Eliminating Mole Size in Melanoma Classification

While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on which they are trained. This paper combats this notion, arguing that mole size, a vital attribute in professional dermatology, is a red herring in automated melanoma detection. Although malignant melanomas are consistently larger than benign melanomas, this distinction proves unreliable and harmful when images cannot be contextually scaled. This implementation builds a custom model that eliminates size as a training feature to prevent overfitting to incorrect parameters. Additionally, random rotation and contrast augmentations are performed to simulate the real-world use of melanoma detection applications. Several custom models with varying forms of data augmentation are implemented to demonstrate the most significant features of the generalization abilities of mole classifiers. These implementations show that user unpredictability is crucial when utilizing such applications. The caution required when manually modifying data is acknowledged, as data loss and biased conclusions are necessary considerations in this process. Additionally, mole size inconsistency and its significance are discussed in both the dermatology and deep learning communities

Establishing Data Provenance for Responsible Artificial Intelligence Systems

Data provenance, a record that describes the origins and processing of data, offers new promises in the increasingly important role of artificial intelligence (AI)-based systems in guiding human decision making. To avoid disastrous outcomes that can result from bias-laden AI systems, responsible AI builds on four important characteristics: fairness, accountability, transparency, and explainability. To stimulate further research on data provenance that enables responsible AI, this study outlines existing biases and discusses possible implementations of data provenance to mitigate them. We first review biases stemming from the data’s origins and pre-processing. We then discuss the current state of practice, the challenges it presents, and corresponding recommendations to address them. We present a summary highlighting how our recommendations can help establish data provenance and thereby mitigate biases stemming from the data’s origins and pre-processing to realize responsible AI-based systems. We conclude with a research agenda suggesting further research avenues

Customer-Base Analysis Using Repeated Cross-Sectional Summary (RCSS) Data

Abstract We address a critical question that many firms are facing today: Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers' transaction activity? We address this question in the context of customer-base analysis. A number of researchers have developed customerbase analysis models that perform very well given detailed individual-level data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated cross-sectional summaries (RCSS) of the transaction data (e.g., four quarterly histograms). Such summaries are easy to create, visualize, and distribute, irrespective of the size of the customer base. An added advantage of the RCSS data structure is that individual customers cannot be identified, which makes it desirable from a privacy viewpoint as well. We focus on the widely used Pareto/NBD model and carry out a comprehensive simulation study covering a vast spectrum of market scenarios. We find that the RCSS format of four quarterly histograms * Corresponding author Email addresses: [email protected] (Kinshuk Jerath), [email protected] (Peter S. Fader), [email protected] (Bruce G.S. Hardie) URL: www.petefader.com (Peter S. Fader), http://www.brucehardie.com (Bruce G.S. Hardie) 1 The authors thank David Bell for providing the Bonobos data used in this paper. 2 The second author acknowledges the support of the Wharton Customer Analytics Initiative. serves as an suitable substitute for individual-level data. We confirm the results of the simulations on a real dataset of purchasing from an online fashion retailer

On the Existence and Significance of Data Preprocessing Biases in Web-Usage Mining

The literature on web-usage mining is replete with data preprocessing techniques, which correspond to many closely related problem formulations. We survey datapreprocessing techniques for session-level pattern discovery and compare three of these techniques in the context of understanding session-level purchase behavior on the web. Using real data collected from 20,000 users ’ browsing behavior over a period of six months, four different models (linear regressions, logistic regressions, neural networks, and classification trees) are built based on data preprocessed using three different techniques. The results demonstrate that the three approaches result in radically different conclusions and provide initial evidence that a data preprocessing bias exists, the effect of which can be significant