As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.

Advances in both semiconductor and automotive industry are today enabling the next generation of vehicles with significant electronics content than ever before. Consumers can now avail vehicle offerings in the form of Electric and Hybrid Electric Vehicles (EV/HEV) that have improved fuel efficiency, provide enhanced driver-passenger comfort and experience through Advance Driver Assistance Systems (ADAS) and car infotainment systems, and more. Increasing electronics, software content, and connectivity drive two consumer concerns - “functional safety” and “security” - to the forefront. In this tutorial, we dissect these concerns from an end application perspective and translate the system level requirements and standards into semiconductor development requirements. We indicate both current and emerging practices, and touch upon areas requiring new or optimal design and electronic design automation (EDA) solutions. While functional safety is the primary focus for deep-dive in this tutorial, we also examine key facets of automotive security which is now emerging as a critical area for further understanding and standardization.

Jet-boats perform remarkably well at high-speed but lack low speed maneuverability for tight maneuvers such as ducking. This paper presents a low speed omnidirectional propulsion system controlled with a joystick for jet-boats. Two concepts are presented and evaluated. The first concept uses all original parts. The second concept uses a set of fix jet nozzles disposed around the hull. The position and angles of the nozzles are optimized with an index of omnidirectionality quality based on the projection of a set of force solutions on a shell with the shape of the desired force space. A 3D simulator backed by experimental results serves for the evaluation by potential customers of each design. The first concept exhibited poor maneuverability, as it offers ten times less force in sway than in surge. The optimized force space of the second concept is much more uniform, and was unanimously appreciated for its quality of low speed maneuverability. Both designs have been validated experimentally. The present work offers an omnidirectional propulsion system that is easy to enhance with advanced control laws. Velocity feedback control is given as example, and shows important improvement of maneuverability and robustness to miscalibration.