As artificial intelligence (AI) grows more significant in society, researchers have emphasized the need for ethical boundaries when developing and implementing new AI capabilities. Although there is currently no broad-based governing organization to create and enforce these guidelines, several technological businesses have adopted their own form of AI ethics or code of conduct.

AI ethics are the moral concepts that companies employ to guide the responsible and equitable development and application of AI. In this post, we’ll look at what ethics in AI are, why they’re important, and the obstacles and benefits of creating an AI code of conduct.

What are Ethics in AI?

AI ethics is a set of moral ideas and strategies that aim to guide the development and responsible application of artificial intelligence technology. As AI has grown more integrated into products and services, corporations are developing AI codes of ethics.

An AI code of ethics, also known as an AI value platform, is a policy statement that formalizes artificial intelligence’s role in human progress and well-being. The goal of an AI code of ethics is to offer stakeholders with direction when making ethical decisions about the use of artificial intelligence.

Isaac Asimov, a science fiction writer, predicted the possible perils of autonomous AI agents long before their emergence and devised The Three Laws of Robotics to mitigate such risks. The first law of Asimov’s code of ethics prohibits robots from deliberately causing harm to humans or allowing harm to occur by refusing to intervene. The second law requires robots to obey humans unless their directives conflict with the first law. The third law requires robots to safeguard themselves in accordance with the first two principles.

The fast growth of AI over the last five to ten years has prompted organizations of specialists to propose protections against the risk of AI to humans. One such group is the nonprofit institute established by MIT cosmologist Max Tegmark, Skype co-founder Jaan Tallinn, and DeepMind research scientist Victoria Krakovna. The institute collaborated with AI researchers and developers, as well as professors from several fields, to formulate the 23 recommendations currently known as the Asilomar AI Principles.

Kelly Combs, managing director at KPMG US, stated that when designing an AI code of ethics, “it’s imperative to include clear guidelines on how the technology will be deployed and continuously monitored.” These policies should include procedures to prevent inadvertent bias in machine learning algorithms, continuously detect drift in data and algorithms, and track both data provenance and the identities of persons who train algorithms.

Examples of AI ethics

It may be simplest to demonstrate the ethics of artificial intelligence through real-world situations. In December 2022, the app Lensa AI employed artificial intelligence to create creative, cartoon-like profile photographs from people’s ordinary pics. From an ethical aspect, some users criticized the app for failing to credit or compensate artists who created the original digital art on which the AI was trained. According to The Washington Post, Lensa was being trained on billions of photos downloaded from the internet without permission.

Another example is ChatGPT, an AI model that allows people to engage with it by asking questions. ChatGPT searches the internet for information and responds with a poem, Python code, or proposal. One ethical quandary is that people are utilizing ChatGPT to win coding competitions or produce essays. It presents similar problems as Lensa, but with words instead of images.

These are only two popular instances of AI ethics. As AI has grown in recent years, touching practically every business and having a significant positive impact on industries such as health care, the issue of AI ethics has become increasingly important. How can we ensure that AI is bias-free? What can be done to reduce risk in the future? There are many potential solutions, but stakeholders must act responsibly and collaboratively in order to create positive outcomes across the globe.

An AI code of ethics can spell out the principles and provide the motivation that drives appropriate behavior. For example, Mastercard’s Jha said he is currently working with the following tenets to help develop the company’s current AI code of ethics:

• An ethical AI system must be inclusive, explainable, have a positive purpose and use data responsibly.

• An inclusive AI system is unbiased and works equally well across all spectra of society. This requires full knowledge of each data source used to train the AI models in order to ensure no inherent bias in the data set. It also requires a careful audit of the trained model to filter any problematic attributes learned in the process. And the models need to be closely monitored to ensure no corruption occurs in the future as well.
• An explainable AI system supports the governance required of companies to ensure the ethical use of AI. It is hard to be confident in the actions of a system that cannot be explained. Attaining confidence might entail a tradeoff in which a small compromise in model performance is made in order to select an algorithm that can be explained.
• An AI system endowed with a positive purpose aims to, for example, reduce fraud, eliminate waste, reward people, slow climate change, cure disease, etc. Any technology can be used for doing harm, but it is imperative that we think of ways to safeguard AI from being exploited for bad purposes. This will be a tough challenge, but given the wide scope and scale of AI, the risk of not addressing this challenge and misusing this technology is far greater than ever before.
• An AI system that uses data responsibly observes data privacy rights. Data is key to an AI system, and often more data results in better models. However, it is critical that in the race to collect more and more data, people’s right to privacy and transparency isn’t sacrificed. Responsible collection, management and use of data is essential to creating an AI system that can be trusted. In an ideal world, data should only be collected when needed, not continuously, and the granularity of data should be as narrow as possible. For example, if an application only needs zip code-level geolocation data to provide weather prediction, it shouldn’t collect the exact location of the consumer. And the system should routinely delete data that is no longer required.

Why are AI Ethics Important?

