A Predictive Analytics-Based Decision Support System for Drug Courts

Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

“…In combination with increasing IT-processing capabilities, especially machine learning approaches including artificial neural networks have led to a task performance of AI that has never been seen before. Hence, advanced technologies of today make increasingly use of 'bio-inspired paradigms' in order to effectively tackle complex real-world problems (Zolbanin et al, 2019). We still speak of such systems as "weak AI" or "narrow AI" -since they are only used for very specific tasks and, in contrast to "strong AI", are not universally applicable (Searle, 1980;Watson, 2017).…”

“…AI is thus increasingly applied in use cases with potentially severe consequences for humans. This holds true not only in medical diagnostics, but also in processes of job recruitment (Dastin, 2018), credit scoring (Wang et al, 2019), prediction of recidivism in drug courts (Zolbanin et al, 2019), or as autopilots in aviation (Garlick, 2017) and autonomous driving (Grigorescu et al, 2020). Furthermore, corresponding technology is more and more integrated into our everyday private lives in the form of intelligent agents like Google Home or Siri (Bruun & Duka, 2018).…”

Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda

“…This initiative, if performed properly, has proven to be effective in lowering costs to the community and improving social outcomes. To enable better management of resources and improvement of outcomes, advanced analytics models are developed using large real-world data obtained from drug courts to predict and explain who would or would not graduate from these treatments (Zolbanin et al, 2020), who would be a returning offender (i.e., recidivism) (Delen et al, 2021), and to prescribe a set of guidelines (presented as characteristics of the offenders) that can help jurisdictions and drug court administrators to make more effective and efficient decisions. • Educational analytics.…”

A Techno-Business Platform to Improve Customer Experience Following the Brand Crisis Recovery: A B2B Perspective

“…The broad acceptance of the fermium business model as well as the advent of cost-effective analytics solutions has made analytics more accessible (Mäntymäki et al, 2020). Many firms have embraced analytics to make data-driven choices and improve daily operations as a result of the influx of data in various forms (Zolbanin et al, 2019) and B2B is not an exception (Hallikainen et al, 2020;Wang et al, 2020). Business analytics such as descriptive, predictive and prescriptive analytics are the most significant areas for better decision making as the amount of data grows exponentially (Song et al, 2014).…”