Pendulums swing and embarrassed agencies overreact.

https://www.pogowasright.org/the-fbi-is-spending-millions-on-social-media-tracking-software/

The FBI is spending millions on social media tracking software

Aaron Schaffer reports:

Social media users seemed to foreshadow the Jan. 6 attack on the U.S. Capitol — and the FBI apparently missed it.

Now, the FBI is doubling down on tracking social media posts, spending millions of dollars on thousands of licenses to powerful social media monitoring technology that privacy and civil liberties advocates say raise serious concerns.

The FBI has contracted for 5,000 licenses to use Babel X, a software made by Babel Street that lets users search social media sites within a geographic area and use other parameters.

Read more at The Washington Post.

Who’d a thunk it?

https://link.springer.com/article/10.1007/s10506-022-09312-z

Perceptions of Justice By Algorithms

Artificial Intelligence and algorithms are increasingly able to replace human workers in cognitively sophisticated tasks, including ones related to justice. Many governments and international organizations are discussing policies related to the application of algorithmic judges in courts. In this paper, we investigate the public perceptions of algorithmic judges. Across two experiments (N = 1,822), and an internal meta-analysis (N = 3,039), our results show that even though court users acknowledge several advantages of algorithms (i.e., cost and speed), they trust human judges more and have greater intentions to go to the court when a human (vs. an algorithmic) judge adjudicates. Additionally, we demonstrate that the extent that individuals trust algorithmic and human judges depends on the nature of the case: trust for algorithmic judges is especially low when legal cases involve emotional complexities (vs. technically complex or uncomplicated cases).

Taking a seat on the bandwagon?

https://ojs.stanford.edu/ojs/index.php/intersect/article/view/2168

On Facial Recognition Technology

Since the beginning of the 2000s, Facial Recognition Technology (FRT) has become significantly more accurate and more accessible. Both government and commercial entities use it in increasingly innovative approaches. News agencies use it to spot celebrities at big events. Car companies install it on dashboards to alert drivers falling asleep at the wheel. Governments have used it to track Covid-19 patients’ compliance with quarantine regimes, or to reunite missing children with their families. However, as the use of technology has become more widespread, the controversies around it have also grown. The technology offers tremendous opportunities, but there are reasons to be concerned about its impact on privacy and civil liberties, if it is not used properly. In this paper, I make a brief introduction to facial recognition technology, look separately at commercial and government applications of it, and present my argument why the US needs a federal legislation on FRT.

Making AI work for you.

https://research.cbs.dk/en/publications/ai-ethics-regulation-amp-firm-implications

AI Ethics, Regulation & Firm Implications

As the widespread application of artificial intelligence permeates an increasing number of businesses, ethical issues such as algorithmic bias, data privacy, and transparency have gained increased attention, raising renewed calls for policy and regulatory changes to address the potential consequences of AI systems and products. In this article, we build on original research to outline distinct approaches to AI governance and regulation and discuss the implications for firms and their managers in terms of adopting AI and ethical practices going forward. We examine how manager perception of AI ethics increases with the potential of AI-related regulation but at the cost of AI diffusion. Such trade-offs are likely to be associated with industry specific characteristics, which holds implications for how new and intended AI regulations could affect varying industries differently. Overall, we recommend that businesses embrace new managerial standards and practices that detail AI liability under varying circumstances, even before it is regulatory prescribed. Stronger internal audits, as well as third-party examinations, would provide more information for managers, reduce managerial uncertainty, and aid the development of AI products and services that are subject to higher ethical as well as legal, and policy standards.

Following advances(?) in my field…

https://rucore.libraries.rutgers.edu/rutgers-lib/67194/

The use of artificial intelligence in auditing and forensics

This dissertation examines the use of artificial intelligence for auditing and forensics. The first essay is a conceptual analysis, the second is quantitative and experimental. The first essay focuses on the ethics of AI. Accounting firms are reporting the use of Artificial Intelligence (AI) in their auditing and advisory functions, citing benefits such as time savings, faster data analysis, increased levels of accuracy, more in-depth insight into business processes, and enhanced client service. AI, an emerging technology that aims to mimic humans' cognitive skills and judgment, promises competitive advantages to the adopter. As a result, all the Big 4 firms are reporting its use and their plans to continue with this innovation in audit planning risk assessments, tests of transactions, analytics, and the preparation of audit work-papers, among other uses. As the applications and benefits of AI continue to emerge within the auditing profession, there is a gradual awakening to the fact that unintended consequences may also arise. Thus, this essay responds to the call of numerous researchers to explore the benefits of AI and investigate the ethical implications of the use of this emerging technology. By combining two futuristic ethical frameworks, this study forecasts the ethical implications of using AI in auditing, given its inherent features, nature, and intended functions. This essay provides a conceptual analysis of AI's practical ethical and social issues, using past studies and inferences based on the reported use of the technology by auditing firms. Beyond exploring these issues, this essay discusses the responsibility for the policy and governance of emerging technology.

The second essay focuses on the use of machine learning in auditing. Fraud risk assessment is challenging for external auditors due to its complexity and because external auditors are usually the outsiders looking in. This essay examines the use of a framework that combines natural language processing and machine learning for detecting fraud red flags within corporate communication. The framework uses natural language processing to measure the temporal sentiments and emotions conveyed in corporate communication and the topics discussed that point to fraud red flags. The framework relies on machine learning to identify the temporal changes in the derived quantitative measures. When applied to a real corporate communication dataset for a firm with known financial statement fraud, the machine learning framework correctly flagged the implicated departments, demonstrating how auditors can use the framework for fraud risk assessments. Additionally, the essay validates the machine learning framework. To validate the machine learning framework, I used an expert panel of forensics experts with CPA certification. Given the same information, the expert panel expressed fraud risk assessments consistent with the machine learning framework. This second essay uses an ensemble of machine learning methods to analyze the temporal changes of the sentiments, emotions, and topics discussed by individuals within an organization to detect fraud cues. The key contribution of the second essay is that it examines how machine learning and textual analysis can be used to detect fraud risk cues in the organization before the issuing of financial statements (i.e., does not rely on elements of the issued financial statement and therefore can be used in continuous auditing). Since the methodology in this paper begins with unsupervised machine learning, this study demonstrates an automated approach to labeling a digital communication dataset for machine learning to detect fraud cues. The use of an unsupervised machine learning approach enables this framework to be generalizable in that there is no requirement for a context-specific pre-labeled dataset. However, there is an initial requirement for a fraud word list, as discussed in chapter 3. Based on a literature review by Sánchez-Aguayo et al. (2021), there is an identified gap in studies that use fraud detection, human behavior, machine learning and fraud theory. This second essay cuts across these four areas.

It’s hard to strangle someone by texting…

https://dilbert.com/strip/2022-04-10