On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.Comment: 17 pages, 19 figures, in Proceedings of ACM FAT* 2019, dataset & demo available at https://deception.machineintheloop.co

Counting the costs of crime in Australia: a 2011 estimate

This report estimates the costs of crime for the calendar year 2011. Executive summary This report seeks to estimate how much crime costs the Australian economy by calculating the number of crimes that come to the attention of the authorities and, using crime victimisation survey data, the number of crimes that are not recorded officially. A dollar figure is then calculated for each estimated crime event and an indication given of the total cost of each specific crime type in terms of actual loss, intangible losses, loss of output caused through the criminal conduct and other related costs such as medical expenses, where relevant. Added to these costs are the costs of preventing and responding to crime in the community including the costs of maintaining the criminal justice system agencies of police, prosecution, courts and correctional agencies, as well as a proportion of the costs of Australian and state and territory government agencies that have crime-related functions. Finally, a deduction is made for the value of property recovered in the case of property crime, as well as the amount of funds recovered from criminals under federal, state and territory proceeds of crime legislation. More detailed information about how each of these estimates was derived is provided in the main body of the report. Official attention paid to specific crime types, particularly drug-related crime and organised crime, affects both the reporting rate and also the cost of policing and correctional responses. In this sense, individual crime type costs and prevention and response costs are not mutually exclusive. Arguably, as individual crime types attract more attention, reporting rates increase and prevention and control of the crimes in question are seen as being deserving of increased resource

Innocent Until Proven Guilty: Suspicion of Deception in Online Reviews

- Purpose: This study formulates a new framework for identifying deception in consumer reviews through the lens of Interpersonal Deception Theory and the Persuasion Knowledge Model. It evaluates variables contributing to consumer intentions to purchase after reading deceptive reviews and proposes deception identification cues to be incorporated into the interpersonal communication theoretical framework. - Methodology: The first study is qualitative and quantitative, based on sentiment and lexical analysis of 1000 consumer reviews. The second study employs a USA national consumer survey with a PLS-SEM and a Process-based mediation-moderation analysis. - Findings: The study shows deceptive characteristics that cannot be dissimulated by reviewing consumers that represent review legitimacy based on review valence, authenticity, formalism, and analytical writing. The results also support the central role of consumer suspicion of an ulterior motive, with a direct and mediation effect regarding consumer emotions and intentions, including brand trust and purchase intentions. - Research implications: This paper presents a new framework for identifying deception in consumer reviews based on IDT and PKM, adding new theoretical elements that help adapt these theories to written digital communication specificities. The study clarifies the role of suspicion in a deceptive communication context and shows the variables contributing to consumers’ purchase intention after reading deceptive reviews. The results also emphasize the benefits of lexical analysis in identifying deceptive characteristics of reviews. - Practical implications: Companies can consider the vulnerability of certain generations based on lower levels of suspicions and different linguistic cues to detect deception in reviews. Long-term, marketers can also implement deception identification practices as potential new business models and opportunities. - Social implications: Policymakers and regulators need to consider critical deception cues and the differences in suspicion levels among segments of consumers in the formulation of preventative and deception management measures. - Originality/value: This study contributes to the literature by formulating a new framework for identifying deception in consumer reviews, adapted to the characteristics of written digital communication. The study emphasizes deception cues in eWOM and provides additional opportunities for theorizing deception in electronic communication

Review Manipulation: Literature Review, and Future Research Agenda

Background: The phenomenon of review manipulation and fake reviews has gained Information Systems (IS) scholars’ attention during recent years. Scholarly research in this domain has delved into the causes and consequences of review manipulation. However, we find that the findings are diverse, and the studies do not portray a systematic approach. This study synthesizes the findings from a multidisciplinary perspective and presents an integrated framework to understand the mechanism of review manipulation. Method: The study reviews 88 relevant articles on review manipulation spanning a decade and a half. We adopted an iterative coding approach to synthesizing the literature on concepts and categorized them independently into potential themes. Results: We present an integrated framework that shows the linkages between the different themes, namely, the prevalence of manipulation, impact of manipulation, conditions and choice for manipulation decision, characteristics of fake reviews, models for detecting spam reviews, and strategies to deal with manipulation. We also present the characteristics of review manipulation and cover both operational and conceptual issues associated with the research on this topic. Conclusions: Insights from the study will guide future research on review manipulation and fake reviews. The study presents a holistic view of the phenomenon of review manipulation. It informs various online platforms to address fake reviews towards building a healthy and sustainable environment

Deception Detection: An Exploration of Annotated Text-Based Cues

Do embedded textual cues in asynchronous communication affect deceptive message detection? The expanded use of social media and rich media applications in business make this an important issue. Prior research indicates deception commonly occurs in all forms of communication and people have difficulty detecting its use. Asynchronous online communications are no exception and offer users a variety of media choices which may complicate deception detection, particularly if the sender has strategically selected a channel intended to disguise their intentions. The current study investigated whether embedded, non-verbal cues in common media forms found in asynchronous online venues influenced deception detection. Drawing on media synchronicity theory, results suggest embedding non-verbal cues in the form of annotated text can enhance deception detection. Overall, the findings suggest managers must be wary of sender motivations, which can influence message veracity, particularly in low synchronicity environments where media is subject to edits and manipulations

Man vs machine – Detecting deception in online reviews

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material