Digital transformation strategies put pressure on organizations to digitalize their processes quickly and operations. Although this change may revolutionize their business, it has its own difficult challenges that they should be aware of.
Even though digital technologies are designed to increase the speed, efficiency, agility, and profitability of enterprises, recent developments have exposed unexpected dark sides of the digital world.
Whether organizations seek operational efficiency, contemporary business models, or improve operations, business executives must be aware of the unexpected downside of digital transformation.
· Workforce Risks: As businesses rely heavily on technology, the human element of threats will be important. Organizations face issues of data storage, network security and talent acquisition as much of the current workforce is hybrid or remote following the COVID-19 pandemic.
· Cloud Risks: As more organizations adopt the cloud, it introduces many new risks everyday. Architectural modifications, cloud implementation, deployment, most importantly, cloud management with current IT systems are among the frequently observed digital risks.
· Compliance Risks: Compliance risk is the obvious dark side of digital transformation. This is a known but unexpected dark side of digital that most businesses realize much later and have to suffer huge losses: data generation and deployment of new technologies to meet compliance requirements.
· Automation Risks: Businesses are always looking for ways to benefit from automation. But, apart from promoting scalability and flexibility, automation also has some drawbacks.
Many automation solutions may introduce software incompatibilities or add unnecessary operational complexity. Additionally, more automation means more vulnerabilities that increase the likelihood of data breaches. There may also be multiple unexpected risks regarding artificial intelligence and machine learning automation.
· Data Privacy Risks: Data privacy risks are increasing as a byproduct of digital transformation. Businesses nowadays know the threats, yet many fail to take advantage of digital capabilities to protect their data. Organizations often suffer serious consequences from data breaches in terms of reputation, finances, legitimacy, regulatory compliance and, most importantly, customer trust.
Read Full Article: Beware! Digital Transformation Has a Dark Side Too
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IntroductionPrepared learning machine best machine learning course online models do not always reflect the real desire for the work being done.
Interpreting for models allows us to evaluate their decisions and obtain information that the purpose alone cannot.Interpretation takes many forms and can be difficult to explain; we first examine the general frameworks and sets of definitions in which the interpretation of a model can be analyzed and compared.Next, we analyze a few well-known examples of interpretation – LIME (Ribeiro et al.
We conclude by making some general statements about the sector by looking at these examples.
Desired features in certain situations can often be a domain- or a problem, and although many papers seek models that translate into different domains or applications, the machine learning models community does not have a cohesive framework to discuss what structures make a model definition and what we do.Hopefully, do with descriptive results.
In many cases, the intended function cannot accurately capture the real-world costs of model decisions.The costs associated with ethics or fairness are difficult to determine in the intended work, and researchers may be unaware or unable to anticipate these costs in advance.Interpretation is required if model metrics are inadequate.
Interpreting allows us to understand exactly what a learning model is, what other information a model can provide, and the reasons behind its decisions, and to consider all of this in the context of the real-world problem we are trying to solve.
