What is AI washing?

This deceptive marketing process has come to be known as “AI washing.” Ultimately, companies who participate in this practice are incredibly unethical, taking advantage of consumers and investors eager to reap the advantages of a powerful technology without offering them said benefits.

The most common form of AI washing is using misleading product descriptions. For example, some businesses have attempted to label traditional algorithms as AI, yet since many people in the general public don’t understand the subtle differences between these two technologies, it can be easy to pass this switch off on unsuspecting consumers. However, this is deceptive marketing through and through.

Other examples of AI washing take the deceit even further. Some companies have begun to exaggerate the scale of their AI capabilities or even claim to utilize AI without any substantial implementation. Since artificial intelligence is such a new technology, many businesses only use it in ways that are incidental to the business’s function or in the early stages of development. As such, there’s no way to tell what a claim of “AI-powered” actually means.

How does AI washing hurt everyone?

The most obvious consequences of AI washing are for consumers who spend their hard-earned money on falsely represented AI products, as their purchasing decisions could be falsely swayed by this misrepresentation. As for the business, there could be legal consequences, including fines and sanctions, especially as regulatory bodies become more vigilant.

The consequences of AI washing also extend beyond the parties directly involved because AI washing and deceptive marketing have a detrimental impact on the market as a whole. Any money invested in or spent on a company engaging in AI washing could be going to companies that are genuinely engaging with the technology and participating in innovation. 

Even more severe is that AI washing can potentially erode the public’s trust in the technology overall. It is important to remember that artificial intelligence is still a very new technology, relatively speaking, and it takes time for the mass public to build trust for any innovation of this caliber. 

AI washing sets up a situation where the public has a perception of AI that overpromises and underdelivers. Because of this, they’ll be much less likely to trust future products or services claiming AI use.

How do we fight back against AI washing?

To combat these adverse effects of AI washing, we must push for increased transparency and honesty in the artificial intelligence industry — particularly when marketing AI products and services. Unfortunately, many companies still use confusing or cryptic language to describe their product or service’s use of artificial intelligence, partly to protect their intellectual property and partly because the general public doesn’t entirely understand this technology in its infancy. To fix this, companies should be expected to clearly and understandably define what constitutes AI in their products and provide evidence of AI implementation when possible.

One of the first steps to encourage transparency and honesty in the artificial intelligence industry is the establishment of industry-wide standards. For example, innovators in the AI space can self-regulate by establishing a certification body and universal benchmark for evaluating AI claims and products’ AI capabilities. This will both ensure that consumers and stakeholders have a trusted mark of authenticity and foster more international collaboration.

While establishing industry standards around the use of artificial intelligence will go a long way in creating an ecosystem of more responsible AI use, some have argued that more formal regulatory oversight will be necessary to mitigate the consequences of AI washing. By enforcing more stringent verification processes and penalties (such as fines or sanctions) against those who make false claims, regulatory bodies can effectively discourage the practice of AI washing.

Nevertheless, education is the most powerful tool we have to stop the consequences of AI watching. When people are informed of the benefits, features, and limitations of legitimate artificial intelligence products, they are less likely to fall victim to the deceit of AI washers, as they will know the difference between “real” and “fake” claims. Thus, by increasing the discourse around artificial intelligence, we have the opportunity to ensure that people are reaping the benefits of legitimate AI innovation rather than falling victim to those who hope to exploit its popularity falsely.

Although artificial intelligence can do a lot of good for the world, businesses that participate in AI washing threaten to undermine the technology’s positive impacts. Consumers who are misled by businesses conducting AI washing will have expectations of artificial intelligence technology that these deceptively marketed products and services will not meet. 

Stopping the practice of AI washing is an integral step in restoring the public’s trust in the technology and creating an ecosystem where AI can be used responsibly.

The post AI Washing: How To You Know If You’re Being Taken to the Cleaners appeared first on SiteProNews.

