiopulmonary bypass (CPB). Design: Prospective observational study requiring blood sampling to measure EACA concentrations and fibrinolysis markers (fibrinogen, D-dimer, α2-antiplasmin, and tissue plasminogen activator-plasminogen activator inhibitor [tPA-PAI-1] complex). Setting: Single-center, tertiary medical center. Participants: Patients who underwent cardiac surgery with CPB between 2018 and 2019 for aortic or mitral valve replacement/repair or coronary artery bypass grafting. Previous sternotomy patients were included. Intervention: None. Measurements and Main Results: The pharmacokinetics of EACA, during CPB, were described by a 3-compartment disposition model. EACA concentrations were greater than 130 mg/L in all patients after CPB and in most patients during CPB. The D-dimer level trended up and reached a peak median level of 1.35 mg/L of fibrinogen equivalence units (FEU) at 15 minutes after protamine administration. The median change in D-dimer (ΔD-dimer) from baseline to 15 minutes after protamine was 0.34 (–0.48 to 3.81) mg/L FEU. ΔD-dimer did not correlate with EACA concentration intraoperatively, urine output, body weight, glomerular filtration rate, cell salvage volume, and ultrafiltration volume. The median 24-hour chest tube output was 445 (180-1,011) mL. Conclusion: This regimen provided maximum EACA concentrations near the time of protamine administration, with a total perioperative dose of 15 g. Most patients had EACA concentrations greater than the target during CPB. ΔD-dimer did not correlate with EACA concentration. The median 24-hour chest tube output compared well to similar studies that used higher doses of EACA