The Dangers of Human-Like Bias in Machine-Learning Algorithms

Machine learning algorithms (MLAGs), frequently used in artificial intelligence (AI), rely on using patterns across sets of data to derive decision-making intelligence. In recent years, as society continues to give increasing authority to ML-driven AIs, these algorithms have demonstrated the ability to take on human-like discriminatory biases. Microsoft\u27s Tay, for example, a social media-based chatbot, went from resembling a normal teenage girl to displaying racist and sexist attitudes in a mere sixteen hours). Tay and many other ML-driven implementations across a wide variety of fields have replicated numerous human biases. In most cases, these human-like biases originated due to improper training of the associated MLAG. The purpose of this study is to illustrate the harmful effects of learned human-like biases in MLAGs, to highlight how improper algorithm training leads to bias formation, and to analyze research in bias correction. Discriminatory, human-like biases observed in ML algorithms have numerous harmful effects, and there exists a growing need to regulate and correct these biases

A quantum analog of Huffman coding

We analyze a generalization of Huffman coding to the quantum case. In particular, we notice various difficulties in using instantaneous codes for quantum communication. Nevertheless, for the storage of quantum information, we have succeeded in constructing a Huffman-coding inspired quantum scheme. The number of computational steps in the encoding and decoding processes of N quantum signals can be made to be of polylogarithmic depth by a massively parallel implementation of a quantum gate array. This is to be compared with the O (N^3) computational steps required in the sequential implementation by Cleve and DiVincenzo of the well-known quantum noiseless block coding scheme of Schumacher. We also show that O(N^2(log N)^a) computational steps are needed for the communication of quantum information using another Huffman-coding inspired scheme where the sender must disentangle her encoding device before the receiver can perform any measurements on his signals.Comment: Revised version, 7 pages, two-column, RevTex. Presented at 1998 IEEE International Symposium on Information Theor

Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

Quantitative Molecular Endoscope for Real-­‐Time Optical Imaging of Colorectal Cancer

http://deepblue.lib.umich.edu/bitstream/2027.42/96180/1/me450f12project3_report.pd