The biggest revolution we are currently seeing in research is artificial intelligence. Researchers must understand the ethical implications of this technology to use it responsibly.

Use cases of AI in research

Artificial intelligence is a tool that leaders across industries have applied to help streamline processes and make workflows more efficient. In the world of research, some of the most common use cases for AI include:

• Data analysis: One of the most common uses of AI technology in research is data analysis, as artificial intelligence models have much faster, more efficient data processing capabilities than humans. A well-trained AI model can scan a data set and perform calculations near-instantaneously, allowing researchers to analyze the results and draw conclusions more effectively.

• Literature review: Some researchers have also used AI to conduct literature reviews. An integral stage of the research process is putting findings in the context of existing knowledge, and an AI model can help researchers quickly search databases for similar research that may be relevant to the study at hand. 

• Academic writing: After the research has been conducted, researchers typically publish their findings in academic or professional journals. Researchers can use large language models to help translate their findings into prose, reducing the time spent on writing and allowing more time to be spent on actual discovery.

• Grant writing: Researchers have also seen the potential for AI to be used for administrative tasks, such as grant writing. While these processes can be incredibly time-consuming, they are a critical component of funding research. AI can be used as a tool to help maximize efficiency.

Ethical considerations for AI in research

As powerful of a tool as artificial intelligence is, researchers must use it responsibly or face serious consequences. While there has been much talk about the need for “ethical AI,” this does not refer to a specific type of AI model designed to be ethical, but rather a set of use cases and guidelines to ensure that any negative impacts of AI use are minimized or eliminated.

Researchers and the platforms they use must be transparent about how they use and handle data. The responsibility primarily falls on users to ensure privacy, consent, and understanding of the platform’s data collection, use, and storage policies. This is particularly important for researchers working in fields where they handle sensitive data, such as the medical field.

Another key factor AI users must consider regarding ethics is the possibility of unintentional copyright infringement. It is essential to realize that many AI models, particularly large language models, train themselves based on the input they receive from users. In fields like research, where originality is paramount, the potential for accidental copyright infringement threatens to undermine any sort of discovery that might have been made.

Finally, researchers who choose to employ AI in their research must ensure they follow fair experimentation practices. Practices like avoiding biases and ensuring diverse representations in datasets are necessary to ensure that results are valid. Relying too heavily on a single AI tool can easily cause results to become skewed.

At the same time, researchers must understand that artificial intelligence suffers from inherent biases. Because modern AI is completely dependent on pre-existing data, any prejudices in that data will be perpetuated by the model, which can be particularly worrisome in research as research is supposed to represent a diverse sample accurately. As such, the data used to train AI models must originate from a diverse set of sources, and — ideally — the developers should represent a diverse background.

After the research is conducted, researchers should be held to high standards when publishing their research. All work should be accurately represented, with potential biases or limitations disclosed, and the role of AI in their research explained transparently while avoiding sensationalism or overstating the results.

The evolving nature of artificial intelligence

The dynamic nature of research and the constantly evolving status of AI demand continuous ethical review. To maintain an ethical landscape for AI use, researchers must stay informed about ethical concerns and adapt their practices to align with fluid standards. With a technology as innovative as AI, there are still things to be figured out. Researchers must be prepared to handle challenges when they emerge.

Many in the AI community have forgotten the most valuable tool for evaluating the efficacy of their platforms: their user base. As those who get the most hands-on experience with the technology, users get a firsthand look at what may need improvement about a platform’s features. By engaging in an open dialogue with their peers and the creators of the AI platforms they use, researchers can collaborate to foster an ecosystem that is more conducive for all.

Artificial intelligence has the power to become an essential tool in any researcher’s arsenal. From data analysis to literature review and streamlining academic writing, researchers have at their disposal a flexible innovation that can be adjusted to any number of robust use cases. However, maintaining ethical use is integral for the continued development of this technology and its applications.

The post Ethics in AI Research: Discussing Responsible Conduct in AI Research, Including Data Usage, Experimentation, and Publication Practices appeared first on SiteProNews.