Artificial intelligence (AI) is a technology developed by humans to mimic, augment, or replace human intelligence. These tools often rely on enormous amounts of various sorts of data to generate insights. Poorly conceived projects based on incorrect, inadequate, or biased data might have unforeseen and potentially negative repercussions. Furthermore, due to the rapid growth of algorithmic systems, it is not always evident how the AI arrived at its conclusions, thus we are ultimately relying on systems we cannot explain to make judgments that may damage society.

Read Also: Virtual Reality: Beyond Gaming

An AI ethics framework is significant because it highlights the hazards and benefits of AI tools while also establishing standards for their appropriate use. To develop a set of moral tenets and methodologies for responsible AI use, the industry and interested parties must explore significant social concerns, including the question of what makes us human.

The rapid acceleration in AI adoption across businesses has coincided with — and in many cases helped fuel — two major trends: the rise of customer-centricity and the rise in social activism.

“Businesses are rewarded not only for providing personalized products and services but also for upholding customer values and doing good for the society in which they operate,” said Sudhir Jha, CEO and executive vice president, Bridgerton unit, Mastercard.

AI plays a huge role in how consumers interact with and perceive a brand. Responsible use is necessary to ensure a positive impact. In addition to consumers, employees want to feel good about the businesses they work for. “Responsible AI can go a long way in retaining talent and ensuring smooth execution of a company’s operations,” Jha said.

Ethical Challenges of AI

Enterprises face several ethical challenges in their use of AI technologies.

• Explainability. When AI systems go awry, teams need to be able to trace through a complex chain of algorithmic systems and data processes to find out why. Organizations using AI should be able to explain the source data, resulting data, what their algorithms do and why they are doing that. “AI needs to have a strong degree of traceability to ensure that if harms arise, they can be traced back to the cause,” said Adam Wisniewski, CTO and co-founder of AI Clearing.
• Responsibility. Society is still sorting out responsibility when decisions made by AI systems have catastrophic consequences, including loss of capital, health or life. The process of addressing accountability for the consequences of AI-based decisions should involve a range of stakeholders, including lawyers, regulators, AI developers, ethics bodies and citizens. One challenge is finding the appropriate balance in cases where an AI system may be safer than the human activity it is duplicating but still causes problems, such as weighing the merits of autonomous driving systems that cause fatalities but far fewer than people do.
• Fairness. In data sets involving personally identifiable information, it is extremely important to ensure that there are no biases in terms of race, gender or ethnicity.
• Misuse. AI algorithms may be used for purposes other than those for which they were created. Wisniewski said these scenarios should be analyzed at the design stage to minimize the risks and introduce safety measures to reduce the adverse effects in such cases.

The public release and rapid adoption of generative AI applications, such as ChatGPT and Dall-E, which are trained on existing content to generate new content, exacerbate the ethical issues surrounding AI, introducing risks such as misinformation, plagiarism, copyright infringement, and harmful content.

The Future of Ethical AI

Some suggest that an AI code of ethics can quickly become out of date, necessitating a more proactive strategy to adapt to a constantly changing profession. Arijit Sengupta, founder and CEO of Aible, an AI development platform, stated, “The basic problem with an AI code of ethics is that it is reactive rather than proactive. We have a tendency to characterize things like prejudice and then go seeking for it and attempting to eliminate it, as if it is achievable.”

A reactive strategy may have difficulty coping with bias embedded in data. For example, if women have historically not received loans at the proper rates, this will be reflected in the data in a variety of ways. “If you remove variables related to gender, AI will just pick up other variables that serve as a proxy for gender,” according to Sengupta.

He believes that the future of ethical AI must address issues such as justice and societal norms. For example, at a lending bank, management and AI teams must decide whether to aim for equal consideration (e.g., loans processed at an equal rate for all races), proportional results (a relatively equal success rate for each race), or equal impact. Sengupta stated that the focus should be on a guiding concept rather than something to avoid.

Most people would agree that it is easier and more effective to teach youngsters what their guiding principles should be rather than listing every possible decision they may face and telling them what to do and not do. “That’s the approach we’re taking with AI ethics,” Sengupta said. “We are telling a child everything it can and cannot do instead of providing guiding principles and then allowing them to figure it out for themselves.”

For the time being, we must rely on humans to design policies and technologies that support responsible AI. Shephard stated that this includes developing products and services that preserve human values and are not discriminatory against specific groups, such as minorities, those with special needs, or the impoverished. The latter is particularly alarming because AI has the potential to spark widespread social and economic conflict, widening the gap between those who can afford technology (including human enhancement) and those who cannot.

Society must also quickly plan for evil actors’ immoral exploitation of artificial intelligence. Today’s AI systems include fancy rule engines, machine learning models that automate simple tasks, and generative AI systems that mimic human intelligence. “It may be decades before more sentient AIs begin to emerge that can automate their own unethical behavior at a scale that humans wouldn’t be able to keep up with,” Shephard pointed out. However, considering the rapid progress of AI, now is the time to implement safeguards to avoid this catastrophe.