From Copyright to Carbon: Tracking AI’s Impact
Occasionally I peek in on the SSRN Generative AI Special Topic Hub which describes itself as follows:
SSRN’s Generative AI Special Topic Hub provides a curated view into new early-stage research addressing generative artificial intelligence (AI), its potential applications in fields as varied as search technology, law, finance, and education, and evolving conversations around the ethical use of these technologies. With increasingly sophisticated computerized platforms producing more human-like results, researchers in many disciplines are debating the role of AI large language model (LLM) chatbots such as generative pretrained transformers (GPT) like ChatGPT, Google Bard, and Microsoft Bing and Copilot in knowledge work and scientific research. The main points of the discussion revolve around ownership of the output of the systems as well as legitimate uses of these technologies and the need to identify guidelines to avoid misuse. This hub presents curated insights from many disciplines that may inform the ongoing conversation about GPTs, AI chatbots, and related applications.
A recent scan of SSRN came up with these links which I thought deserve special interest; maybe you will find these interesting as well. In each case I discuss the reasons for my interest, which may be parallel to—or completely different from—your own:
From the abstract:
Critical copyright problems are raised by the convergence of AI and creativity, which challenges conventional notions of authorship and infringement. The study advocates for a unified framework to navigate the legal complexities and guarantee ethical considerations in the evolving landscape of creative technology, addressing the global scope of AI- generated art.
My reasons for interest were twofold: (1) I once managed a series of research projects related to the economics of how copyright law operates in industries including bibliographic databases, movies, textile patterns, and academic publishing, and (2) I currently make frequent use of AI tools in my own consulting, research, and writing.
Also, I’ve always been interested in the topics of creativity and innovation, and I have been thinking lately that AI tools when used in research actually hel people become more innovative and creativity. But the ethical issues raised by the authors of this work are very real. Nevertheless, is a “unified framework to navigate the legal complexities and guarantee ethical considerations” a real solution? You be the judge.
Examining the Impact of Generative AI on UX/UI Design
From the abstract:
This study examines the impact of generative artificial intelligence (GAI) on UX/UI design, proposing a new framework for its accountable integration.
Also:
The ability to quickly iterate and test numerous user interface variations speeds up the design process while enforcing the creation of user-friendly products.
The reason for my interest in this paper, again, is twofold: (1) My consulting involves working with companies that develop data-intensive software applications, and (2) having managed projects involving large amounts of testing and test data creation, I’m interested in anything that might speed up the development process, especially when it comes to making complex systems easier for humans to use.
One image I have of complex computer system involving the need for a user to fill out a series of forms at the start of a process is that the user interface is like the above-surface tip of an iceberg that hides a below-water massive object. As the user works his or her way through the process, the system, unlike the iceberg, is constantly changing and adapting to what the system knows (or thinks it knows) about the person, and to what the person is entering into the system.
Algorithms (or even simple if/then types of rules and table look-ups) can be programmed to constantly shift what the system (i.e., the system’s designers) think the user needs to see and do next. The data intensive nature of this process is clearly an opportunity for the use of AI in both the design and operation of the system, as long as we keep an important need in mind: the need to make sure the human user has ample opportunities to interact with and even evaluate the appropriateness and accuracy of the process.
Designing Data Warehouses, Applying Big Data, and Implementing Green Computing
From the abstract:
This portfolio project explores the integration of three pivotal areas in modern IT operations: data warehouse architecture, big data utilization, and green computing.
Also:
Lastly, the project highlights green computing strategies through case studies like Google’s implementation of AI-optimized energy systems and renewable resources.
What caught my attention to this were the terms “green computing” and “big data,” terms that have come into general use and in some quarters might even be considered old fashioned. Anyone who follows the politics surrounding the construction of energy-hungry data centers (especially someone who like me lives in Northern Virginia) will recognize the increasingly critical relationship of the two. Data centers require vast amounts of data and vast amount of energy to operate. Managing the environmental consequences of such operations provides a profound consideration about how to best employ AI based analytics to optimize operational efficiency.
It would be shortsighted to limit consideration of such issues to data centers, of course. A quick online search (using my ChatGPT Plus subscription) easily identifies several practical “facilities management” applications of AI tools:
Predictive energy optimization to reduce peak energy loads and overall consumption without sacrificing occupant comfort.
Smart building automation and controls that fine-tune operations that lead to significant reductions in energy waste and operational costs.
Preventive maintenance and asset health monitoring that extends equipment life and prevents energy inefficiencies caused by malfunctions.
Space utilization and occupancy analytics that enable consolidation of space, reduction of unnecessary heating/cooling, and more efficient facility planning.
Environmental monitoring and compliance that improves indoor air quality, reduces environmental impact, and ensures sustainable facility operations.
This all makes sense if it can be done efficiently. An important question is, though, what do we need to do, data governance wise, to make sure that data are available to support such useful applications?
Copyright © 2025 by Dennis D. McDonald
