In this article we talk for AI Ethics
Artificial Intelligence (AI) has transformed the way we live and work, revolutionizing industries and enhancing efficiency. However, as AI becomes increasingly integrated into our lives, ethical concerns have arisen regarding its impact on society, privacy, and human rights. In this comprehensive article, we’ll explore the multifaceted landscape of AI ethics, highlighting its importance, key considerations, and potential solutions.
Table of Contents
What are ethics in AI?
AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technology. As AI has become integral to products and services, organizations are starting to develop AI codes of ethics.
An AI code of ethics, also called an AI value platform, is a policy statement that formally defines the role of artificial intelligence as it applies to the continued development of the human race.
The purpose of an AI code of ethics is to provide stakeholders with guidance when faced with an ethical decision regarding the use of artificial intelligence.
Science fiction writer Isaac Asimov foresaw the potential dangers of autonomous AI agents long before they were developed and created the Three Laws of Robotics to limit those risks.
In Asimov’s Code of Ethics, the first law prohibits robots from actively harming humans or allowing humans to be harmed if they refuse to act. The second law tells robots to obey humans unless the commands are consistent with the first law. The third law tells robots to defend themselves if it matches the first two laws.
The rapid development of artificial intelligence over the past 5–10 years has prompted expert groups to develop safeguards against the risks posed by artificial intelligence to humans.
One such group is a non-profit institute founded by MIT cosmologist Max Tegmark, Skype founder Jaan Tallinn and DeepMind researcher Victoria Krakovna.
The institute worked with AI researchers and developers, as well as researchers from many fields, to create 23 guidelines, now called the Asilomar AI Principles.
Kelly Combs, director of KPMG’s Digital Lighthouse, said that when developing an AI code of conduct, it is “absolutely important to include clear guidelines for the adoption and ongoing monitoring of the technology.”
These policies should require measures that protect against unintentional bias in machine learning algorithms, continuously identify data and algorithm, and track both the origin of the data and the identity of those training the algorithms.
Why are AI ethics important?
Artificial intelligence is technology developed by humans to duplicate, supplement or replace human intelligence. These tools typically rely on large amounts of different types of data to provide insights.
Poorly designed projects based on faulty, insufficient or biased data can have unintended, potentially harmful consequences. In addition, the rapid development of algorithmic systems means that in some cases it is not clear to us how the AI reached its conclusions, so we rely mainly on systems that we cannot explain to make decisions that affect society.
An ethical framework for AI is important because it sheds light on the risks and benefits of AI tools and provides guidance for its responsible use. Developing a system of moral principles and techniques for the responsible use of AI requires industry and stakeholders to look at the big social issues and ultimately the question of what makes us human.
What are the ethical challenges of artificial intelligence?
Businesses face a number of ethical challenges using artificial intelligence technology.
- 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. Responsibility for the consequences of AI-based decisions needs to be sorted out in a process that includes lawyers, regulators 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.
What are the benefits of ethical AI?
The rapid acceleration of AI adoption in businesses has coincided with, and in many cases contributed to, two main trends: the rise of customer centricity and social activism.
“Companies are rewarded not only for offering personal products and services, but also for increasing customer value and doing good for the society in which they operate,” says Sudhir Jha, head of Brighterion at Mastercard.
AI plays a huge role in how consumers interact with and perceive a brand. Responsible use is necessary to ensure a positive effect. In addition to consumers, employees want to feel good about the companies they work for. “Responsible AI can go a long way in retaining talent and ensuring the smooth running of business operations,” Jha said.
What is an AI code of ethics?
A proactive approach to ensuring ethical AI requires addressing three key areas, according to Jason Shepherd, vice president of ecosystem at Zededa, an edge AI tools provider.
- Policy. This includes developing the appropriate framework for driving standardization and establishing regulations. Efforts like the Asilomar AI Principles are essential to start the conversation, and there are several efforts spinning up around policy in Europe, the U.S. and elsewhere. Ethical AI policies also need to address how to deal with legal issues when something goes wrong. Companies may incorporate AI policies into their own code of conduct. But effectiveness will depend on employees following the rules, which may not always be realistic when money or prestige are on the line.
- Education. Executives, data scientists, front-line employees and consumers all need to understand policies, key considerations and potential negative impacts of unethical AI and fake data. One big concern is the tradeoff between ease of use around data sharing and AI automation and the potential negative repercussions of oversharing or adverse automations. “Ultimately, consumers’ willingness to proactively take control of their data and pay attention to potential threats enabled by AI is a complex equation based on a combination of instant gratification, value, perception and risk,” Shepherd said.
- Technology. Executives also need to architect AI systems to automatically detect fake data and unethical behavior. This requires not just looking at a company’s own AI but vetting suppliers and partners for the malicious use of AI. Examples include the deployment of deep fake videos and text to undermine a competitor, or the use of AI to launch sophisticated cyberattacks. This will become more of an issue as AI tools become commoditized. To combat this potential snowball effect, organizations need to invest in defensive measures rooted in open, transparent and trusted AI infrastructure. Shepherd believes this will give rise to the adoption of trust fabrics that provide a system-level approach to automating privacy assurance, ensuring data confidence and detecting unethical use of AI.
Examples of AI codes of ethics
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.
Future of ethical AI
Some argue that the ethics of AI can quickly become outdated and that a more proactive approach is needed to adapt to the rapidly evolving field.
Arijit Sengupta, founder and CEO of Aible, an AI development platform, said: “The main problem with AI code of ethics is that it is reactive, not proactive. We tend to define things like bias, look for bias and try to eliminate it. . It — — like that’s possible.”
A reactive approach may have difficulty dealing with bias in the data. For example, the fact that women have historically been unable to access loans at appropriate interest rates is intertwined with the data in several ways.
“If you remove the variables associated with gender, the AI simply picks up other variables that act as proxies for gender,” Sengupta said.
He believes that the future of ethical AI must be about defining justice and social norms. For example, in a lending bank, management and AI teams should decide if they want to ask for equal weighting (eg loans processed at the same speed in all competitions), relative results (the success of each competition is relative). ). equal) or equal impact (ensuring that a proportional number of loans go to each competition).
The focus should be on the guiding principle rather than the avoidable principle, argued Sengupta.
Most people would agree that it is easier and more effective to teach children what their guiding principles should be than to list all the possible decisions they face and tell them what to do and what not to do. “This is our approach to AI ethics,” Sengupta said.
“We tell the child everything they can and can’t do, instead of providing guidelines and letting them figure it out for themselves.”
Right now, we need to turn to people to develop the policies and technologies that promote responsible AI. That includes software products and offerings that protect people’s interests and are not biased toward certain groups, such as minorities, people with special needs and the poor, Shepherd said.
The latter is particularly worrisome, as AI could fuel massive social and economic warfare by widening the gap between those who can afford technology (including human augmentation) and those who cannot.
At the same time, we must also plan for the unethical use of AI by bad actors. Today’s AI systems range from fancy rule engines to machine learning models that automate simple tasks.
“It could be decades before more sentient AIs emerge that can automate their unethical behavior to a degree that humans cannot continue,” Shepherd said.
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