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Chang Zeng
About me
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Posts
portfolio
publications
talks
Talk 1 on Human-AI Cooperation for Fairness Elicitation
Published:
The talk delves into the complex notion of fairness and the difficulties involved in mathematically quantifying it for decision-makers. We investigate strategies for eliciting individuals’ fairness concepts using straightforward inquiries and comparisons. To express fairness concepts mathematically, we adapt the power mean function family. The introduction of the supremum distance metric $\Delta$ allows us to evaluate the maximum disparity between concepts in diverse scenarios. Moreover, we outline two approaches to formulating questions and establishes an upper threshold for the supremum distance metric $\Delta$ between any pair of fairness concepts.
Talk 2 on Human-AI Cooperation for Fairness Elicitation
Published:
The talk delves into the intricate concept of fairness and the challenges it poses when attempting to mathematically quantify it for decision-makers. We explore techniques for extracting individuals’ fairness concepts through straightforward inquiries and comparisons. To express fairness concepts mathematically, we employ the power mean function family $\text{M}$$p$$(s; w)$, where $p$ represents the fairness concept, and $s$ and $w$ represent the utility vector and probability measure for the groups. The introduction of the supremum distance metric $\Delta$ allows us to assess the maximum disparity between concepts in various scenarios. Recognizing the computational complexity of $\Delta$ due to the supremum operation, we introduce the additive supremum distance bound function $\Delta_{\uparrow}$ which provides upper bounds for the supremum distance metric. Additionally, we present two practical and efficient supremum distance bound functions, which are proportional to the harmonic difference and log ratio of any pair of fairness concepts ($p, p’$). Furthermore, we demonstrate how to leverage and modify the bounded/unbounded binary search to effectively identify human cardinal fairness concepts.
teaching
COMPSCI 560: Introduction To Computer & Network Security
Graduate course, Teaching assistant, The Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, 2022
Course Objective
Introduce the basic concept of computer security: the fundemental principle of the message security involving digest, signature, encryption/decryption algorithm, and detail illustration of system-wise security such as firewall and malicious software.
INFO 203: A Network World
Undergraduate course, Teaching assistant, The Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, 2023
Course Objective
This course provides an introduction to fundamentals and high-level concepts of the design and implementation of technologies that provide us with A Connected World; a world in our hand. This technology as a whole, enables us to reach the furthest point of the Planet Earth, and for that matter, the space surrounding it (of course, if the technology is enabled in the far end point). While we will mostly focus on the technical foundation of these technologies, we will also cover, to the extent possible, the social, policy, and economic aspect of these technologies. To achieve concrete learning results, we will focus on the current Internet and will learn the building blocks that bring the word at our reach.
COMPSCI 560: Introduction To Computer & Network Security
Graduate course, Teaching assistant, The Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, 2023
Course Objective
Introduce the basic concept of computer security: the fundemental principle of the message security involving digest, signature, encryption/decryption algorithm, and detail illustration of system-wise security such as firewall and malicious software.