Artificial Intelligence and Machine Learning from an

Artificial Intelligence and Machine Learning from an Ethical Perspective

How to define AI Alan Turing is known for his work with artificial intelligence, machine learning, and developing what is known as the “Turing Test” which measures the effectiveness of AI to “mimic” human response. Turing noted machines have the ability to “imitate” human reactions, and we have seen many iterations of this.

Machine Learning – The Trained Model Machine Learning Predictive models versus natural law The computing power of modern servers and clustering technologies has advanced the development and integration of dissimilar models to be predict features.

A Gamechanger? ChatpGPT is what is known as a large language model (LLM) using deep learning algorithms and transformer models for massive amounts of data. This is the Artificial Intelligence (AI) element that most users interact with and is trained with common and domain-specific utterances Its problem-solving capabilities can be applied to fields like healthcare, finance, and entertainment where large language models serve a variety of NLP applications, such as translation, chatbots, AI assistants, and more

Rapid Growth Studies show that when developers utilized LLMbased tools, such as ChatGPT integrated with an IDE, there was over a 55% increase in performance over the control group (Peng et al., 2021). Variable Calculation: Where P number of supported parameters by the language model Where N number of supported programming languages supported by the tool Where L average number of variables per language

The Ethical Dilemma Considering the work of Kramer et al. (2014) brings many issues to mind on Advanced AI and topics around Emotional Contagion including: Lack of informed consent Harm and beneficence Justice and fairness Other emotional contagion studies show bots can exploit people with emotional contagion (Ferrara & Yang, 2015). Rich Adds can influence consumer behavior and persuasion (Matz et al., 2017)

General Discussion Machine Learning and Artificial Intelligence continue to improve the human experience, in general, often detecting things a passing glance might miss. The adage that, if you don’t pay for it, then you’re likely the product holds true Explainers can improve not only the experience and help consumers, but also help prevent violations of consumer and personal protection statutes While these OpenAI tools are relatively new, they have been woven into the fabric of tools such as Office 365 and integrated development environments (IDEs) such as VS Code, Github, and more requiring the need for more foundational courses in ethics at the undergraduate and graduate levels

In Conclusion The need for educators to reconsider how ethics are taught in engineering and business schools The use of explainers to help support transparency, accountability, and fairness Further exploration into the intricacies of featurization and the impact DEI activities might improperly influence decision making. The need for more research into the ethics of AI and Machine Learning across generational boundaries.

References Thank You! Berghel, H. (2018). Malice domestic: The Cambridge analytica dystopia. Computer, 51(05), 84-89. Ferrara, E., & Yang, Z. (2015). Measuring emotional contagion in social media. PloS one, 10(11), e0142390. Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National academy of Sciences of the United States of America, 111(24), 8788. Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the national academy of sciences, 114(48), 12714-12719. Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of ai on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590. Samuel, A. L. (1959). Machine learning. The Technology Review, 62(1), 42-45. Turing, A. M. (1950). Mind. Mind, 59(236), 433-460. Wang, H., Li, Z., & Cheng, Y. (1961). PACIIA 2008.

James H. Herring I’m a data specialist and researcher who loves to discover and share insights from data. I use various tools and languages to analyze, manage, and visualize complex datasets and make informed decisions. I also create data-driven solutions to improve efficiency and performance for different partners. I’m always learning new skills and staying updated with the latest developments in data science, engineering, and analytics. I’m looking for opportunities to use my expertise and help organizations achieve their goals with data-driven strategies. I also teach and train budding data specialists as a mentor and faculty on topics from foundations to ethics.