At the same time, the value we attach to human life is decreasing, as evidenced by a diminishing life expectancy. There is an urgent need to rein in costs and still enhance the quality of life. Life should be valued high, while living it should not be expensive. Investing in Artificial Intelligence can help facilitate these.

When I was debating whether to relocate to the United States more than two decades ago, my friends told me that the value the country attaches to life is far greater compared to any other country and essential commodities are surprisingly affordable.

Unfortunately, that seems to be changing for various reasons – a corrupted value system, mental health issues, diseases, drug overdoses, the resulting strain on the healthcare system, and many more. No wonder, the American quality of life has been declining.

Artificial Intelligence (AI) can’t fix everything, of course, but it can help alleviate financial pressures on individuals and communities. And that will help improve the quality of life.

AI can help lower costs and, owing to productivity gains, give time back to people to focus more on the quality of their and others’ lives. By automating repetitive, mundane tasks and making the work more interesting, jobs can become more satisfying, reducing anxieties and stress levels, and boosting mental health.

AI can help identify opportunities for improved efficiency and democratize access to services including healthcare and education. Enhancing access reduces costs significantly. The World Economic Forum Annual Meeting in May 2022 reported that the proportion of the US hospitals using AI has increased thrice since 2020.

Significant productivity improvements can be achieved in any vertical that AI is applied to. Increased productivity translates to reduced costs for the manufacturers and lowered prices of consumer goods to the customers. In transportation, AI can help reduce congestion and optimize routes, thereby saving fuel.

Automating tasks in financial management using AI goes way beyond algorithmic trading by executing trades based on analytics generated from the market and opportunities identified. In addition to keeping the trading costs low, this also removes human error due to emotions, biases, and unnecessary apprehensions that are often a cause for poor performance.

AI has unleashed major breakthroughs in preventive care and personalized medicine lowering the risk for expensive treatments and hospitalization. Public safety can be enhanced by AI enabled predictive policing, efficient emergency response, and improved disaster management.

In my machine learning classes, sustainability topics play a big role. Students are incentivized to work on a semester-long project applying machine learning for sustainable outcomes. Sustainable practices lead to a better environment, help avoid calamities, and therefore lower costs. Using AI for precision farming, optimizing agricultural practices, and making them sustainable can lower food costs. All these and more lead to lowering inflation.

Of course, governance and social factors always trump technology. If the government decides to increase its spending like in the case of the American Rescue Plan and CARES Act, or if the national debt ceiling continues to be raised, or if the Federal Reserve increases the money supply, AI may not be able to help much.

Corporate greed, excessive price increases, opportunistic labor practices, and other such vile circumstances can also create inequities and inflation that AI may not be able to help with. Also, AI is not entirely free of flaws. AI deployments are often impacted by privacy, fairness, and job displacement concerns, to name a few. In my talk to the University of Bolton audiences,  I explained the social sustainability impacts of AI.

It is therefore imperative that AI be used responsibly. AI is certainly no panacea but will go a long way in ensuring an affordable quality of life while lowering costs and keeping inflation in check if used ethically, responsibly, and in a manner fair and beneficial to society. For all these reasons and more, investing in AI can help in an affordable quality of life and retirement in the long run.

A major revamp is needed to address the issues that are plaguing the dissemination of research, which is the very foundation of the advancement of civilization. The formal publication process has become a disservice to the research community.

While the peer review process is, in theory, essential for maintaining quality standards,  in practice it is often marred by biases, inconsistencies, sloppiness, and more importantly, unreasonable delays that lead to unreliable and sometimes outdated outcomes that hinder scientific progress. When rapid developments in some areas like generative AI are being tracked in days and hours, the review process of articles even in the same or similar areas of study takes several months. Uploading preprints is a weak workaround because preprints do not come with the same quality guarantees as the published articles.

A primary reason is the voluntary nature of reviews. Some journals expect reviewers to almost work with the authors, revision after revision to spruce up the manuscripts. Some journals make them accountable by listing the reviewers’ names on the publication but do not pay the reviewers for their extraordinary service. Recently, an article that I rejected twice due to the poor quality of the manuscript was sent for my review a third time because one or more other reviewers suggested revisions instead of rejecting it.

The current scientific publishing model also lacks accessibility and transparency. Scientific literature is often locked behind paywalls, making it inaccessible to the general public and even to researchers from institutions without subscription access. This restricts the dissemination of knowledge and impedes the progress of science, as researchers are unable to build upon existing findings without access to the full breadth of literature in their field even in this fast-paced day of the Internet and AI.

The current open access model where the author pays for publication is no panacea either because it restricts the publication channels for authors who have budget constraints. Ironically, authors need to pay instead of getting remunerated for contributing their intellectual property.

A better business model may be for the publishers to monetize the intellectual property rights of the authors, in addition to the subscription model, and use portions of the revenue to remunerate reviewers for sure and possibly authors as well.

The “publish or perish” paradigm in academia complicates these issues further. Scholars are under constant pressure to publish frequently in high-impact journals to secure funding, tenure, and career advancement.

This promulgates a culture of hyper-competition and possible academic misconduct if researchers decide to prioritize publication metrics over the rigor and integrity of their work. For instance, in arecently published book chapter, I talked about how some highly cited research papers claimed up to 100% accuracy at detecting misinformation – but we all know the problem of misinformation is nowhere near being solved.

The business model of journal publishing needs to evolve to support open-access initiatives. The peer review process must be reformed to reward the reviewers and enhance speed, transparency, accountability, and fairness. Open peer reviews, where the review process and reviewers’ identities are disclosed, can certainly help mitigate biases and improve the quality of evaluations, but the increased responsibility on the reviewers must be suitably compensated.

I had to undergo prior training to be a high school science fair judge. Unfortunately, reviewers of highly impactful research do not have to go through any priming. Establishing clear guidelines and standards for peer review, along with providing training and support for reviewers, can go a long way in ensuring consistency and integrity in the process.

Along with my students, I recently submitted a research paper on how language models, particularly those used for Generative AI, can be used for peer reviews of research papers. The paper has been under review, since July 2023. To reduce the turnaround time, technology must be used to support the review process, particularly in this day of generative AI, as we discussed in a publisher-sponsored webinar. There are ways to address or work around the biases and other issues that the AI models come with.

The academic evaluation system must move away from overreliance on publication metrics and embrace a more utilitarian approach to assessing research impact and quality. A holistic approach could involve incorporating diverse metrics such as reproducibility, data sharing, outreach, and societal impact into academic evaluations, thereby incentivizing responsible and impactful research practices.

The current scientific publishing model and the “publish or perish” paradigm are overdue for a major overhaul to address their inherent flaws and promote a more robust, transparent, and equitable research ecosystem.

By evolving the business model for open-access publishing, reforming peer review practices, and adopting holistic evaluation metrics, we can foster a culture of scientific integrity, professionalism, and innovation that benefits researchers, institutions, and society as a whole. It’s time to reimagine the way we conduct, evaluate, and disseminate scientific research for the betterment of science and humanity, particularly in the age of Generative AI, which is expanding the technological horizons by the day.