Artificial intelligence in the financial services industry

For financial advisors, the use cases for artificial intelligence are generally related to the technology’s advanced data processing capabilities. Advisors can run a data set through an AI model, which can research and analyze said data far more efficiently than a person could manually. The advisor can then use these analytics to provide customized, data-driven recommendations to their clients.

Critics of artificial intelligence have pointed out several ethical concerns that the technology creates, but the stakes are incredibly high in the financial services sector. If one of these ethical concerns begins to impact a financial advisor’s performance, money is lost. Because of this, it is crucial that financial advisors implement a clear set of guidelines for the ethical and responsible use of AI, extending even beyond compliance with regulations.

In the financial services industry, the pivotal factor for success is trust. After all, clients entrust their hard-earned money to their financial advisors, so an advisor must earn their client’s trust. Advisors must carefully consider not only what tools they use but also how they use them, ensuring that every use case is in the best fiduciary interest of the client.

Concerns about AI for financial advisors

Financial advisors have to be particularly careful with the data they allow AI models to access, as much of their data is privileged or confidential, and many artificial intelligence programs use the information users feed them to train. When financial advisors work with their clients’ personally identifiable information or financial data, security is paramount to ensure this information does not end up in the wrong hands. Thus, it is essential to read all terms of use and privacy policies carefully to understand how these platforms use and store data.

Financial advisors hoping to use artificial intelligence models for research and analysis must also realize that these tools have inherent biases. Because AI is still entirely dependent on pre-existing data, any biases found within that data will be reflected in a model’s output. This is why any data set a financial analyst uses must represent the fundamental research guideline of accurate representation. If a particular set of data is skewed — be it for or against particular companies, sectors, or trends — the model’s output cannot be trusted.

For instance, if an advisor is using an AI model to analyze historical trends and provide recommendations to their client, but the data being used to illustrate those trends is biased, the recommendations will not accurately reflect reality. The responsible use of artificial intelligence in the financial services sector demands that advisors actively identify and mitigate any biases and limitations the model may suffer.

Another consideration financial advisors must consider when using AI tools for research and analysis is the transparency of their use. In most cases, it would be necessary to disclose the use of any artificial intelligence platforms to clients, and SEC rules prohibit financial advisors from making false statements or omissions about their advisory business. As such, financial advisors must understand how to accurately convey their use of AI technology to their clients to reassure them that they are acting in their best interests.

Like any other technology they may use, financial advisors should take specific security measures around the use of artificial intelligence. For example, advisors should practice access control procedures to ensure that only authorized users can access AI programs that may store sensitive client information. These platforms should also only be used from secure networks to prevent unwanted access.

The evolution of AI in finance

Finally, it is essential for those using artificial intelligence to remain flexible with their use and standards. Remember, AI is still in its infancy, and as people continue to adopt the technology and discover new use cases, our understanding of its ethical and responsible use will evolve. 

It is the responsibility of users to stay abreast of emerging ethical concerns and adapt practices. For financial advisors, this adaptability is vital, as clients will expect advisors to uphold the highest and most recent standards.

Artificial intelligence is a powerful tool, and its advanced data analytics capabilities show enormous potential to help make the jobs of financial advisors easier. However, more so than those in other industries, financial advisors must ensure that their clients’ data and information are handled properly. 

Financial Advisory and Services firms may have a much better chance earning the trust of investors by ensuring that the AI tools they use to build their solutions are aligned with the core principles of initiatives like the World Digital Governance that promote User Privacy and Ethical AI along with Transparency and Explainable AI. 

The future of AI in the financial sector is on the backs of those who use the technology. If a robust set of guidelines can be established to ensure its fair and responsible use, financial advisors can maximize this tool’s potential to maximize their clients’ funds.

The post Ethics in AI Research: Discussing Responsible Conduct in AI Research in Financial Services appeared first on SiteProNews.