Blog 10 - Transforming Business - The use of Artificial Intelligence in Business

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  Artificial Intelligence in Business: Blessing or a Curse? To understand AI in business we must first figure out what artificial intelligence (AI) is? It can be defined as the simulation of human intelligence process by machines, scary stuff!, particularly computer systems. Some applications of AI may include; natural language processing, speech recognition and machine vision. Is AI out to replace us in the future?  Is AI a curse to businesses? Here are some potential challenges, and possible solutions, that businesses may face if they chose to incorporate AI  (Forbes, 2023) . Lack of expertise: To tackle this issue you could train, collaborate, hire and use user-friendly tools. Building internal expertise is vital for successful AI adoption. Uncertainty Implementation: AI's placement is critical; avoid harming customer experience. It should aid, not replace. You should monitor closely to prevent frustrations that drive customers away. Outdated infrastructure: May a...
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Blog 6 - Big Data - Potential Ethical and Social challenges

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 Ethical and Social implications of Big Data

"Big data refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods" 

Big Data's introduction has completely changed the way we gather, store, and evaluate data. It has provided numerous advantages to society, revolutionised industries, and enhanced decision-making processes. These benefits also present ethical and societal issues that need to be resolved. I will examine the numerous ethical and societal conundrums raised by big data in this blog.

Privacy Concerns: Organisations are attractive targets for cybercriminals due to fact that they collect and analyse vast amounts of data. A complex cyberattack, an insider threat, or a straightforward mistake like an insecure and unsecure databases can all lead to privacy breaches that let unauthorised parties access and use personal information. As a result the organisation may face financial repercussions, damaged reputations, and a decline in customer trust.

Discrimination: Factors that may be the cause of discriminatory treatment include skin colour, ethnicity, and sex; other factors include gender, residence location, income or education level, and others. Discrimination against specific categories of individuals may be a result from the use of big data analytics to enhance company procedures or to offer individualised services. Unintentional data biases may be produced at any stage of the big data analytics pipeline as a result of incorrect statistical processing or poor data quality.

Bias: Biased data produces skewed results, systematic prejudice, and low accuracy because it does not fairly represent the dataset. Even inadvertently introducing bias into data collection is possible. Access barriers can introduce bias into the data collection process, although certain biases may already be present in the surveys used to gather the data. Bias can result from obstacles related to language, poverty, or even just having access to technology in general during the collection of data.



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